• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Advertising
 About the book and authors
 Title Page
 Table of Contents
 Preface
 Acknowledgement
 Introduction
 Part I: Planning applied resea...
 Part II: Conducting applied...
 Reference
 An applied research project...
 Index
 Back Cover






Title: Planning and conducting applied agricultural research
CITATION PAGE IMAGE ZOOMABLE PAGE TEXT
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00055232/00001
 Material Information
Title: Planning and conducting applied agricultural research
Series Title: A Westview special study
Alternate Title: Applied agricultural research
Physical Description: xii, 94 p. : ill. ; 23 cm.
Language: English
Creator: Andrew, Chris O
Hildebrand, Peter E
Publisher: Westview Press
Place of Publication: Boulder Colo
Publication Date: 1982
 Subjects
Subject: Agriculture -- Research -- Planning   ( lcsh )
Agriculture -- Research -- Methodology   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references and index.
Statement of Responsibility: Chris O. Andrew and Peter E. Hildebrand.
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
 Record Information
Bibliographic ID: UF00055232
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 000316613
oclc - 08666787
notis - ABU3417
lccn - 82013503
isbn - 0865314616 :

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
    Advertising
        Page ii
    About the book and authors
        Page iii
        Page iv
    Title Page
        Page v
        Page vi
    Table of Contents
        Page vii
        Page viii
    Preface
        Page ix
        Page x
    Acknowledgement
        Page xi
        Page xii
        Page xiii
    Introduction
        Page 1
        Page 2
        Applied research
            Page 3
        The book
            Page 4
            Page 5
    Part I: Planning applied research
        Page 5a
        Effects of resource availability on applied research
            Page 6
            Information resources
                Page 7
                Secondary and primary information
                    Page 8
                Time series and cross section data
                    Page 9
                Experimental and non-experimental data
                    Page 9
            Human resources
                Page 10
            Physical resources
                Page 11
            Financial resources
                Page 12
            Time constraints
                Page 12
            Summary
                Page 13
        Orientation and focus of projects: researchable problems, hypotheses, and objectives
            Page 14
            A conceptual model
                Page 14
                Page 15
            Specification of researchable problem
                Page 16
                Problems reflect felt needs
                    Page 17
                Problems are non-hypothetical
                    Page 17
                Problems suggest meaningful, testable hypotheses
                    Page 18
                Problems are relevant and manageable
                    Page 19
                Researchable problems vs. problematic situations
                    Page 19
                Examples of problems statements
                    Page 20
                    Page 21
                    Page 22
            Formulation of the hypotheses
                Page 23
                Characteristics of hypotheses
                    Page 24
                Some examples of hypotheses
                    Page 24
                    Page 25
                    Page 26
                    Page 27
            Delineation of the objectives
                Page 28
                Page 29
            Summary
                Page 30
                Page 31
                Page 32
                Page 33
    Part II: Conducting applied research
        33a
        Experimental data collection
            Page 34
            Experimental design
                Page 35
                Relationship to the problem
                    Page 35
                    Page 36
                    Page 37
                Relationship to resources
                    Page 38
                    Page 39
                    Page 40
            Secondary experimental data
                Page 41
            Multi-purpose experimentation
                Page 42
                Page 43
                Page 44
            Multi-disciplinary experimentation
                Page 45
            Summary
                Page 46
        Non-experimental data collection
            Page 47
            Selecting respondents
                Page 47
                Page 48
            Designing the questionnaire
                Page 49
                Difficulties in interpretation and communication
                    Page 50
                Designing for data retrieval
                    Page 51
            Pretesting the questionnaire
                Page 52
                Size of pretest
                    Page 53
                Information checking
                    Page 54
                Time difficulties
                    Page 55
            Selecting and training interviewers
                Page 56
            Verifying primary data
                Page 57
            Verifying and using secondary data
                Page 58
                Page 59
            Summary
                Page 60
        Data utilization - what does it all mean?
            Page 61
            Page 62
            Flexibility of interpretation
                Page 63
                Meaning of the results
                    Page 63
                    Page 64
                    Page 65
                Reliability of results
                    Page 66
                    Page 67
                    Page 68
            Presentation of the results
                Page 69
                Page 70
            Summary
                Page 71
                Page 72
    Reference
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
    An applied research project proposal
        Page 80
        Page 81
        Page 82
        Page 83
        Page 84
        Page 85
        Page 86
    Index
        Page 87
        Page 88
        Page 89
        Page 90
        Page 91
        Page 92
        Page 93
        Page 94
    Back Cover
        Back Cover
Full Text
i A Westview Special Study / Y /I
Planning and
.Conducting
_lied. Agr-cu
I I. -
,- U.









Planning and Conducting
Applied Agricultural Research













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About the Book and Authors


Planning and Conducting Applied Agricultural Research
Chris O. Andrew and Peter E. Hildebrand

This study focuses on applied research as a service to a client
with a problem that research can help solve. Because applied research
has a definite purpose, there is usually a time constraint, a deadline
by which the work must be completed, as well as a limit on the
resources the client has available or is willing to use. Consequently,
the researcher must concentrate on the efficient use of the research
resources while trying to maximize the likelihood of providing a
useful product. Professors Andrew and Hildebrand offer an approach
to identifying researchable problems and proceeding efficiently to
their resolution. Their material can be used effectively both in teaching
and by individuals working in the field.

Dr. Chris O. Andrew is professor in the Food and Resource
Economics Department and associate director of the Center for
Tropical Agriculture and International Programs at the Institute of
Food and Agricultural Sciences (IFAS), University of Florida. Dr.
Peter E. Hildebrand is professor in the Food and Resource Economics
Department and coordinator of the Farming Systems Research and
Extension Program at IFAS.






Planning and Conducting
Applied Agricultural Research
Chris O. Andrew
and Peter E. Hildebrand


Westview Press / Boulder, Colorado































A Westview Special Study


All rights reserved. No part of this publication may be reproduced or transmitted in
any form or by any means, electronic or mechanical, including photocopy, recording,
or any information storage and retrieval system, without permission in writing from
the publisher.

Copyright 1982 by Westview Press, Inc.

Published in 1982 in the United States of America by
Westview Press, Inc.
5500 Central Avenue
Boulder, Colorado 80301
Frederick A. Praeger, President and Publisher


Library of Congress Cataloging in Publication Data
Andrew, Chris O.
Planning and conducting applied agricultural research.
(A Westview special study)
Includes bibliographical references.
1. Agricultural research-Planning. 2. Agricultural research-Methodology.
I. Hildebrand, Peter E. II. Title. III. Title: Applied agricultural research.
S540.A2A53 1982 630'.72 82-13503
ISBN 0-86531-461-6
ISBN 0-86531-460-8 (pbk.)


Printed and bound in the United States of America








TABLE OF CONTENTS


PREFACE ............................................... .ix
ACKNOWLEDGMENTS ...................................xi

CHAPTER I:
INTRODUCTION .................................... 1
Applied Research ................................ 3
The Book ........................................ 4

PART ONE: PLANNING APPLIED RESEARCH

CHAPTER II:
EFFECTS OF RESOURCE AVAILABILITY ON APPLIED
RESEARCH ....................................... 6
Information Resources .......................... 7
Secondary and Primary Information ............... 8
Time Series and Cross-Section Data ................ 9
Experimental and Non-Experimental Data .......... 9
Human Resources ............................. 10
Physical Resources .............................. 11
Financial Resources ............................ 12
Time Constraints ................... .......... 12
Summary ..................................... 13

CHAPTER III:
ORIENTATION AND FOCUS OF PROJECTS: RESEARCH-
ABLE PROBLEMS, HYPOTHESES,
AND OBJECTIVES ............................ 14
A Conceptual Model ........................... 14
Specification of a Researchable Problem .............. 16
Problems Reflect Felt Needs. ................... .. 17
Problems are Non-hypothetical ................... 17
Problems Suggest Meaningful, Testable
Hypotheses .................... ........... 18
Problems are Relevant and Manageable ............. 19
Researchable Problems vs. Problematic
Situations ................... ............. 19
Examples of Problem Statements ................. 20
Formulation of the Hypotheses ..................... 23
Characteristics of Hypotheses .................... 24
Some Examples of Hypotheses ................... 24
Delineation of the Objectives ....................... 28
Summary ..................................... 30








PART TWO: CONDUCTING APPLIED RESEARCH
CHAPTER IV:
EXPERIMENTAL DATA COLLECTION ............... 34
Experimental Design ............................ 35
Relationship to the Problem ..................... 35
Relationship to Resources ....................... 38
Secondary Experimental Data .................. .. 41
Multi-purpose Experimentation ................... 42
Multi-disciplinary Experimentation ................ 45
Summary ................................... 46

CHAPTER V:
NON-EXPERIMENTAL DATA COLLECTION .......... 47
Selecting Respondents ........................... 47
Designing the Questionnaire ................... .. 49
Difficulties in Interpretation and
Communication ............................. 50
Designing for Data Retrieval .................... 51
Pretesting the Questionnaire ................... .. 52
Size of Pretest ............................... 53
Information Checking .......................... 54
Time Difficulties .............................. 55
Selecting and Training Interviewers ................ 56
Verifying Primary Data ........................ .. 57
Verifying and Using Secondary Data ............... 58
Summary ..................................... 60


CHAPTER VI:
DATA UTILIZATION WHAT DOES IT ALL MEAN? 61
Flexibility of Interpretation ....................... 63
Meaning of the Results ......................... 63
Reliability of Results ........................... 66
Presentation of the Results ....................... 69
Summary ................................... 71
REFERENCES ..................................... 73
APPENDIX .............................................. 79
INDEX .................................................. 87








PREFACE


This book is the culmination of a group effort to eliminate a defi-
ciency made evident during the organization of a graduate course in
research methodology at the UN-ICA Graduate School in
Agricultural Sciences in Bogota, Colombia.' The deficiency resulted
from the difficulties associated with organizing research projects
oriented toward real world problems and formulated so that 1) the
research can be completed within the available period of time, and 2)
the results will be useful in helping to resolve the problem toward
which the study is directed. It became obvious to a group of
agricultural economists2 working with the graduate program that
the various standard approaches to the presentation of research
methodology are not successful in helping students become efficient
researchers consistently able to make meaningful contributions to
the resolution of agricultural and related problems of their country.
Initially the efforts of Michael Steiner who was responsible for the
methodology class and of James Driscoll, Chris Andrew, and Peter
Hildebrand, who were helping in the development of the material,
focused upon new means of presenting the requisites for successful
research in a manner that would have real meaning and utility for
the students. Thus, the core idea now presented in Chapter III ger-
minated. The topics are not new; most research methodology
material discusses research problems, hypotheses, and objectives.
It is the means of fitting these parts together into a more useful
form which is new. During several courses and while counseling
students and fellow staff members of ICA in research, we were able
to revise and improve the concepts. We found at the same time that
this approach to planning and executing applied research could be
easily understood and used. As the success of the approach became
clearer, we decided that it was worth the time required to present it
in book form for a wider audience.
In the process of developing the book, to which all four of us in-
itially contributed, we found that, although it was relatively easy to
use the approach in training students and counseling researchers, it
was difficult to present the approach in a form understandable and
usable by persons with whom we would have no direct contact.

'The graduate school is jointly administered by the National University of Colom-
bia (UN) and the Colombian Agricultural Institute (ICA). Besides the graduate
school, ICA has responsibilities in research and extension as well as service ac-
tivities such as control of agricultural chemicals and port sanitation.
'This group consisted of several Colombian agricultural economists with ICA in-
cluding those mentioned in the acknowledgements and agricultural economists with
the University of Nebraska Mission in Colombia. This technical assistance team
worked with ICA, National University and the Graduate School from 1967 to 1972.








Because of our various commitments and spatial separation it
became increasingly difficult for all four of us to coordinate our ef-
forts, so finally Andrew and Hildebrand assumed the responsibility
for the long process required to convert the vague ideas and con-
cepts into a form which could be readily conveyed to students,
researchers, and others.
The basic theme of this book is that of applied research as a
service to a client with a problem for which the information obtained
by research can help resolve. Because applied research has a definite
purpose, there is usually a time constraint or deadline within which
the work must be completed as well as a limit on the other resources
the client has available or is willing to use in the resolution of the
particular problem. Consequently, the researcher must be cognizant
of the efficient use of research resources while at the same time func-
tioning so as to maximize the likelihood of providing a useful
product to the client.
Besides the approach to applied research (covered in the book),
another important factor affecting the success a researcher will ex-
perience in serving his clients is the research environment within
which he labors. An applied researcher cannot be effective in satis-
fying clients when he is isolated from them by a system that reduces
or prevents effective communication between them.3 This can hap-
pen, for example, when an extension service with direct client con-
tact has little communication with research personnel even though
they may be in the same organization. It can also happen in research
organizations in which projects are dictated by administrators who
have little contact with the clients and hence have no appreciation
of their real problems. This may be an argument for maintaining
only small research organizations, but more realistically, it is an
argument for an organization in which the researcher maintains
close personal contact with the clients and where he in turn shares
in determining the research priorities of the organization. We sug-
gest that a better coordinated working relationship between
research administrators, researchers, and clients will develop if all
three groups understand the approach to problem identification
which is presented in this book.
Although a majority of the examples are authentic, and are drawn
from Colombia where most of the writing was done, the research dif-
ficulties presented are not unique to that country; they are en-
countered throughout the world including the United States. Our
desire is to provide the researcher, wherever he may be, with an ap-
proach to research under various time and resource limitations

3See [2, 30, 38, 60, 61, 65, 69, 75, 80, 82, 86, 87, 88] for readings on the role and im-
pact of institutions in the research process.








which will help him be of greater service to his clients. This is par-
ticularly true in developing countries where applied research is so
needed.




ACKNOWLEDGMENTS

To acknowledge the assistance of each individual for comments
presented during numerous discussions and on the seemingly
endless series of drafts leading to this book is impossible. Without
the early dedication and ideas and comments by Mike Steiner
(presently with the Armour Food Company) and Jim Driscoll
(presently with the Economic Research Service of the United States
Department of Agriculture) this book would not have been initiated
nor, possibly, completed. Two Colombians, Juan Acosta and
Ramiro Orosco, at the Colombian Agricultural Institute (ICA)
deserve special recognition for reviews of the material and construc-
tive criticism, and for using concepts and early drafts in the
classroom. Likewise to Rafael Samper, Department Head in
Agricultural Economics at ICA, and his staff, gratitude is due for
sustained interest and encouragement.
Ideas and most of the drafts for the book were forthcoming while
the authors were under contract with the University of Nebraska at
the Colombian Agricultural Institute and the National University
of Colombia. This contract, funded by the Ford Foundation and the
USAID, helped develop the agricultural economics profession in
Colombia including three undergraduate programs, a graduate pro-
gram, and both research and extension programs. To our colleagues,
and to students at these institutions, we offer sincere
acknowledgments and hope that the book will be useful to them.
We express our appreciation for assistance received from the
Food and Resource Economics Department at the University of
Florida. To Fred Prochaska and his students who have used the text
for two years in the research methodology course, we are grateful
for constructive criticism. Special recognition is due to Leo
Polopolus, Chairman of the Food and Resource Economics Depart-
ment, and W. W. McPherson, Graduate Research Professor, for
reviews and consultation. Special appreciation is extended to Beth
Davis for supervising preparation of the final manuscript for
printing.
Also, we extend our appreciation to the Ministry of Agriculture of
El Salvador, where the second author was stationed on a technical
assistance contract, for translation and preliminary publication of
the manuscript in Spanish. For final reviews of the Spanish transla-







tion and preparation of the final manuscript for printing, gratitude
is extended to the Guatemalan Institute of Agricultural Science and
Technology where the second author was employed from 1974 to
1979.
Lastly, we want to express our sincere gratitude to our families
who shared their time for numerous weekends and nights with an
idea which may have seemed vague and boring.
Chris O. Andrew
Peter E. Hildebrand













CHAPTER I


INTRODUCTION


About midmorning, the Minister of Agriculture is just com-
pleting a phone conversation as the door to his office opens to admit
one of the young men in the research group from the Planning
Department of the Ministry. At about the same time the Vice
Minister and the head of planning enter and the four men seat
themselves around a small conference table. The Minister opens the
meeting, directing his comments to his Vice Minister: "I'm told
that we have a final report on that project on importing fertilizer,
that's great. It's just in time. Now we ought to be able to convince
the import-export people that they can't reduce our request for fer-
tilizer and still expect us to meet our new trade commitments."
Turning to the young researcher, the Minister says encouraging-
ly, "OK, young man, they tell me you've made an excellent study.
Now tell us exactly how much fertilizer is it going to take over the
next five years to meet our production targets?"
"Sir?" replies the researcher, apparently a little confused.
"Come, come, now, don't be nervous. Just tell us about the
research you've been doing. What are the results?" The Minister
realizes the researcher may be a bit timid.
"Oh, yes sir," smiles the researcher, "thank you."
"Well, as soon as we got your request for information on the im-
portance of fertilizer, we checked to see what data we had on Japan
and some other countries where there have been recent increases in
production. We thought this ought to give us some good ideas about
the relationship between fertilizer use and crop production. Here,
sir, we have a series of graphs showing the correlation between
these two variables for a number of countries."
"Yes," replies the Minister, "it's quite evident that fertilizer is
important in increasing crop output. Now, how much are we going
to need?"
The researcher continues, "According to the latest census, which
unfortunately is several years old as you know, only about 40 per-
cent of our farmers are using any fertilizer. This is considerably
below the rate in the other countries I mentioned. And in those
countries income per farm family has been increasing rapidly, again







demonstrating the importance of fertilizer." The Minister nearly in-
terrupts but lets the researcher continue. "Now if we want to double
the number of farmers using fertilizer, we might be able to assume
that we need twice as much fertilizer as now."
"Yes, I suppose" replies the Minister, "but what about the land
area involved and what about the requirements for the different
crops?"
The researcher, thumbing back through his report answers, "We
don't have any information on area of each crop that is fertilized but
cotton and sugarcane consume about 80 percent of the fertilizer
used and..."
He is interrupted by the Minister who says, "But don't you have
any estimates of the quantities required to get the production we
need over the next five years? Our problem is that they want to cut
back on fertilizer imports to help domestic fertilizer production just
at the time we have to try to increase crop production in a big hurry.
We need to know how that could affect our program and how much
importation we need to ask for." Turning to the head of the plann-
ing group he says, "I thought we discussed this pretty thoroughly
that day it came up. What happened?"
"Well, that's right, and I knew we were going to have a hard time
convincing them how important fertilizer was in our program and I
know we talked about that in my office, didn't we?" the head of
planning asks the young researcher.
"Yes, sir," replies the young man, "we knew we had to get you
some good information on the' importance of fertilizer and that's
why I have this information on Japan and those other countries."
"Well," replies the Minister, "that's not going to be much help in
solving our problem. We have the meeting with them tomorrow.
But maybe we could hold off a decision for a couple of days if you
think you can get me the information we need by that time. Why
don't you call someone at the experiment station? Maybe they can
help you. But you better get going. Don't forget how important fer-
tilizer is to us."
There are several important points in this dialogue, which though
fictional, represents a real-life situation encountered much too fre-
quently. The most important point is that after waiting right up to
the deadline, the Minister, who is the client, did not obtain the infor-
mation he needed for a meeting of great importance. As a result, the
research costs incurred by the Planning Department for this project
yielded little of value. The most serious consequence, of course, is
the cost associated with not having the relevant information for the
meeting. Although some of the reasons for the unfortunate situa-
tion are evident in the dialogue, others are more subtle. Regardless
of the reasons, we hope that with this book we can contribute to the







more effective use of research resources and help prevent the kind of
unhappy discussion as that between the Minister and the re-
searcher.


Applied Research
Research is the orderly procedure by which man increases his
knowledge and is contrasted to accidental discovery because it
follows a series of steps designed precisely for the purpose of
developing information.' Knowledge gained by research may be
used by man to produce a greater abundance of food and fiber, to
lighten the burdens of his labor, or in any number of ways to
generally improve his well being. Or new knowledge may simply be
added to man's store of concepts about the universe to await ap-
plication at some future point in time. Research undertaken
specifically for the purpose of obtaining information to help resolve
a particular problem is applied research. For a research undertaking
to be applied research it is not necessary that the results (the new
knowledge) in fact resolve or help resolve the problem which in-
itiated the project (though hopefully they will), but it is necessary
that the research have a specific problem orientation. It is this kind
of research that oriented toward resolution of specific
problems toward which this book is directed.
The development of Mexican or dwarf wheat was the result of an
applied research process oriented toward the resolution of a specific
problem [72]. A fertilizer experiment oriented toward making
recommendations to farmers is another example, as is the work of a
government planner trying to estimate the likely supply response to
fertilizer for a particular commodity under a proposed new program,
or the total fertilizer requirements necessary to reach specified pro-
duction goals, as in our dialogue. Determining acceptability of a
newly developed feed concentrate for fattening hogs in tropical
areas or the development of a hand seeder for steep terrain in
primitive areas also would be classified as applied research. In
general, the research referred to in this book is oriented toward pro-
viding useful information to decision makers such as farmers and
public administrators.
Applied research, such as that just described, is carried out in all
parts of the world it is a much more widespread activity than
basic research which is a necessity but one that only the wealthiest
countries can afford. Most applied research is conducted under
moderate to severe resource limitations which necessitate efficiency

'For thoughts concerning the meaning of research and the scientific method see [9,
10, 11, 12, 14, 15, 19, 22, 23, 24, 25, 28, 34, 39, 52, 71, 73, 74, 81, 83].







in the research process. An effective applied research methodology
is directed toward the efficient use of available research resources to
maximize the probability of achieving meaningful results to help
resolve problems. Disappointment in the results of applied re-
search a "So what?" response in most instances can be traced
directly to the use of an inadequate and/or ineffective applied
research methodology which failed to correctly identify the
problem.

The Book
Perhaps the most critical deficiency in methodology is the failure
to adequately identify the specific problem toward which the
research is to be oriented (as happened to the researcher in the
Ministry). This may result when the researcher uncritically accepts
the problem as stated by the client or by his spokesman (the "impor-
tance of fertilizer" was not the Minister's problem). Another serious
deficiency may occur even after properly identifying the problem.
This is the failure to formulate hypotheses and objectives correctly
oriented toward the resolution of the problem and to use ap-
propriate analytical techniques (what hypotheses did the Ministry
researcher use?). The most critical concepts are the interrelation-
ships among problem identification, hypotheses, objectives,
analytical techniques, and resource restraints.
The role of theory, though not developed within the main text, is
also critical to applied research. Without discounting the value of
practice and experience, the greater the command of theory pos-
sessed by the researcher, the broader will be his capabilities and the
more efficient he will be in planning and conducting the applied
research project. This is true because theory envelopes and supports
the entire research process.2 Without a good command of stress
theory an engineer cannot properly design nor efficiently build a
safe bridge. A plant breeder must understand the theory of genetics
before he can hope to efficiently develop a strain resistant to a cer-
tain disease. An agricultural economist cannot determine an op-
timum farm organization without knowledge of production
economics theory.
The researcher's foundation in theory provides the orientation for
defining a problem that is researchable within the discipline or
disciplines involved in the research and with the resources available.
Theory also provides the basis for the formulation of hypotheses
and in the selection of the analytical techniques to be used. And it

2For literature concerning the role of theory in research see [20, 35, 40, 53, 62, 63,
70].







should be obvious that the interpretation of the results depends
heavily on the theoretical orientation of the researcher.
Although theory permeates the entire research process, in applied
research, frequently conducted under sub-optimum conditions, the
researcher's practical experience is equally important. Institutional
and budgetary restraints, less than ideal field conditions, poorly
trained personnel, inadequate background information and other
similar factors have a very significant effect on the research process
and therefore must be recognized and dealt with accordingly. Prac-
tical experience is invaluable in helping the researcher overcome the
obstacles which are so often encountered in applied research. In all
phases of the material to be presented, the difficulties associated
with sub-optimum research conditions under which the individual
researcher is apt to be working are considered.
In this book, we have divided the topics into separate chapters
and the chapters into two parts derived from the book title; a con-
vention to which we adhere, though not without some reservation.
Neither section nor the material in any chapter is independent.
Planning activities are discussed in Part I of the book. In plan-
ning the research project, one must always take cognizance of the
means available for conducting the research, and during the
research process it may be necessary to modify portions of the
original plan. Each of the activities is affected by the others and by
the research resource restraints under which the researcher is toil-
ing. The kinds and sources of data which will be used and the
methods of analysis will be dictated by the hypotheses and objec-
tives, but they, in turn, must be finalized only after having taken
into account the effect of resource conditions on availability of data
and/or analytical competence.
In Part II, Conducting Applied Research, we discuss experimen-
tal and non-experimental data collection, verification and inter-
pretation of data, and presentation of the results to the client.
Because of the deliberate and intense emphasis placed upon
problem identification, the book does not discuss in detail each
basic element of a research project. One should be cognizant,
however, of the interrelated components of a research project and
employ them when planning and conducting applied research. These
essential elements are: 1) a problem statement accompanied by suf-
ficient information to justify the need for research; 2) hypotheses; 3)
objectives; 4) budget; 5) the appropriate theoretical and analytical
approach and procedures; 6) data requirements including sources
and procedures for obtaining data; 7) a detailed work plan showing
jobs to be done and time sequences; and 8) the reports to be issued
for each audience.










PART ONE


APPLIED


PLANNING
RESEARCH














CHAPTER II


EFFECTS OF RESOURCE AVAILABILITY
ON APPLIED RESEARCH

The relationship of research activities to the availability of
research resources is an important difference between applied and
basic research. In much of what is commonly considered basic
research, a proposal is prepared, and if funding is granted the pro-
ject is initiated. It normally continues so long as financing is
available and then whatever can be concluded is presented as the
results of the project. Seldom are the results periodically scrutinized
to determine their relationship to the proposal because the urgency
associated with solving a pressing problem is missing. Nor are
results frequently weighed against the use of resources to estimate
the productiveness of the project.
Applied research is more often (though not entirely) carried out
under other circumstances. Because the research is oriented toward
the resolution of a specific problem, there is usually a time restraint
fixed by the need to make a decision. Such research is also carried
out under varying degrees of financial restrictions and usually
under rather severe shortages of trained manpower and modern
data processing resources. Another research resource which is
seldom abundant under many conditions of applied research is
published data, other forms of secondary data or reliable informa-
tion in general. Basic physical facilities such as means of transpor-
tation or land area for research can also limit the scope of research
activities.
In the process of fitting the project to the resource restrictions,
three alternatives are possible; 1) the resources may be expanded to
fit the project, 2) the project may be narrowed to fit within the
restriction, or 3) both may occur within limits. The first alternative
is appropriate if the level of precision or degree of confidence desired
by the client prohibits a reduction in the scope of the project. In this
case, the client must be prepared to provide additional resources
where the limitations are critical whether it be in physical facilities,
manpower, funds, additional time for completion, or some combina-
tion of these provisions.








If resources cannot be expanded for a particular project and the
deadline for conclusion is firm, the researcher has four further
possibilities open to him. First, he may study fewer variables, ignor-
ing some relationships which affect the analysis, but those which he
hopes are less important than the ones included. Second, he may
also aggregate variables into groups. In this manner, it is possible
to include those relationships which may otherwise have been omit-
ted, but the nature of the relationships, because of the aggregation,
becomes less clear. A third alternative is to modify or change the
nature of the analysis to be carried out. Less complex analyses can
usually be conducted more rapidly and with fewer facilities, but the
precision of the results is reduced accordingly. Finally, the re-
searcher may choose to make fewer observations either in the form
of fewer replications or a reduced sample.
It is clear that resource availability has an important effect on the
nature of the research product derived and the level of precision
which can be achieved or the level of confidence which can be placed
in the results. Even in very limited resource situations, however,
decisions are necessary and researchers are expected to provide
useful information. The ultimate decision as to the quantity of
resources to be made available for any particular project and the
time limit for its completion rests with the client, or with the person
responsible for making decisions related to the resolution of the
problem toward which the research is oriented. At the same time,
the client depends on the researcher to provide him with accurate
measures of resource requirements and the scope and precision
which he can expect from devoting different amounts of resources
to any particular project.
The following sections will describe the principal research
resources and include a general discussion of how each resource,
when restricted, can affect a project. A single chapter does not ade-
quately cover the range of alternatives open to the researcher and
his client, but we hope that it will provide sufficient stimulus so that
the researcher, with imagination, will be flexible in adapting his
research efforts to any resource situation.'

Information Resources
Information is the foundation upon which research is based;
hence, one of the major tasks of the researcher is collection of infor-
mation for use in the research process. Information in general and
data specifically are as critical to the problem identification phase of
the project as they are to analysis. Their availability profoundly af-

'See [2] for a discussion concerning allocation of scarce resources to alternative
research programs and projects.








fects both the quantity and quality of research which can be pro-
duced within a given period of time. There are few limits to the
quantity of data that can be accumulated given sufficient time and
resources, however, requirements are narrowed and brought into
focus by careful research planning.
In general, published information provides the basis for problem
formulation while either published data or data generated in the
research process (or both) may be the source of information for
analysis. Published data, or any information not generated or ac-
cumulated under the control of the researcher but utilized in the par-
ticular project are considered to be secondary data. In contrast, any
data generated by the researcher and directly associated with the
research project are primary data. Another classification of data
which has an importance to research is that which differentiates
time series data, or observations made at specific intervals over a
period of time, from cross-section data which are taken at one point
in time. A third comparison is that between experimental and non-
experimental data. Each of these kinds of data or data sources has
different costs associated with availability and analysis, and each
has different implications with respect to confidence in conclusions
based upon it.

Secondary and Primary Information
When secondary information related to the project is available, its
cost to the researcher, both in terms of time and money, is usually
less than that required to obtain the same kind of information first
hand. However, the usefulness of secondary data as a research
resource is not always as great as that of primary information. The
researcher must always select the appropriate primary secondary
data mix and the techniques used to combine the two in order to ob-
tain the most useful research product within the limits of his other
resources.
For practical purposes, some data are only available from sec-
ondary sources. Price series, crop production series and census in-
formation are examples. It is simply not appropriate to consider ob-
taining this kind of information first hand. But it does not follow
that available secondary data of this nature are always adequate,
representative, or even relevant to the particular project. The
researcher must satisfy himself that any particular series really
measures something that is relevant to the project or something
which can be made relevant through acceptable modifications or
manipulation. If care is not taken to verify secondary information in
this manner, the researcher may well draw false conclusions from
his analysis of the data.








Primary data usually will be more closely related to a particular
project than secondary data which are collected for a multitude of
purposes or for projects with other objectives. But primary data col-
lection almost always requires more time than is necessary when us-
ing secondary data, and may require more of the other resources.
Hence, although primary and secondary data are not necessarily
substitutes for each other, the researcher should be aware of the
availability of secondary information and assess its relevance as an
alternative to the collection of primary data.


Time Series and Cross-Section Data2
Time series data, as the name implies, are data obtained in a series
over a period of time. Examples are price series, production and
acreage data, and indices of costs of living or wages, most of which
must be accumulated over long periods of time to be useful. Cross-
section data are those taken at a fixed point in time (or over a
relatively short period of time) and include observations of several
different strata or levels of a population. In many cases, time series
data are essential to a project, but occasionally, cross-section infor-
mation can be substituted. Time series data of consumption and in-
come may be used to study the same relationship at a given point in
time. Because, as in this case, cross-section data can substitute for a
time series, the researcher should not despair if a time series is not
available. Nor, by the same token, should he blindly use the time
series without considering the advantages of obtaining and using
the cross section data. Or in some cases it might be desirable to use
a combination of the two.


Experimental and Non-Experimental Data
Experimentation is a means of obtaining data with relatively high
precision in measurement of the variables. In many instances this
precision is associated with a longer time requirement than that
needed for obtaining non-experimental data. In crops a minimum of
one season is required and much longer periods may be needed if, for
example, effects of weather are to be determined. A year or more
may be common for some animal experiments. When, as discussed
in Chapter I, the Minister requested that the researcher check with
someone at the experiment station to help resolve the fertilizer de-
mand question, he was hoping that secondary experimental data
might provide specific crop requirement guides to be used in

2For a brief discussion of how these two types of data influence demand analysis
as one example, see [64].








Primary data usually will be more closely related to a particular
project than secondary data which are collected for a multitude of
purposes or for projects with other objectives. But primary data col-
lection almost always requires more time than is necessary when us-
ing secondary data, and may require more of the other resources.
Hence, although primary and secondary data are not necessarily
substitutes for each other, the researcher should be aware of the
availability of secondary information and assess its relevance as an
alternative to the collection of primary data.


Time Series and Cross-Section Data2
Time series data, as the name implies, are data obtained in a series
over a period of time. Examples are price series, production and
acreage data, and indices of costs of living or wages, most of which
must be accumulated over long periods of time to be useful. Cross-
section data are those taken at a fixed point in time (or over a
relatively short period of time) and include observations of several
different strata or levels of a population. In many cases, time series
data are essential to a project, but occasionally, cross-section infor-
mation can be substituted. Time series data of consumption and in-
come may be used to study the same relationship at a given point in
time. Because, as in this case, cross-section data can substitute for a
time series, the researcher should not despair if a time series is not
available. Nor, by the same token, should he blindly use the time
series without considering the advantages of obtaining and using
the cross section data. Or in some cases it might be desirable to use
a combination of the two.


Experimental and Non-Experimental Data
Experimentation is a means of obtaining data with relatively high
precision in measurement of the variables. In many instances this
precision is associated with a longer time requirement than that
needed for obtaining non-experimental data. In crops a minimum of
one season is required and much longer periods may be needed if, for
example, effects of weather are to be determined. A year or more
may be common for some animal experiments. When, as discussed
in Chapter I, the Minister requested that the researcher check with
someone at the experiment station to help resolve the fertilizer de-
mand question, he was hoping that secondary experimental data
might provide specific crop requirement guides to be used in

2For a brief discussion of how these two types of data influence demand analysis
as one example, see [64].








preparation of a demand estimate. He knew time would not permit
the design and analysis of crop experiments.
In some cases, however, experimentation can reduce the time and
resources required to resolve a particular problem when compared
with non-experiemental data collection. For example, an experiment
to determine potential consumer acceptance of a new product before
it is marketed can be less time consuming, require fewer resources
and involve less financial risk for an industry than consumer
response research following the full scale production and marketing
of the product.
A possible alternative to experimentation is the collection of non-
experimental data through a survey of a number of people who have
knowledge or experience with the phenomenon in question. This
procedure usually requires less time, but may be less precise and
more costly than experimental data collection. An approximation of
a fertilizer experiment for example, can be made by surveying a
group of farmers, each of whom uses different quantities of fer-
tilizer. Obviously, results will be less precise than experimental
results, but at the same time, estimates of variance will be more
realistic than from a controlled experiment so that farmers will have
a better idea of the range of response to expect from the use of fer-
tilizer.
Another non-experimental source of information in the context of
applied research is the simulation of results through the use of "best
guesstimates" of the most knowledgable people available to the
researcher. In some ways, this is similar to an informal survey. For
instance, sufficiently detailed input requirements, yields, and
resource restrictions can be generated in this manner for use in
preparing budgets, and ultimately a linear program or simulation
model for the agricultural sector of a region. Such a model, while
less valid than could be possible under more optimal conditions, can
be used successfully in project planning where time limits prohibit
experimentation and current conditions in the area will not provide
the detailed information needed from a survey.3



Human Resources
As with most resources, the human element must be considered
from the points of view of quantity and quality. Sheer availability is
not sufficient for most research undertakings; the training and

3An example is research conducted in El Salvador [89] by the Food and Resource
Economics Department, Institute of Food and Agricultural Sciences, The Universi-
ty of Florida.








capabilities of personnel must be considered when planning the pro-
ject. Except in rare instances, the time factor in applied research
prohibits the training of professional personnel though there may be
time to train some non-professionals such as interviewers. A field
hand who cannot read or write may be willing to do the physical
labor of an experiment, but he cannot be relied upon to maintain
records of the results. Nor can professionals with a minimum of
training in statistics be expected to carry out complicated
statistical analyses. In the first case, other arrangements will have
to be made, and in the second, less sophisticated techniques will
have to be employed.
A common misuse of research resources exists where elegant data
collection techniques are employed but the data are not fully util-
ized because appropriately trained personnel are not available to
make proper or correct analyses. In the short run, simpler ex-
periments and surveys which can be analyzed readily by available
personnel are more appropriate. Money saved by not conducting
elegant data collection programs may be used to train personnel to
conduct and analyze more complicated and sophisticated ex-
periments and surveys in the future.



Physical Resources
Non-technical physical resources include transportation facilities,
land, office space, machinery, typewriters, and other items of a
similar nature. Like all other research resources, their availability
must be considered when planning the project.
Technical physical resources include scientific instruments,
calculators, electronic computers, etc. Certain kinds of instruments
may be indispensable for particular aspects of a project some
hypotheses may need to be eliminated if the correct instrument is
not available or is too costly compared to expected results to justify
its use. On the other hand, an electronic computer, while not in-
dispensable, may substitute for other resources such as money or
time. The use of a computer, when available, sometimes can shorten
the time required to achieve useful results. If a researcher must wait
long periods of time, however, for cards to be punched and programs
to be de-bugged, he may be better off to undertake appropriate
analyses on a desk calculator. When computers and calculators are
unavailable a diligent researcher might still provide rough but
meaningful recommendations based upon experience and im-
aginative use of the most basic of physical resources a pencil and
paper.







Financial Resources
This resource to a certain extent can be substituted for all the
other resources. At the same time it may not be appropriate, feasi-
ble, or even possible to make this substitution because of other con-
siderations. Nevertheless, funds are required for almost all research
projects and their availability is an important consideration in
research planning.





Time Constraints
Time is usually not thought of as a resource in the same terms as
physical facilities, information and human resources, but its effect
on the planning and execution of applied research is similar. Con-
sidered as a resource, time can interact with other resources in that
substitution of one for another can be made. If a decision on a par-
ticular problem is critical and time is limited, a greater number of
other resources will be required to achieve a given level of con-
fidence than would be necessary if more time could be taken. Hence,
the use of more time is an effective substitute for quantities of other
resources. But also, consuming more time on one project reduces
the amount of that limited resource which is available to help
resolve other problems.
In some situations, time can be overwhelmingly limited. This is
usually the case when one is involved in so-called "brush fire"
research of the type frequently faced by planning groups within the
various ministries of government. In such instances the researcher
must always maximize the efficiency with which he uses this
resource. Only data readily available can be used and lengthy
methods of analysis cannot be considered. Many times "best
guesstimates" are the only means available to the researcher under
these conditions.
Approaching the other extreme are theses at any of the levels at
which they are written in various education systems of the world.
Often they are not oriented toward any particular problem so the
time factor is not relevant, but among those that are problem
oriented, too often time is relegated to a secondary role in resource
utilization. As a result, many theses are not written in time to be of
any great use their value reduced by failure to account for the
time factor. Those who excuse this fault by emphasizing that theses
are only meant to be training tools often deprive the student of an
opportunity to perform and benefit from meaningful research.







Financial Resources
This resource to a certain extent can be substituted for all the
other resources. At the same time it may not be appropriate, feasi-
ble, or even possible to make this substitution because of other con-
siderations. Nevertheless, funds are required for almost all research
projects and their availability is an important consideration in
research planning.





Time Constraints
Time is usually not thought of as a resource in the same terms as
physical facilities, information and human resources, but its effect
on the planning and execution of applied research is similar. Con-
sidered as a resource, time can interact with other resources in that
substitution of one for another can be made. If a decision on a par-
ticular problem is critical and time is limited, a greater number of
other resources will be required to achieve a given level of con-
fidence than would be necessary if more time could be taken. Hence,
the use of more time is an effective substitute for quantities of other
resources. But also, consuming more time on one project reduces
the amount of that limited resource which is available to help
resolve other problems.
In some situations, time can be overwhelmingly limited. This is
usually the case when one is involved in so-called "brush fire"
research of the type frequently faced by planning groups within the
various ministries of government. In such instances the researcher
must always maximize the efficiency with which he uses this
resource. Only data readily available can be used and lengthy
methods of analysis cannot be considered. Many times "best
guesstimates" are the only means available to the researcher under
these conditions.
Approaching the other extreme are theses at any of the levels at
which they are written in various education systems of the world.
Often they are not oriented toward any particular problem so the
time factor is not relevant, but among those that are problem
oriented, too often time is relegated to a secondary role in resource
utilization. As a result, many theses are not written in time to be of
any great use their value reduced by failure to account for the
time factor. Those who excuse this fault by emphasizing that theses
are only meant to be training tools often deprive the student of an
opportunity to perform and benefit from meaningful research.








Summary
Research undertaken specifically for the purpose of obtaining in-
formation to help resolve a particular problem is applied research.
The purpose of this chapter has been to examine the relationship
between the scope of an applied research project and the quantity
and quality of research resources which can be devoted to the solu-
tion of the problem at hand. The urgency of problem resolution
makes time an important resource or constraint which interacts
with the other financial, human, physical, and information
resources.
The researcher must be aware of the effect that certain resource
limitations can have on his research. This cognizance will improve
his research effort by increasing the probability that the proposed
project will produce useful results. Projects designed in the absence
of this consideration can and frequently do run into difficulties such
that the productive potential of the resources utilized is not at-
tained. The result is that less effective information is made available
for decision making and problem resolution. Careful consideration
of resource availability can help prevent situations where the
deadline arrives and the researcher, still engrossed in gathering
data or making analyses, has little of value to report to his client.







CHAPTER III


ORIENTATION AND FOCUS OF PROJECTS:
RESEARCHABLE PROBLEMS, HYPOTHESES,
AND OBJECTIVES
Proper management of applied research requires clear definitions
of the goals to be achieved through the project. If one does not know
for what he is striving he cannot hope to effectively accomplish the
task. Orientation and focus of the project include the specification
of the problem in terms which make it amenable to research, the for-
mulation of hypotheses which are subject to being tested, and the
delineation of the specific objectives which the project should ac-
complish. The interaction of these three phases and their clear ex-
position serve as a plan or guide for determining the procedures to
be followed by the researcher in conducting the research. Adequate
orientation and focus of the project will help assure that it can be
completed satisfactorily within the resource limitations facing the
researcher and will also serve to explain the nature of the undertak-
ing to the client or the administrators for whom the research is be-
ing undertaken.

A Conceptual Model
We have found it useful to consider the process of planning the ap-
plied research project as being equivalent to a funnel with a series of
filters (Figure 1). Such a funnel is used to reduce a large volume of
liquid to manageable proportions. The orientation and focus of a
research project serves the same purpose it reduces a large
volume of information to manageable proportions. Extraneous in-
formation and ideas are eliminated as foreign matter might be
filtered in the funnel. Each part of the project statement -
problem, hypotheses, objectives serves to narrow down the pro-
posal, to bring it into sharper focus, and to filter out surplus or
unrelated information to make the orientation more precise. One can
consider the size of the lower opening of the funnel as being deter-
mined by available research resources (the bottleneck). Hence, the
proposal as it finally emerges must fit within these resource restric-
tions.
The top of the funnel will be the general subject matter orienta-
tion of the researcher. The research will be within the area of in-
terest of the individual researcher and usually related to his in-
dividual talents. The general problematic situation will fall within
these limits, but any problematic situation suggested by a client
may contain several researchable problems. Hence, the selection of







CHAPTER III


ORIENTATION AND FOCUS OF PROJECTS:
RESEARCHABLE PROBLEMS, HYPOTHESES,
AND OBJECTIVES
Proper management of applied research requires clear definitions
of the goals to be achieved through the project. If one does not know
for what he is striving he cannot hope to effectively accomplish the
task. Orientation and focus of the project include the specification
of the problem in terms which make it amenable to research, the for-
mulation of hypotheses which are subject to being tested, and the
delineation of the specific objectives which the project should ac-
complish. The interaction of these three phases and their clear ex-
position serve as a plan or guide for determining the procedures to
be followed by the researcher in conducting the research. Adequate
orientation and focus of the project will help assure that it can be
completed satisfactorily within the resource limitations facing the
researcher and will also serve to explain the nature of the undertak-
ing to the client or the administrators for whom the research is be-
ing undertaken.

A Conceptual Model
We have found it useful to consider the process of planning the ap-
plied research project as being equivalent to a funnel with a series of
filters (Figure 1). Such a funnel is used to reduce a large volume of
liquid to manageable proportions. The orientation and focus of a
research project serves the same purpose it reduces a large
volume of information to manageable proportions. Extraneous in-
formation and ideas are eliminated as foreign matter might be
filtered in the funnel. Each part of the project statement -
problem, hypotheses, objectives serves to narrow down the pro-
posal, to bring it into sharper focus, and to filter out surplus or
unrelated information to make the orientation more precise. One can
consider the size of the lower opening of the funnel as being deter-
mined by available research resources (the bottleneck). Hence, the
proposal as it finally emerges must fit within these resource restric-
tions.
The top of the funnel will be the general subject matter orienta-
tion of the researcher. The research will be within the area of in-
terest of the individual researcher and usually related to his in-
dividual talents. The general problematic situation will fall within
these limits, but any problematic situation suggested by a client
may contain several researchable problems. Hence, the selection of


















Available


Plan of Execution


Figure 1.-Fitting the research project to the resources.
I I







a researchable problem based upon the client's needs is equivalent
to sharpening the focus on a particular aspect of the more general
problematic situation. Hypothesis formulation narrows the problem
to tentative relationships which will be tested in the research
process. Finally, the objectives specify the limits within which the
project will be conducted and describe the useful product which will
result.
Obviously, information, ideas and relationships do not flow
through the funnel like water but are filtered time and again. The
process of moving from the top to the bottom of the research project
funnel requires push and pull, paring and adjustment, specification
and redefinition until the proper proportions are achieved. The
problem, the hypotheses and the objectives may each have to be
changed and adjusted many times before a satisfactory product
results.
At this point, a word of caution is in order. The result of this fun-
nelling process should be a plan of execution that has a high
probability of accomplishing research which will be helpful in
resolving the problem toward which the research is to be directed. It
is much too common, and seemingly easier, to embark on the next
steps of the research process data collection, analysis, and inter-
pretation with a poorly specified project statement. The conse-
quence is usually that budget and time restrictions cannot be met
and one of the following results:
1) Conclusions must be drawn on the basis of inadequate
evidence,
2) More resources and more time must be devoted to the
project to allow completion, or
3) The project withers and dies and is relegated to a drawer
in the file, never to be heard from again.
No matter what fate the research meets, the return on the invest-
ment in the project will be lower than necessary. To avoid these con-
sequences, careful orientation and focus are essential and can well
be the most productive time spent on the entire project.

Specification of a Researchable Problem
Problem specification, or elaboration of a problematic situation in
such a way that it presents a researchable problem, is a vital step in
the process of applied research [42, 84]. Seldom is a client's problem
defined for the researcher so that the requirements of the research
process are obvious. Even in cases where it may at first appear that
it is so defined, it usually is not the case. A common and deceptively
simple appearing example is the problem of determining crop pro-
duction costs. An economist cannot uncritically accept a "cost of








production" project without understanding which specific cost com-
ponents are of interest to the client. The researcher, because of his
training, will usually have a greater appreciation for the technical
characteristics of the problem than will the client and should
therefore consider it part of his responsibility to identify symptoms
and diagnose the problem. He should perceive the identification of
the problem as a major task.1 On the other hand, the researcher can-
not assume that he automatically understands the problem better
than does the client. The researcher must work with the client until
they have jointly defined an acceptable and researchable problem.
Problem specification is not a simple process. Hildreth and Castle
summarized a discussion concerning problem identification as
follows:
"The start of research is the most important and difficult stage
of research. It requires far more than logic; it includes pro-
cedures which cannot be neatly categorized and
communicated." [56]
But there are several characteristics which a project statement will
possess if it defines a researchable problem within the context of ap-
plied research. These characteristics of a problem statement are
that problems reflect felt needs, problems are non-hypothetical,
problems suggest meaningful and testable hypotheses, problems
are relevant and manageable, and a researchable problem differs
from a problematic situation.
Problems Reflect Felt Needs
A problem exists when there is a need felt by a client. This client
may be an individual, a group, or a society. The need must be "felt"
in the sense that the originating party believes that change can be
realized, and it may arise from social tensions, doubts, conflicts,
failure to realize goals, concern for an anticipated occurrence which
might be preventable, or the simple lack of knowledge if this
knowledge is necessary to contribute directly to the resolution of
another felt need. To be appropriate as a researchable problem, the
need must be amenable to change as a result of the information sup-
plied by the research process. Hence, all felt needs are not necessari-
ly functional so far as permitting the formulation of a researchable
problem statement.
Problems Are Non-hypothetical
A researchable problem statement must be based on factual
evidence. The relationships expressed can be neither hypothetical
nor subject to doubt or question in the mind of the researcher. The

'For a discussion of various attitudes toward problem identification in research
see [68, 77, 79].








production" project without understanding which specific cost com-
ponents are of interest to the client. The researcher, because of his
training, will usually have a greater appreciation for the technical
characteristics of the problem than will the client and should
therefore consider it part of his responsibility to identify symptoms
and diagnose the problem. He should perceive the identification of
the problem as a major task.1 On the other hand, the researcher can-
not assume that he automatically understands the problem better
than does the client. The researcher must work with the client until
they have jointly defined an acceptable and researchable problem.
Problem specification is not a simple process. Hildreth and Castle
summarized a discussion concerning problem identification as
follows:
"The start of research is the most important and difficult stage
of research. It requires far more than logic; it includes pro-
cedures which cannot be neatly categorized and
communicated." [56]
But there are several characteristics which a project statement will
possess if it defines a researchable problem within the context of ap-
plied research. These characteristics of a problem statement are
that problems reflect felt needs, problems are non-hypothetical,
problems suggest meaningful and testable hypotheses, problems
are relevant and manageable, and a researchable problem differs
from a problematic situation.
Problems Reflect Felt Needs
A problem exists when there is a need felt by a client. This client
may be an individual, a group, or a society. The need must be "felt"
in the sense that the originating party believes that change can be
realized, and it may arise from social tensions, doubts, conflicts,
failure to realize goals, concern for an anticipated occurrence which
might be preventable, or the simple lack of knowledge if this
knowledge is necessary to contribute directly to the resolution of
another felt need. To be appropriate as a researchable problem, the
need must be amenable to change as a result of the information sup-
plied by the research process. Hence, all felt needs are not necessari-
ly functional so far as permitting the formulation of a researchable
problem statement.
Problems Are Non-hypothetical
A researchable problem statement must be based on factual
evidence. The relationships expressed can be neither hypothetical
nor subject to doubt or question in the mind of the researcher. The

'For a discussion of various attitudes toward problem identification in research
see [68, 77, 79].







researcher must use his judgement as he sifts through available in-
formation to determine to his satisfaction what are and what are not
acceptable facts and factual relationships. In a probability sense, of
course, few facts can be accepted with complete confidence.
However, the facts or factual relationships accepted by the client
and the researcher in the problem statement must be such that
testing of their validity is unnecessary. If a particular proposition
cannot be accepted as fact, then if it is relevant to the case, it must
be relegated to the status of a hypothesis.
All researchers and their clients will not accept the same informa-
tion as facts or factual relationships because individual judgements,
knowledge and experience affect this choice [5]. In defining the
researchable problem related to a shortage of manufactured dairy
products, one researcher, because of his experience and general
knowledge, may be willing to accept the proposition that there is no
shortage of milk processing equipment in a particular area. For him
this forms part of the problem statement. Another researcher may
feel that the validity of this proposition is not clear, so in the second
case, it must become one of the hypotheses (if it is relevant to the
problem). Obviously, the nature of the research project will be dif-
ferent in each case.
This is the stage of the research process in which a review of
literature is most productive. By means of this review the re-
searcher seeks to utilize the experience of others to help in the orien-
tation of the project. It should be apparent that this kind of orienta-
tion can change the focus of the research effort and can have a
significant impact on the productivity of the research resources.
Problems Suggest Meaningful, Testable Hypotheses
Because the statement of the problem serves to orient the entire
research process, it must suggest testable hypothetical relation-
ships. Hypotheses are formulated as partial explanations of the
unknown relationships which create the problem, and those which
cannot be tested will be of little assistance in the resolution of the
problem. Hypotheses are testable when information about their
validity may be collected and analyzed.
The hypotheses must also be developed from the problem state-
ment in a manner which does not result in trivial solutions. Triviali-
ty indicates a tautology, an obvious solution, or an infeasible solu-
tion.The hypothesis that "per capital consumption of food products
is low because there are too many people," derived from a problem
statement referring to the existence of hunger in a country is such a
hypothesis. Even if the hypothesis is substantiated, it results in an
answer which is of little or no use in the alleviation of the immediate
felt need.








If the problem statement does not suggest testable hypotheses
for resolution of the problem under investigation, the researcher has
not adequately formulated the problem for research.
Problems are Relevant and Manageable
Agricultural scientists tend to work at extremes. "They tend to
work either on problems where the outcome is highly predictable
but which has little impact on problems or on problems so large as
to be unmanageable" [56, p. 38].
This comment was made with reference specifically to
agricultural economists but it applies to other disciplines as well.
An agronomist designing an experiment to determine yield
response on a certain soil, even though information about similar
soils is readily available, is an example. The researcher knows with a
high degree of certainty whether or not a response will be observed.
Overambition, lack of adequate forethought, and inexperience are
the principal causes of unmanageable projects. An over-ambitious
researcher may be motivated by a desire to study all the problems in
a given sector so that he can answer any question that arises. Or an
unmanageable research project can result from suggestions by
clients or administrators unfamiliar with the discipline of the
research. In this case the researcher should not accept the project
without first more precisely defining the problem and reducing the
proposed project to manageable proportions within the time re-
quirement and within the human, financial and other resources
available for the project.
An unmanageable research project yields few benefits to anyone.
The most likely result of such a project is superficial treatment of
part of the problem and the neglect of other parts. Little detail will
be achieved, and much that is presented may be found to be inac-
curate or insufficient. The contribution of this type of study will
almost always be less than that of a more specific study which ex-
amines fewer phenomena, but does so in more detail.
Researchable Problems vs. Problematic Situations
Confusion is likely to exist relative to the differences between a
problematic situation and a researchable problem. In our context,
there is a real and functional difference between the two. First, a
problematic situation is a phenomenon which exists; a researchable
problem must be identified and defined. A problematic situation
represents a generalized situation but a researchable problem, ex-
pressed in the above terms, must be specific. "An increasing rate of
crime in the cities," assuming the statement is true, is an example
of a problematic situation it exists and it pertains to felt needs.
But in no way can the statement be construed as a statement of a re-








If the problem statement does not suggest testable hypotheses
for resolution of the problem under investigation, the researcher has
not adequately formulated the problem for research.
Problems are Relevant and Manageable
Agricultural scientists tend to work at extremes. "They tend to
work either on problems where the outcome is highly predictable
but which has little impact on problems or on problems so large as
to be unmanageable" [56, p. 38].
This comment was made with reference specifically to
agricultural economists but it applies to other disciplines as well.
An agronomist designing an experiment to determine yield
response on a certain soil, even though information about similar
soils is readily available, is an example. The researcher knows with a
high degree of certainty whether or not a response will be observed.
Overambition, lack of adequate forethought, and inexperience are
the principal causes of unmanageable projects. An over-ambitious
researcher may be motivated by a desire to study all the problems in
a given sector so that he can answer any question that arises. Or an
unmanageable research project can result from suggestions by
clients or administrators unfamiliar with the discipline of the
research. In this case the researcher should not accept the project
without first more precisely defining the problem and reducing the
proposed project to manageable proportions within the time re-
quirement and within the human, financial and other resources
available for the project.
An unmanageable research project yields few benefits to anyone.
The most likely result of such a project is superficial treatment of
part of the problem and the neglect of other parts. Little detail will
be achieved, and much that is presented may be found to be inac-
curate or insufficient. The contribution of this type of study will
almost always be less than that of a more specific study which ex-
amines fewer phenomena, but does so in more detail.
Researchable Problems vs. Problematic Situations
Confusion is likely to exist relative to the differences between a
problematic situation and a researchable problem. In our context,
there is a real and functional difference between the two. First, a
problematic situation is a phenomenon which exists; a researchable
problem must be identified and defined. A problematic situation
represents a generalized situation but a researchable problem, ex-
pressed in the above terms, must be specific. "An increasing rate of
crime in the cities," assuming the statement is true, is an example
of a problematic situation it exists and it pertains to felt needs.
But in no way can the statement be construed as a statement of a re-








searchable problem. The expression of a problematic situation can
serve as general orientation to the broad area of interest of a
research project, but it is not sufficient to serve as a guide to a
research project.
A problematic situation can be the source of a variety of re-
searchable problems. Different clients, different research ad-
ministrators and different researchers are likely to arrive at a varie-
ty of researchable problems from the same problematic situation.
That is to say that there is no single right, or correct, researchable
problem there is only a correct framework within which the
researchable problem must be expressed to be useful to the re-
searcher. The selection of the researchable problem which will ac-
tually be undertaken will depend on the situation within which the
research is being conducted.
Examples of Problem Statements
In evaluating statements which are considered by their author to
delineate researchable problems, it is difficult, if not impossible, to
determine when deficiencies are due to lack of care in problem for-
mulation on the part of the researcher or are due to an unclear con-
cept of the nature of the problem. An example of such a statement is
the following:2
1) "Rural unemployment created by an increase in the use
of agricultural machinery."
There is no doubt that the author was focusing on a problematic
situation and his interest had to do with unemployment. But it is
not clear what specific problem existed in his mind. The statement
does not provide the basis for a research project though it might
spark a lively debate. As it stands, it does not meet the basic re-
quisities for the specification of a researchable problem. Other ex-
amples of incomplete or incorrectly formulated problem statements
are the following:
2) "Few cattlemen vaccinate against hoof and mouth
disease."
3) "Exportation of fresh meat from Colombia."
Let us analyze the above three statements from the point of view
of the requisities necessary for a correctly specified problem state-
ment. Do they concern a felt need? Probably all do, but what exact-
ly, is that felt need? The felt needs suggested by those statements

2The examples used in the remainder of this chapter are taken from class papers
and research proposals submitted by graduate students in the Department of
Agricultural Economics, Instituto Colombiano Agropocuario, Bogota, Colombia.








are numerous. Are the rural unemployed a problem from the stand-
point of crime or poverty in the cities, or is the author perhaps con-
sidering those unemployed as a source of inexpensive labor for rural
industry? Is the author (or client) of the second statement the presi-
dent of a drug company or is he the head of a meat export company?
Is the third statement a problem from the point of view of Colom-
bia, which may be looking for increased foreign exchange, or,
perhaps of Argentina which may be considering Colombian competi-
tion in the world market? Each of these points of view suggests a
different problem, and hence, a different research project. It would
be folly to initiate a research project on any of the above topics
without more complete specification of the client's felt need.
Are the statements hypothetical or subject to question in the
mind of the author? This is the most difficult test for a third person
to make because he is not aware of the precise knowledge held by
the researcher or client. For this reason, proper background infor-
mation in the research project statement is critical. Not everyone
will automatically be convinced that the increased use of
agricultural machinery leads to rural unemployment. However, if
such a relationship is acceptable to both the client and the re-
searcher, and is considered firm and non-hypothetical, it can be ac-
ceptable in the problem statement as a non-hypothetical relation-
ship. The second and third statements do not express causal rela-
tionships and hence are not subject to this criterion. They are sim-
ply incomplete.
Do the statements suggest testable hypotheses? The first state-
ment could be formulated as a testable hypothesis, but if it is to be
accepted as a factual relationship for a problem statement, then it
cannot be a hypothesis for the purposes of the research. Although
testable hypotheses associated with the other two statements could
be contrived, any firm relationship between them and the problem
in the mind of the reader would be purely coincidental.
Another problem statement will be analyzed to determine if, or
how, it might be improved.
4) "Deficient milk production reduces domestic consump-
tion and makes imports necessary in this agricultural
subsector."
On the surface this appears to be a more complete problem state-
ment than any of the preceding. Certainly it represents a felt need;
in fact more than one. Is the problem to which the author is di-
recting himself a production problem, one of poor nutrition because
of inadequate quantities of milk, or is he concerned with the expen-
diture of scarce foreign exchange? From the present statement, it is
impossible to be sure.








The relationships expressed probably meet the non-hypothetical
requisite, though not necessarily. The first relationship is really a
tautology if "deficient" is defined in terms of domestic consump-
tion. The second relationship could be hypothetical unless govern-
ment policy, for example, has provided for the imports of milk to
make up deficits in domestic consumption.
The statement does not legitimately suggest any hypotheses ex-
cept the trivial ones that increased production would increase con-
sumption and/or reduce the necessity for imports. And these are not
really testable in the usual research framework. Hence, even though
the problem would be amenable to one or more meaningful solu-
tions, it is probably not researchable within the usual limits of time
and funds. Several years would be required to determine experimen-
tally if increased domestic production would, in fact, result in in-
creased consumption.
In order to improve this statement, it is first necessary to focus
more precisely on the orientation of the author with respect to his
felt need. It turns out that the author of the statement was con-
cerned with problems of production, principally with high costs, low
productivity, and the small profit margins of the producers of milk.
His research interest then focuses on the dairy herd and the dairy
farm, and possibly on the farm-to-market process. Although it will
never be possible to determine the exact nature of a proposed
research project from the problem statement alone, a more precise
statement is necessary before one can proceed to formulate
hypotheses and specify objectives. A better formulation of the
above statement resulted from more reflection by the author on his
orientation:
5) "The low level of technology on dairy farms contributes
to high costs of production, low average productivity,
and a deficient marketing system for milk. These factors
cause low profits to the producer, price fluctuations for
the consumer, and a deterioration in the balance of
payments because of the necessity to import milk."
In this form, the statement implies, in accordance with the re-
quisites, that the author accepts as fact that a low level of
technology is a causal factor in low productivity, high costs of pro-
duction and a deficient marketing system for the product. Further-
more, he accepts that these factors cause low profits, price fluctua-
tions and the necessity of importing milk. By accepting these rela-
tionships as fact, they cannot appear as hypotheses to be tested by
the research process. If the researcher or his client cannot accept
any of these relationships as factual, then the problem statement
should be modified and the doubtful statements submitted as








hypotheses to be tested (assuming they can be tested within the
limits of the available research resources.)

Formulation of the Hypotheses
It is clear that the logical sequence of events in the process of ap-
plied scientific inquiry begins with the observation of phenomena in
the empirical world in common sense terms. Determination and
classification of these events into problematic situations and
specific researchable problems set the stage for postulating various
potential means of problem resolution. This part of the process in-
volves the formulation of hypotheses which formalize the premises
to be tested by the research effort.
Hypotheses are tentative propositions that must relate to the
problem so as to be helpful in providing means of resolution. Any
suggested or indicated means of resolving a problem must be for-
mulated so as to be testable and the relationship to the problem
must be evident. To be logically complete and functional a
hypothesis must involve a relationship. Such a formulation con-
stitutes, implicitly if not explicitly, a hypothesis in the form of if-
then propositions. The "if" clause describes the relationship be-
tween the postulated condition and the proposed result. For exam-
ple, "If Colombia can increase its beef production by 15 percent and
reduce the cost of production by 10 percent, then it can successfully
export beef and meet domestic requirements." The first clause "If
Colombia can increase its beef production by 15 percent and reduce
the cost of production by 10 percent" sets the conditions that must
be met and the remaining clause relates the proposed results.
Hypotheses are derived from the observations and relationships
accepted as, or assumed to be, fact in the problem statement. They
provide the guidelines for the type of data and techniques necessary
for analysis. This implies that hypotheses are formulated before the
data collection activity of the research project has started. In this
sense hypotheses indicate the direction for data collection;
hypotheses that are formulated to explain observations after they
are collected may not be useful for problem resolution. The
hypotheses, then, are a necessary link between the problem and the
data collection and analytical stages of the research.
The basis for correct formulation of hypotheses is the knowledge
of the researcher, this knowledge being founded primarily in
theory.3 The broader the experience of the researcher in relating
theory to applied problems, the more efficient he will be in for-
mulating appropriate hypotheses. Whereas the researcher and the
3For this argument and others relating theory to the research process see [35, 45,
53, 63, 73].








client, jointly, must share the responsibility for problem specifica-
tion, it is primarily the responsibility of the researcher, as the expert
in his field, to formulate the hypotheses.

Characteristics of Hypotheses
Hypotheses appropriate to applied research have the following
characteristics:
1) They must be formed as if-then relationships and stated
in such a manner that their implications and relation-
ships to the problem can be shown logically. The explicit
use of the words if-then is not necessarily required; the
relationship, however, is critical and often an explicit if-
then statement will assure an accurate relationship.
2) They should be stated as simply as possible both in terms
of theoretical complexities and implications and in terms
of number of variables.
3) They must be capable of verification or rejection within
the limits of the research resources.
4) They must be stated in a manner which provides direc-
tion for the research. The hypotheses, when well for-
mulated, will suggest the appropriate data and analytical
techniques for testing that should be employed in the
research process. Thus, a set of hypotheses can be
thought of as a plan of action.
5) Taken together, they must be adequate and efficient in
suggesting one or more meaningful solutions to the
problem. They must provide for an acceptable level of
confidence in the results, but at the same time economize
in the use of scarce research resources.

Some Examples of Hypotheses
To continue with the proposed dairy study, the following are the
hypotheses which were submitted in the second draft of the pro-
posal:
1) "Increasing the level of technology and of physical pro-
duction and the economy per unit of exploitation, in
association with a reduction in the costs of production,
would result in stability between supply and demand."
2) "Providing more financial resources for increasing pro-
duction and restructuring the market channels would
allow simpler price regulation."
3) "Establishing milk regulations would provide optimum
quality and a price warranted by that quality."








client, jointly, must share the responsibility for problem specifica-
tion, it is primarily the responsibility of the researcher, as the expert
in his field, to formulate the hypotheses.

Characteristics of Hypotheses
Hypotheses appropriate to applied research have the following
characteristics:
1) They must be formed as if-then relationships and stated
in such a manner that their implications and relation-
ships to the problem can be shown logically. The explicit
use of the words if-then is not necessarily required; the
relationship, however, is critical and often an explicit if-
then statement will assure an accurate relationship.
2) They should be stated as simply as possible both in terms
of theoretical complexities and implications and in terms
of number of variables.
3) They must be capable of verification or rejection within
the limits of the research resources.
4) They must be stated in a manner which provides direc-
tion for the research. The hypotheses, when well for-
mulated, will suggest the appropriate data and analytical
techniques for testing that should be employed in the
research process. Thus, a set of hypotheses can be
thought of as a plan of action.
5) Taken together, they must be adequate and efficient in
suggesting one or more meaningful solutions to the
problem. They must provide for an acceptable level of
confidence in the results, but at the same time economize
in the use of scarce research resources.

Some Examples of Hypotheses
To continue with the proposed dairy study, the following are the
hypotheses which were submitted in the second draft of the pro-
posal:
1) "Increasing the level of technology and of physical pro-
duction and the economy per unit of exploitation, in
association with a reduction in the costs of production,
would result in stability between supply and demand."
2) "Providing more financial resources for increasing pro-
duction and restructuring the market channels would
allow simpler price regulation."
3) "Establishing milk regulations would provide optimum
quality and a price warranted by that quality."








Although all the hypotheses are generally related to the problems
as stated, it is quite obvious that they encompass a much broader
front than we were imagining from the reformulation of the problem
statement. This revelation leads one immediately to the suspicion
that the hypotheses violate the second requisite of hypotheses -
simplicity. The first hypothesis encompasses a fair proportion of all
economic theory and certainly does not clearly specify how one
moves from supply to demand with ease. Any reasonably low-cost
means of testing this hypothesis is hard to imagine. The second and
third hypotheses are apparently related to the problem through the
concern with price fluctuations and, perhaps with the low profits of
the producer. However, the relationship to the problem is only
tenuous at best, and actually introduces new concepts that were not
in the problem statement. Rather than helping to clarify the
research proposal, as they should, these hypotheses tend to add con-
fusion. Clearly, they do not provide direction or guidance for the
research which is one of the primary functions of hypotheses.
Because the author of the hypotheses in the example did not pro-
gress beyond this point, we must now begin to act as if we were the
researcher and formulate the hypotheses according to our
understanding of the problem. In doing so, it may be necessary to
better identify the problem itself (normally done in consultation
with the client) so that the proposal can be improved. Since this oc-
curs many times in the formulation of a good research proposal, we
will not be wasting effort to do it at this point. Few people are
capable of writing an acceptable research proposal on the first at-
tempt. Several modifications usually are necessary as the orienta-
tion and focus become clearer. The final version of the problem
statement in the previous section read as follows:
"The low level of technology on dairy farms contributes to
high costs of production, low average productivity, and a
deficient marketing system for milk. These factors cause
low profits to the producer, price fluctuations for the con-
sumer, and a deterioration in the balance of payments
because of the necessity to import milk."
Let us assume that we were correct in deciding that the orienta-
tion of the proposal was more toward production than toward
marketing. Let us also accept the relationships expressed in the
problem statement although many of us may not be comfortable
with some of them. Then, presumably, the focus of the proposed
research project is the low level of technology on dairy farms, this
being the cause of the unfavorable relationships expressed in the
problem statement.
Because the hypotheses must be testable within the resource
limitations, care must be exercised in comparing resource re-
quirements and limitations before continuing with the formulation








of the proposal. Assume that the client in this case is the Planning
Department of the Ministry of Agriculture. In preparing their plans
for the following year, they are considering the long range necessity
of importing milk. They have requested a series of studies to help
them clarify the situation so they can make firmer predictions and
to aid them in establishing domestic policies. They consider that the
country is capable of producing more milk but are not sure why it is
not doing so. One factor that is evident is that there is a low level of
technology used in the dairy industry. In this particular study their
desire is to determine why technology has not improved in recent
years, because general knowledge far exceeds implementation. Pro-
fessionals will be provided to work full time on the project, and the
Ministry wants a preliminary report in four months and a final
report in six. Generally, calculator and computer facilities are
available and it will be possible to hire the services of some inter-
viewers for a short period of time if necessary.
The first hypothesis might well treat the profitability of the new
technology because, if it is not profitable, most assuredly the pro-
ducers will not adopt it. For a beginning, let us use:
1) "There exist in the country improved methods of dairy
production which, if used by producers, would increase
their profits."
Stated in this fashion the hypothesis does indicate the possible
direction which the research might take. In verifying or rejecting it,
one method might be a simple review of literature to determine if we
can satisfy ourselves as to the existence of profitable new
technologies. Perhaps the technologies which are available have
never been subjected to tests of profitability, which would indicate
the need for some partial budgeting or possibly linear programming
for a series of typical farm resource situations. Of course it might
also be true that the client is satisfied that there are profitable new
technologies and therefore does not desire or require verification of
this hypothesis. Assuming that we will need to include this
hypothesis in the project, further clarification will be made in the
statement of the objectives and the procedures.
The hypothesis can be stated in an if-then form, "If farmers adopt
presently known technology then their profits will increase," and
the relationship to the problem statement is clear. Hence, it
satisfies the first requisite of hypotheses.
Depending on the other hypotheses which are derived, all of the
previously discussed means of verification or rejection of the
hypothesis could be accomplished under the limitations of the
budget which we have described.
The theoretical implications of the hypothesis are about as simple
as it is possible to make them and still retain meaningful relation-








ships to the problem. To be profitable, the new technologies must fit
within the resource restrictions of the farmer and must be profitable
given present or projected price situations. Notice that it is not ade-
quate, even though it is simpler, to hypothesize only that the new
technologies could increase farmers' production.
The last criterion of hypotheses applies to all of them taken
together, so it cannot be applied individually except in the sense
that it would not be adequate to consider only an increase in produc-
tion without considering, at the same time, the profitability of the
practice.
Assuming that in the course of the research we will be able to ac-
cept the first hypothesis, we must then consider additional
hypotheses because the first, alone, is not adequate to answer the
question put to us by the client. The second hypothesis might be
that,
2) "Farmers are not aware of the new technologies."
As stated this is a known fact, but the if-then implication is that if
farmers are not aware of profitable alternatives then they will not
(or cannot) adopt them. The direction of the research in this case is
also indicated. A sample survey of farmers should be able to provide
the evidence necessary to accept or reject the hypothesis.
Another hypothesis could treat credit, or the related financial
situation of the farmers.
3) "If special credit sources are not made available, farmers
will not be able to adopt the new technologies."
This can be considered a broad hypothesis with two sub-hypothesis
which will be tested,
a) "Farmers are unable to adopt new technologies
because internal financial resources are limited."
and
b) "Farmers are unable to obtain credit, which limits
their ability to finance changes in their production
techniques."
An additional hypothesis could treat the problem of price in-
stability from the point of view of the producer.
4) "A milk price stabilization program could induce farmers
to adopt improved methods of production."
This hypothesis is not quite as straightforward as the others. To be
able to verify or reject it empirically, a great deal of time and money
would be involved certainly more of both than we have available
within our research budget. However, attitudes of farmers toward a
price program could be ascertained and certain conclusions drawn
regarding the probable outcome of such a program. Again, if this
hypothesis is included, the precise nature of the research process
will need to be specified in the objectives and the procedures.








Other possible hypotheses could be suggested, but those pro-
posed above are adequate for present purposes. Probably, all can be
accomplished within the research budget, but it will be necessary to
clarify them further (and perhaps modify them) as we develop our
statement of objectives and procedures.
In summary, our hypotheses are the following:
1) "There exist in the country improved methods of dairy
production which, if used by producers, would increase
their profits.
2) Farmers have not adopted the new methods because they
are unaware of their existence.
3) Special credit sources are necessary if dairy farmers are
to adopt improved methods of milk production.
a) Dairy farmers are unable to adopt new technologies
due to internal financial resource limitations.
b) Dairy farmers are unable to obtain credit, which
limits their ability to finance changes in production
methods.
4) A milk price stabilization program could induce farmers
to adopt improved methods of production."
Are these hypotheses, taken together, adequate and efficient in
suggesting a means to one or more meaningful solutions to the
problem? If they cover the range of possibilities open to the govern-
ment (extension programs, credit programs, and/or price programs,
for example) they should be adequate in suggesting guidelines to
one or more meaningful solutions. They are efficient if we cannot
contrive other hypotheses which could provide solutions to the
problem with the use of fewer of our research resources, do so in less
time, or result in more precise information within the research
budget.

Delineation of the Objectives
Objectives usually are expressed in lay terminology and are
directed as much to the client as to the researcher. The primary ob-
jective of applied research will be either 1) to suggest or recommend
to the client practical means of problem resolution, or 2) to provide
information to clarify an unknown situation. Generally, the objec-
tives taken as a group will 1) define the limits of the research project
for the researcher, 2) clarify the means of conducting the research, 3)
identify the client or clients, and 4) describe the expected product of
the research for the client.
The objectives link the theoretical relationships presented in the
hypotheses to the analytical and methodological orientation
necessary for conducting the research. An objective specifies what
the researcher intends to do or find in the project and suggests one








or more research procedures to be used. Later in the research pro-
posal the specific procedures must be defined. These procedures of
course, must be appropriate, not only to the stated objectives but
also to the resource availability as discussed in Chapter II. It is
necessary, therefore, that the objectives be sufficiently broad to
satisfy the needs of the client but also sufficiently specific to con-
form to budgetary restrictions.
An erroneous idea of the nature of the objectives of a research pro-
ject should be clarified. Research objectives are neither political ob-
jectives nor are they objectives of an action program of the govern-
ment. Objectives of a research project suggest what information
will be obtained for the client to help resolve the problem which in-
itiated the research. This information, in turn, can be used by the
client to establish an action program. For example, "to redistribute
land in Mexico" suggests a government action program. This can-
not be an objective of a research project. But, "to study the past
and present agrarian reform programs in Mexico and their effects
on land redistribution" is a legitimate research objective related to
a felt need concerning land use and productivity. In the terms of our
dairy problem, "to establish a credit program for dairy farmers" is
not a legitimate research objective, though it may be a feasible solu-
tion in the mind of the researcher and the client. "To determine if
dairy farmers are able to obtain credit for improving methods of
production," or "to recommend methods of resolving credit defi-
ciencies if identified," on the other hand, are acceptable research ob-
jectives.
Given these conditions, the primary objective of the dairy study
which we are proposing could be, "to determine the obstacles to the
adoption and optimal use of new technology by dairy farmers." This
objective precisely describes the purposes of the project. Ac-
complishing this objective will provide information to clarify an
unknown situation for the client, and, if we are successful, should
provide him with the information he needs to make policy decisions
and predictions concerning future milk supplies.
Secondary objectives can include the following, listed in order of
their relationship to the hypotheses:
1) Determine if presently known modern technology is
profitable to the dairy farmers given their present
resource situation and market outlet.
2) Determine if the Extension Service is effectively pro-
viding necessary information to farmers concerning
possible alternatives for production.
3) Determine if the required changes to adopt new
technologies on farms are outside the financial means of
the farmers.







4) Ascertain whether present credit sources are adequate in
providing for the farmers' needs related to new practices.
5) Obtain farmers' opinions about a price stabilization pro-
gram and their possible reaction to it with respect to
changes in use of technology and concurrent production
practices.
Notice that each of these objectives is directly related to a
hypothesis and help to clarify the direction of the research. If the
client does not understand the terminology used in the hypotheses,
he should be able to understand the nature of the research from
reading the objectives. Also, the objectives clarify the means of con-
ducting the research. It is clear that interviews with dairy farmers
will be necessary and, in general, what the content of these inter-
views will be. Specific questions must be written from the guidelines
given in the objectives. These questions either singly or as groups
will provide the information to test the hypotheses.
Of special interest is the fifth objective. The related hypothesis (a
milk price stabilization program could induce farmers to adopt im-
proved methods of production) states a positive relationship
without giving any indication of the nature of the test which the
researcher has in mind. The client in such a case could well for-
mulate an erroneous impression of the nature of the results which
the researcher proposes to present him. The fifth objective
specifically states that the researcher expects no more than to ob-
tain the farmers' opinions and possible reactions. This is clearly dif-
ferent from empirical evidence which the client might otherwise ex-
pect if he only had the hypothesis for information.
We have been talking about a special or explicit client but other
clientele could also benefit from the research in question. If the
researcher is hired specifically by a client, and the client considers
the research private, then, of course, he is the only one who will
directly benefit from the work. However, as is more often the case,
this type of research is undertaken by a public or semi-public entity
so the research becomes public property and the identification of a
broader audience is useful. In the context of the present problem,
the full clientele could be identified in a sixth objective:
6) Provide information to farmers about the profitability of
new methods of production, to bankers and other credit
institutions of possible sources of new business, and to
government planners to aid them in making decisions
concerning the future of the dairy industry.

Summary
Three of the most important and critical aspects of the planning
of a research proposal problems, hypotheses, and objec-








tives have been presented and discussed. These three parts are
not independent from each other, nor are they independent from
other portions of the proposal and aspects of conducting the
research to be discussed in following chapters. They serve as a
framework for developing the data collection and analytical pro-
cedures, the budget including time sequences and the publication
plans for the research results.
Time spent in careful development of the problem statement, the
hypotheses, and the objectives is the key to efficient research and
can well be the most productive use of time by the researcher. Even
in cases where the researcher may have only one month, one week,
or perhaps just one day to provide an answer, the time spent in this
phase of the research is critical to the success of the undertaking.
On many occasions when a person is given a rush task, the tendency
is to "come up with something." Little time is devoted to analyzing
the situation to determine precisely what the client wants, what is
really needed, and what resources are available to accomplish the
necessary task. Too often the results have no value because the
"something" which the researcher "comes up with" is not really
related to the problem of the client.
Problems appropriately specified for applied research have the
following characteristics:
1) They are based on felt needs of individuals, groups, and
societies;
2) The causal relationships expressed in a problem state-
ment are not hypothetical and are relevant to the
problem;
3) Problem statements must suggest testable hypothetical
relationships that, when analyzed, yield relevant and non-
trivial results;
4) The problem and the research to resolve the problem
must be relevant and manageable within resource restric-
tions.
Researchable problems can be distinguished from problematic
situations in that numerous researchable problems can be for-
mulated from a problematic situation.
The hypotheses serve as guides to executing the research.
Hypotheses must:
1) Be stated to provide direction for the research;
2) Be formulated as causal relationships with if-then im-
plications;
3) Be capable of tests within the limits of the research
resources;
4) Be stated as simply as possible; and







5) As a group be adequate and efficient in suggesting means
to one or more meaningful solutions to the problem.
Objectives in general describe what is expected to be achieved by
the project. Specifically, objectives:
1) Define the limits of the research project;
2) Suggest or clarify the means of conducting research;
3) Describe the nature of the potential research product to
the client; and
4) Identify the client or clients.
The following problem statement, hypotheses and objectives are
those of the example discussed in the chapter.4

Problem:

The low level of technology on dairy farms contributes to high
costs of production, low average productivity, and a deficient
marketing system for milk. These factors cause low profits to the
producer, price fluctuations for the consumer, and a deterioration in
the balance of payments because of the necessity to import milk.

Hypotheses:

1) There exist in the country improved methods of dairy
production which, if used by the producers, would in-
crease their profits.
2) Farmers have not adopted new methods because they are
unaware of their existence.
3) Special credit sources are necessary if farmers are to
adopt improved methods of production.
a) Farmers are unable to adopt new technologies due
to internal financial resource limitations.
b) Farmers are unable to obtain credit, which limits
their ability to finance changes in production
methods.
4) A milk price stabilization program could induce farmers
to adopt improved methods of production.

Objectives:
1) To determine the obstacles to the adoption of new
technology by dairy farmers.
2) Determine if presently known modern technology is
profitable to the dairy farmers given their present
resource situation and market outlook.

4See the appendix for an example of a complete project statement.








3) Determine if the Extension Service is effectively pro-
viding necessary information to farmers concerning
possible alternatives for production.
4) Determine if the required changes to adopt new
technologies on farms are outside the financial means of
the farmers.
5) Ascertain whether present credit sources are adequate in
providing for the farmers' needs related to new practices.
6) Obtain farmers' opinions about a price stabilization pro-
gram and their possible reaction to it with respect to
changes in use of technology and concurrent production.
The chapters to follow will continue with discussions of data
sources and collection, and analysis and presentation of research
results, all of which depend upon well specified problem statements,
hypotheses and objectives.









PART TWO

CONDUCTING
APPLIED RESEARCH


















CHAPTER IV


EXPERIMENTAL DATA COLLECTION

The execution of research involves the collection of data which
pertain to the project, the utilization of the data to test the
hypotheses (usually called analysis of the data), reaching conclu-
sions which are useful in the resolution of the problem which in-
itiated the research, and making appropriate recommendations to
the client. If the project has been properly planned, the type of data
which are needed have been determined prior to conducting the
research. The researcher will know if only secondary information
will be used or if primary data collection will be necessary. The plan-
ning phase of the project will also determine if experimentation is
necessary or if the source of data will be non-experimental. If ex-
perimentation is to be used, a great deal of care must be exercised in
experimental design.
Experimentation has been the principle basis for obtaining scien-
tific information and will continue to remain of paramount impor-
tance. The use of non-experimental data, however, is becoming more
prominent, due to improvements in measurement techniques and
development of more adequate data series. The advantage of ex-
perimental data over non-experimental data is basically the degree
of control which the researcher is able to exert over the variables in-
cluded in the study. In most experiments, with the use of the proper
design the researcher can select the factors which will vary, the
levels at which they will be included in the experiment, and the pat-
tern in which they will be used. By using appropriate equipment he
can also usually obtain quite accurate measures of the input
variables as well as of the results of the experiment. With non-
experimental data, the levels and combinations of variables are
predetermined by nature or society, so the researcher must measure
and use them as they exist. Because these variables are very dif-
ficult to identify and measure, non-experimental data are usually
more subject to inaccuracies than are experimental data.








Experimental Design
Experimental design, the form in which the experiment is to be
set up, is a critical factor in the generation of experimental data.1
There are any number of designs which can serve a particular need,
but there will usually be one that is better suited to the conditions
under which one experiment is going to be conducted and which is
the most efficient in the use of the research resources. Selection of
the most efficient experimental design will provide more informa-
tion directly related to problem resolution for the given set of
research resources than any other alternative design.
Selection of the number of treatments, the number of controlled
and measured factors, the levels at which the variables are to be in-
cluded, and the number of replications to be used in the experiment
is not a simple process. The final choice can be complicated in ap-
plied research because the conditions under which the researcher is
working are often poor in relation to what he would like for im-
proved precision. Good research planning is extremely helpful to the
researcher in choosing an experimental design. This is so because
the design must be related to the problem and the methods of
analysis to be used and these should have been carefully considered
in the planning phase of the project.
In the choice of a design, then, the researcher will be able to an-
ticipate the type of information which will be forthcoming from any
particular design. He will be able to predetermine the applicability
of the design to the problem and to the methods of analysis which
are appropriate. The researcher will also ascertain whether or not
the experiment can be conducted within his resource limitations.



Relationship to the Problem
Although it is possible to achieve measurement accuracy with ex-
perimentation, it does not necessarily follow that experimental data
as a source of information for a research project will assure research
precision. If the design of the experiment is not properly related to
the problem orientation of the project it will not be possible to
achieve precision in determining relationships useful to problem
resolution. An example may best serve to illustrate the point.
A common type of design in fertilizer experimentation is the
following, with each treatment being replicated a number of times
(frequently four):

'Selected references on experimental design include [3, 4, 7, 26].








Experimental Design
Experimental design, the form in which the experiment is to be
set up, is a critical factor in the generation of experimental data.1
There are any number of designs which can serve a particular need,
but there will usually be one that is better suited to the conditions
under which one experiment is going to be conducted and which is
the most efficient in the use of the research resources. Selection of
the most efficient experimental design will provide more informa-
tion directly related to problem resolution for the given set of
research resources than any other alternative design.
Selection of the number of treatments, the number of controlled
and measured factors, the levels at which the variables are to be in-
cluded, and the number of replications to be used in the experiment
is not a simple process. The final choice can be complicated in ap-
plied research because the conditions under which the researcher is
working are often poor in relation to what he would like for im-
proved precision. Good research planning is extremely helpful to the
researcher in choosing an experimental design. This is so because
the design must be related to the problem and the methods of
analysis to be used and these should have been carefully considered
in the planning phase of the project.
In the choice of a design, then, the researcher will be able to an-
ticipate the type of information which will be forthcoming from any
particular design. He will be able to predetermine the applicability
of the design to the problem and to the methods of analysis which
are appropriate. The researcher will also ascertain whether or not
the experiment can be conducted within his resource limitations.



Relationship to the Problem
Although it is possible to achieve measurement accuracy with ex-
perimentation, it does not necessarily follow that experimental data
as a source of information for a research project will assure research
precision. If the design of the experiment is not properly related to
the problem orientation of the project it will not be possible to
achieve precision in determining relationships useful to problem
resolution. An example may best serve to illustrate the point.
A common type of design in fertilizer experimentation is the
following, with each treatment being replicated a number of times
(frequently four):

'Selected references on experimental design include [3, 4, 7, 26].








Design 1
Variables
Treatment Number N P K
1 0 0 0
2 0 0 1
3 1 0 1
4 2 0 1
5 0 1 1
6 1 1 1
7 2 1 1
8 1 1 0

In the project proposal an objective might read something like,
"to determine the effect of nitrogen (N), phosphorus (P) and
potassium (K) on the production of pangola grass in the Cauca
Valley." As we now know, of course, part of the difficulty here is
that we do not know what the identified research problem is. But
that aside, the objective as stated is vague and is not an adequate
guide for designing the experiment. Toward what end is the experi-
ment oriented? Does the researcher want to know simply, "is there a
response to N, P, and K?" or is he more interested in the
magnitude of the response, or even the kind or form of response over
a range of N, P, and K in various combinations?
Each of these questions may well require the use of a different ex-
perimental design, number of treatments or number of replications
in order to achieve precision and efficiency in arriving at an answer.
In some cases, two or more questions can be answered efficiently
from one experiment, but in others, the attempt to answer too many
questions from a single design may render the experiment useless
for answering any question.
Without discussing the theoretical logic for the statements, the
following can be said about the relationship of Design I to some of
the questions which could be asked. When the researcher is in-
terested in knowing only if there is a response to each of three
nutrients, it is not necessary to include all the treatments and levels
of N in the design. A simpler design such as the following could be
used:

Design II
Variables
Treatment Number N P K
1 0 0 0
2 1 0 0
3 0 1 0
4 0 0 1










Here, instead of eight treatments there are only four. Using four
replications of each treatment, only 16 separate plots are required
rather than 32 as would be necessary in the first design if four
replications were also used. The choice of the level of each nutrient
(1 = 100 kg/ha or 1 = 200 kg/ha, etc.) is important because this
design provides information only for the one level chosen.
If the researcher wanted to know the magnitude of the response
for two different positive levels of N, it would be necessary to in-
clude the three different treatments (0, 1, 2) of this nutrient. Ap-
parently in Design I it was desired to know something about the
magnitude of response to N for two different levels of P, because the
N treatments are repeated for 0 and for 1 P. This question can be
answered with the use of treatments 2 through 7 of Design I.
The last treatment of Design I can only answer two questions, 1)
"Is there an effect from K when N = 1 and P = 1?" This requires
treatments 6 and 8. No other information will be available on the ef-
fect of K. The last treatment along with the control (treatment 1)
can show the response of N and P together when No K is applied.
Because we are interested in applied research, let's suppose that
the ultimate use of the data from the proposed experiment will be to
make fertilizer recommendations to farmers. To what extent does
Design I permit us to make the appropriate analyses? One alter-
native is to compare the cost and return of each treatment with the
control (0-0-0) and determine the net return from each. Assuming
that there were significant differences between treatments, we
could then recommend the treatment with the highest net return.
Another alternative would be to determine the functional relation-
ship between N and production (production function for N) for each
of the two levels of P. Assuming the curves thus generated were of
proper form (satisfying the conditions of optimality), we could
determine the economically optimum quantity of N for each level of
P, make a cost-return estimate for each, and choose the one with the
higher net return as that which we would recommend.
The second procedure would probably result in a somewhat dif-
ferent recommendation than the first method. There is obviously
less precision with respect to N in the first method than in the
second but there may be even less precision with respect to P because
we have only two treatments to choose from. Apparently, the
design is not well suited to answer the question the farmer might
ask, "What is the best combination of N, P, and K to use?" even
though it does appear to be at least reasonably suitable for answer-
ing some other questions.
To answer the farmer's question may require a more complicated
design and could require more resources than are available, but
much more precision is possible if there are sufficient resources. As










an example, a complete factorial with 3 or 4 levels of each nutrient
would require 27 or 64 plots respectively for each replication, but
could provide rather precise answers for the farmer (the use of the 43
factorial, of course, would provide more precision than would the 33
factorial but it also requires more resources). Another, more effi-
cient design is the rotatable central composite which is a modified
factorial that requires only 15 plots for each replication when three
nutrients are considered. For this last design, fewer replications are
necessary so that with only about 40 plots a complete experiment
can be conducted,2 providing a wide range of information.
In summary, the problem toward which the research is directed
has a strong bearing on the type of experimental design which
should be chosen if the researcher is going to use experimentation in
the execution of the project. Should the magnitude of the problem
require a very detailed experimental design, the researcher should
be encouraged to consult with a competent statistician when one is
available. Also, if the researcher is going to use secondary ex-
perimental data, the design which was used in the experiment
should be considered when choosing between various sources. More
will be said on this aspect in a later section of this chapter.
Relationship to Resources
The ultimate size and complexity of an experiment depends on
many factors including number of independent factors to be con-
trolled or measured, analytical techniques to be used, statistical
precision required, quantity and quality of prior information
available, the objectives of the research project, the time within
which results must be obtained, and the resource constraints en-
compassing the research project.
If it is necessary to determine the best combination of N, P, and
K, a rather complex design with a relatively large number of ex-
perimental observations (plots) is required. But if it is simply not
possible to conduct such a complex experiment, the research project
must be modified. For example, if some information is available
which indicates little or no response to K, then the number of in-
dependent variables could be reduced to two (N and P) and K could
be excluded or held constant at some predetermined level in the ex-
periment. The best combination of N and P can then be determined
with a rotatable central composite design requiring only about 26
experimental units rather than the 40 units required for three in-
dependent factors (N, P, and K).
The design can be simplified further by considering only one in-
dependent factor. This factor could be the one considered most im-

2All treatments are not replicated the same number of times [55].








portant such as perhaps N or it could be some fixed combination of
several factors such as a complete fertilizer containing N, P, and K
with an analysis of 10-30-10, for example.
An experiment requiring only about 15 experimental units will
yield as much statistical precision for one factor as 26 for two fac-
tors. But it is important to realize that the total amount of informa-
tion is reduced accordingly as the number of independent factors
and resource requirements are reduced. With three factors and 40
plots, we should be able to tell the farmer, who is on similar soil as
that used for the experiment, how much N, how much P, and how
much K he should use to achieve the greatest net returns from fer-
tilizer use. With two independent or variable factors, we have the
option of telling the farmer the best combination of only two
nutrients and can do that only for some predetermined quantity of
the third nutrient. With only one variable factor it is not possible to
recommend best combinations but only the best quantity of that
single factor. We may be able to recommend 300 kilos per hectare of
10-30-10 as the best quantity of that complete fertilizer, but we
would not be at all confident that the resulting 30 kilos of N, 90 of
P,05, and 30 of K20 would be the best combination of the three
nutrients to use.
The effect on resource requirements of the analytical technique to
be used is more complex, but an example will serve to illustrate the
point. Suppose that it is desired to determine if there are significant
differences in the response of three different levels of a new feed ad-
ditive on fattening steers. A probable design would have three
treatments with the additive (one for each level to be tested) and a
control. This results in four treatments per replication. Under most
conditions three to four replications have been found to be required
to obtain reliable statistical estimates and provide enough degrees
of freedom for this type experiment and the analysis of variance
which would be used. A total of 12 to 16 experimental units (pens of
cattle) would be required to execute the research.
An alternative to analysis of variance is regression analysis. If
this technique is envisioned for the feed additive experiment the
same four treatments could be used and if the interval between
treatments was not uniform, they could be adjusted to make them
equal (a desirable though not necessary attribute of a design for
regression analysis). In analysis of variance, the observations from
only two treatments are compared at any one time. In regression
analysis the observations from all the treatments are considered
simultaneously. For this reason roughly the same amount of
statistical precision can be obtained with two replications of the four
treatments for regression analysis as with four replications of the








four treatments for analysis of variance. Hence, regression analysis
may require fewer experimental resources than analysis of variance.
Similar relationships hold for other types of analysis which may be
required in the project and should be considered when choosing the
design to be used.
The statistical precision required in experimental results is
associated closely with the nature of the problem for which the
research is being undertaken. Lower levels of confidence are accep-
table in research dealing with animal health than with human
health, for example, and lower confidence levels may be satisfactory
in some social research than in some biological research. But
regardless of the nature of the research, an increase in the precision
required is almost always associated with the need for more ex-
perimental resources. For any given research objective or ex-
perimental design, more replications and/or more treatments will
usually result in more precise statistical estimates than would fewer
treatments or replications.
In addition to increasing the number of treatments or replica-
tions, precision can also be increased by closer supervision and bet-
ter control during the experimental process. Spotty application of
fertilizer by hand broadcasting on grass plots can result in large ex-
perimental errors as can carelessness and lack of thoroughness dur-
ing harvest. More time, workers with more skill, or specialized
machines or equipment can reduce this source of experimental error,
but all will add to the requirements for experimental resources.
From a practical point of view, the least expensive means of reduc-
ing experimental error from this source is closer supervision by the
researcher during all phases of the experimental process. Time
spent at the experimental site by the researcher can be highly pro-
ductive particularly when it is possible to prevent the complete loss
of data through carelessness.
The use of prior information can reduce the need for resources by
reducing the range and number of treatments otherwise required,
by taking advantage of known estimates of variance to minimize
the number of replications, and perhaps even by providing all the
data needed making further experimentation unnecessary. In the
following section we will discuss in more detail the use and utility of
secondary experimental data, which is one source of prior informa-
tion available to the researcher.
The time limit within which a decision must be made is also an im-
portant factor in determining the size and complexity of an experi-
ment. If time is not a critical factor, an exploratory experiment of
relatively simple design can be conducted and this can be followed
by an experiment which is more exact in its focus. The range over
which the experiment is conducted can be successively narrowed un-








til the required accuracy is achieved. None of the experiments in
this sequence need be large nor complex. But if timing is critical and
decisions must be made rapidly, it may be necessary to increase the
size and complexity of the experiment in order to include several
links of the chain all at the same time. That is, to conserve on time
(in this case a very limited resource) other available resources may
need to be substituted in order to achieve a useful research product
within the time period allowed for making a decision.

Secondary Experimental Data
When experimentation is undertaken in the execution of the
research project, the experimental design can and should be tailored
to the needs of the project. The data obtained will then be the best
available and will be well suited to the needs of the researcher.
However, it is not always necessary to conduct an experiment to
have suitable or adaptable experimental data for analysis, because
in most places where applied research is being conducted, at least
some prior experimental data are available. These data, though
generated for other research purposes, frequently provide an insight
into the nature of the relationships to be studied in the current pro-
ject, and may provide sufficient data so that additional experimen-
tation need not be undertaken.
In situations where time is a limiting factor, the use of secondary
experimental data may be advantageous or even essential, but data
generated by another person or from one or more other experiments
must be used with caution to assure that they are relevant and com-
parable. Some adjustment or selection may well be necessary to
adapt the data to the current study.
Several means can be used to adapt, select, or adjust data of this
nature. One means is to select only the relevant portion of the data
and exclude those parts not related to the current analysis. An ex-
ample would be to select data in a fertilizer experiment from those
parcels where potash was at a constant level, eliminate plots where
trace elements were included, and make the analysis for the portion
of the data in which only nitrogen and phosphorus were variable.
A second method of using secondary data is to analyze the results
of several experiments and search for consistencies which may in-
dicate relationships not otherwise evident. In a series of corn fer-
tilization trials in the Cauca Valley, Colombia, production functions
resulted, individually, in very poor statistical estimates. For any
one trial, little confidence could be placed in the conclusions. But
after dividing the trials into soil types, it was noted that there was a
great deal of consistency in the functions within a soil type. The
form of the curves was similar with each reaching a maximum








within a short range, and with calculated optimum applications at
about the same levels. Hence, by using information from all the
curves, together, general recommendations could be made even
though the individual analyses yielded little information.
A third useful method of analysis is to consider the possibility of
combinations of data from two or more different experiments which
were conducted under similar conditions. This method was used to
estimate the optimum stocking rate for fattening steers on grass
from three different experiments [50]. Each experiment was con-
ducted to determine the effect of hormones, and all were conducted
on similar grasses, under similar conditions and with comparable
cattle. Because the rate of stocking was different in each case, it was
possible to use a mathematical response relationship from which the
optimum stocking rate could be calculated.
One additional point with respect to secondary data should be
mentioned. When one has spent time attempting to use secondary
experimental data, he appreciates the productivity of any efforts
made by the first researcher to preserve the data for other users.
Nothing is more frustrating than to discover a description of an ex-
periment that should have provided usable information and then to
find the data in such poor shape that they are impossible to use.
Simple notations such as units of measurement are often not even
included. The thoughtful researcher will leave a clear record of his
data so that users who follow will have no trouble interpreting
them.

Multi-purpose Experimentation
As a practical matter, a great deal of experimentation is carried
out with an orientation that is only partly research centered. An im-
portant example in agriculture is the demonstration trial usually
conducted by, or in cooperation with, the extension service. One of
the purposes of this type of research is to demonstrate the results of
research under real conditions, i.e., under conditions which will be
applied by the client toward whom it is focused. For this reason
rather poor experimental control is to be expected, and accordingly,
the experimental design is nearly always quite simple. In many
cases, as few as two treatments, with and without a particular input
or package of inputs, are included. At times, a complete experiment
is conducted at one location; in other trials, different locations are
considered to be different replications of the same experiment with
all treatments being repeated at each; and in some cases only one or
a few treatments are conducted at each location.
It should be obvious that as more and more locations are included
in a demonstration trial, exposure to the clients is increased, but ex-








perimental control is decreased. Also, with more treatments in a
project, more information is possible, but also, the supervision of
the project becomes more difficult. Hence, the persons responsible
for the project must determine, based on the orientation of the pro-
ject and the available resources, what the size and scope should be.
Presenting an example of a fairly successful demonstration trial
may be the best means of discussing some of the more important
aspects to be considered when inititating a project of this nature.
This project was conducted in southwestern Colombia in an area not
far from the city of Cali. The area comprised that of a large number
of descendants of former slaves who had divided their holdings into
an average of about 2 hectares per family. The farms were nearly all
in old stands of cocoa, coffee, and plantain which were in such poor
condition that monthly income per farm barely reached fifteen
dollars. General nutritional deficiency was evident, as was the near-
ly complete absence of any source of animal protein. Physical condi-
tions for any type of livestock were very bad, and, of course, little of
no money was available for supplemental feed.
As part of a development program for this area, it was thought
that Khaki-Campbell ducks (an egg laying breed) could prove to be a
potential source of animal protein to supplement the diets of these
poor rural families. In designing the project, several alternatives
were possible. The simplest of those considered reasonable was to
select a small number of families (those most apt to be conscientious
in maintaining the necessary records), to give each about 10 ducks
and provide them wtih a recommended concentrate ration. The
results could probably be established with a fairly high level of con-
fidence and would show whether or not the ducks could survive
under the conditions of the area. It would also be possible to deter-
mine the cost of the eggs to the families and whether or not they
would eat the eggs.
A second approach that was considered was to use more families
and experiment with several rations for the ducks. At first, three ra-
tions were considered adequate, with three families provided with
each ration. In this manner, it was hoped to be able to determine not
only if the ducks would survive and lay under the severe conditions
prevalent in the project area, but also what the lowest cost ration
would be. In other words, there would be three different rations to
choose from. Although more information could be obtained, more
families would be needed and more supervision required to assure
that the rations were correctly administered. The extension person-
nel who were cooperating in the project felt they could find nine
families and had sufficient resources to do the majority of the super-
vision. Over 100 young female ducks were going to be available, so
the extension people began to locate the cooperating families.








As it turned out, 16 families were eager to cooperate in the pro-
ject, and the extension people felt this number should not be too
great a supervisory burden. The use of this many families would
reduce the number of ducks available per family to seven. It was
decided that this would be satisfactory, but that it would be more ef-
ficient in the use of the families to include four rations and have four
replications of each. With four rations it was hoped that there would
be sufficient information to estimate a production function for eggs
and with this information be able to determine the lowest cost ra-
tion even if it were not one included in the experiment.
The final design included one group with a complete concentrate
ration, one with % of this amount, one with /3 of a ration, and a con-
trol group with no concentrate. Except for those receiving a full ra-
tion, all could receive whatever scraps were available and be allowed
to graze (or be fed chopped grass). Material for constructing ade-
quate housing for the ducks was available locally at no cost and all
families were required to be ready to receive the ducks by a fixed
date.
A one day short course was held with all families participating
sufficiently far enough in advance of the delivery date to allow time
for construction of the housing. The care of the ducks was discussed
and also the design of the experiment and its purpose. The families
were all given very simple record forms and instructed in their use
(to be able to use the forms it was not necessary to be able to read or
write or even count). Cups were provided to measure the exact
amount of feed for each duck for each ration so that in the event of
losses, the adjustment of the ration would be simple. At the close of
the course a meal including duck eggs was served to assure the
families that the eggs were good to eat since there was some local
bias against eating duck eggs.
With the encouragement of the extension agent, all participating
families were ready on the date that the twelve week old ducks were
delivered. Inititally, extension field men and the researchers
checked frequently with the families to ascertain if everything was
being done properly. The records were collected every two weeks
during the whole project to assure they were kept current.
To be sure, there were rather large differences in the results for
each ration. One family, for example, never was rewarded with even
one egg. Although they were a bit embarrassed and the neighbors
were sure they were not taking good care of their ducks, they took it
good naturedly because they were assured that this was not unex-
pected and was a necessary part of the experiment. Nevertheless, by
having four replications and using the average for each ration, ade-
quate information was obtained. It was possible to determine the
best level of concentrate to use if the eggs were going to be sold, as








well as the ration which resulted in the lowest cost for the eggs if
they were to be used to supplement the family diet. The second use,
of course, was the primary purpose of the project.
Upon completing the analysis of the results, a field day was held
for the families at which time the results were presented. After the
general results were shown, those of each family were discussed in
an attempt to determine why some had better results (hence lower
costs) than others with the same ration. Following this discussion,
all the families participated in the the selection of the final recom-
mendations.
In summary, this project was tailored to fit within the resources
available and yet to provide the greatest exposure in the project
area. Although experimental control was relatively low, sufficient
treatments and replications were included to maintain adequate
statistical reliability in part due to the presence of a high degree of
supervision by the researchers.

Multidisciplinary Experimentation
Multidisciplinary experimentation is another means of conserv-
ing scarce research resources. When researchers from two or more
disciplines cooperate in a project it is frequently possible to obtain
answers for each with little change in the basic experimental
design.3 Too often a researcher in one discipline minimizes the ef-
fects of those factors commonly included by others, so that in the
absence of cooperation, the product is of low value to other re-
searchers. An example occurs in beef or dairy projects where the re-
searchers raise their own corn for silage without the cooperation of
the corn researchers who may be at the same experiment station.
The animal researchers may be able to document accurately the ef-
fect of the silage on the animals but be unable to estimate animal
production per hectare of corn because they had little interest in the
corn production. Even more difficulties will arise if it is later desired
to make an economic evaluation of feeding silage to animals [46].
Of course, it is not always possible to obtain answers for more
than one discipline without increasing the size and the complexity
of the experiment beyond manageability and available resources.
But it is precisely in those cases when resources are most scarce
that it is important to consider the advantages of multidisciplinary
research. Most experiments are costly both in terms of time and of
other resources so it is obvious that if the results can serve two or
three researchers, the additional effort required in planning the ex-
periment can be extremely valuable [60].
'For a brief and excellent discussion of multidisciplinary cooperation in extending
experimental results to practice see [67].








Summary
Experimentation and experimental design usually are associated
with objectivity, precision, and scientific purity concepts that
imply rigidity and inflexibility in thought and procedure. In basic
research this is mostly true, but in applied research, considerations
other than pure scientific objectivity can become more important in
determining the type of design to be used for any given experiment.
Resource limitations will usually force a reduction in the accuracy
obtainable, and factors such as demonstration uses will modify the
nature of the design ultimately selected. It is important that the ap-
plied researcher maintain a flexible attitude with respect to ex-
perimentation and the experimental design in order to increase his
effectiveness and make him more efficient in his work.








CHAPTER V


NON-EXPERIMENTAL DATA COLLECTION
A major difference between experimental and non-experimental
research is the degree of control the researcher exercises over the
variables being studied or measured. In an experiment, the re-
searcher controls the design and levels of certain variables and the
measurement of phenomena resulting from the experiment. In non-
experimental research, the researcher in most instances cannot
determine the design and level of the variables nor directly measure
the phenomena, but controls only the technique used in measure-
ment (primarily a sample survey and questionnaire). For non-
experimental observations sample survey design plays a role com-
parable to that of experimental design when experiments provide
the observations to be analyzed. Again, a competent statistician, if
available, can be a helpful consultant.
The non-experimental researcher relies upon interviews and ques-
tionnaires to communicate his measurement need to a respondent.
Often it is the respondent, not the interviewer, who performs the
measurement based upon his experience. The respondent's ex-
perience may be compiled, for example, in farm records or consumer
budgets, but frequently his responses are based upon a subjective
evaluation of the phenomenon as he best remembers it.
Thus, in non-experimental research the researcher usually con-
trols only the general levels of variables, through stratification and
sample selection of respondents, questionnaire development and in-
terview training. Successful non-experimental measurement rests
primarily upon all of these activities. But even with a good sample,
a tested questionnaire, and a well trained interviewer, the re-
searcher cannot completely control interviewer-respondent com-
munication, part of which may be misleading [16].
This chapter will first focus on the selection of respondents and
the design and implementation of questionnaires and then discuss
verification and preparation of both primary and secondary data for
analysis. No specific emphasis is given to unstructured interviews
and case studies because most of the approaches included are ap-
plicable in these situations. Time and resource restrictions along
with the needs expressed by the hypotheses and objectives will dic-
tate whether the measurement process will be a large survey or a
case study [31].

Selecting Respondents
The selection of the respondents, or sample design, is an extreme-
ly important part of the process of non-experimental data collection.








CHAPTER V


NON-EXPERIMENTAL DATA COLLECTION
A major difference between experimental and non-experimental
research is the degree of control the researcher exercises over the
variables being studied or measured. In an experiment, the re-
searcher controls the design and levels of certain variables and the
measurement of phenomena resulting from the experiment. In non-
experimental research, the researcher in most instances cannot
determine the design and level of the variables nor directly measure
the phenomena, but controls only the technique used in measure-
ment (primarily a sample survey and questionnaire). For non-
experimental observations sample survey design plays a role com-
parable to that of experimental design when experiments provide
the observations to be analyzed. Again, a competent statistician, if
available, can be a helpful consultant.
The non-experimental researcher relies upon interviews and ques-
tionnaires to communicate his measurement need to a respondent.
Often it is the respondent, not the interviewer, who performs the
measurement based upon his experience. The respondent's ex-
perience may be compiled, for example, in farm records or consumer
budgets, but frequently his responses are based upon a subjective
evaluation of the phenomenon as he best remembers it.
Thus, in non-experimental research the researcher usually con-
trols only the general levels of variables, through stratification and
sample selection of respondents, questionnaire development and in-
terview training. Successful non-experimental measurement rests
primarily upon all of these activities. But even with a good sample,
a tested questionnaire, and a well trained interviewer, the re-
searcher cannot completely control interviewer-respondent com-
munication, part of which may be misleading [16].
This chapter will first focus on the selection of respondents and
the design and implementation of questionnaires and then discuss
verification and preparation of both primary and secondary data for
analysis. No specific emphasis is given to unstructured interviews
and case studies because most of the approaches included are ap-
plicable in these situations. Time and resource restrictions along
with the needs expressed by the hypotheses and objectives will dic-
tate whether the measurement process will be a large survey or a
case study [31].

Selecting Respondents
The selection of the respondents, or sample design, is an extreme-
ly important part of the process of non-experimental data collection.







Because the researcher has no control over the distribution of the
factors he is studying (these exist as a result of nature and social
organization), the proper selection of the respondents is necessary
to assure that the information is being obtained from an appropriate
population. Once respondents have been selected and the interview
process is completed, a faulty or ill-designed sampling procedure
may generate data that do not describe the target population. To
resolve this difficulty more time and resources, if available, must be
expended than would have been necessary with a more carefully
designed sample.
Two commonly employed sampling approaches are the random
sample and the stratified sample [8, 18, 20]. For information of a
broad census nature, a completely random sample gives each person
in the total population (all farmers, high school students, married
women, etc.) an equal chance of being chosen. In this manner,
results can be associated with the characteristics of that population.
Stratified samples where particular sub-groups are chosen are used
to assure that each sub-group is equally represented, or to assure
uniformity or representation over specific ranges of certain group
traits (different farm size or different income groups for example).
Such a stratified sample can approximate experimental measure-
ment but with less precision than is normally associated with ex-
perimental research. A stratified sample is often more efficient than
a random sample in use of scarce research resources because the
sample size required may be smaller thus reducing both interview
and data processing expenses.
In drawing a sample, one hopes to have available a reasonably
good listing of the general population including some descriptive
characteristics to aid in stratification. To help determine the sample
size, measures of variance within the population may be calculated
and used along with resource limitations and levels of confidence
desired by the researcher and his client.
Many times in applied research, these general characteristics of
the population are unknown, making sample selection more dif-
ficult. One researcher studying farms in a jungle area was forced to
utilize somewhat unorthodox but otherwise relatively functional
techniques [42]. In one of the three zones of the region to be studied
maps were available showing farm locations. A simple random sam-
ple of farms was easily obtained from these maps. In another zone
'trail maps' were available but the farms were not included. The ap-
proximate number of farms in total was known and the trails and
farms were, in general, uniformly distributed throughout the region.
A complete enumeration to develop the sample frame would have
been difficult and would have required more time than was
available. The researcher chose to send out interviewers by mule
with instructions to obtain an interview at every fifth farm. The







sampling problem was thus handled as the interviews were taken.
In the third zone, maps were extremely poor and little was available
to guide the sampling process. To assure that the entire zone would
be represented, the interviewers were instructed to ride their mules
for one hour and then take an interview at the nearest farm, again
proceeding along the trail. As one might expect, errors did result,
but adjustments in the analysis were based upon general observa-
tions made by the researcher while in the zone.
Had logistical problems not been so extreme, the above researcher
could have used a sequential sampling technique. That is, based
upon a predetermined level of confidence, he could have measured
the variance in selected population characteristics by using the in-
terview data taken daily while in the field. From this calculation he
could then determine when the sample had reached the size needed
by reducing the variance to meet the desired confidence level. When
that level had been achieved he could have moved to another zone.
A further need for flexibility in the sampling process to allow for
obtaining needed information is illustrated by a research project
recently completed in the East Uttar Pradesh of India [58]. Initial
interview attempts with Indian farmers concerning their land
holdings were met with considerable suspicion and apprehension,
possibly because of speculation about land ceiling legislation. In-
dividuals conducting planned interviews in villages encountered
farmers unwilling to provide the desired information. Yet when the
farmers were selected at random while traveling along the roads in
the district they responded readily to questions about village and
other matters related to land ownership. Since personal identity and
specific location of the respondents were not imperative the sam-
pling approach was changed substantially but to the benefit of the
entire research effort.
In summary, a flexible researcher who places major emphasis on
problems and views his sampling techniques as tools and not ends
in themselves will be able to perform applied non-experimental
research under much less than optimum conditions.

Designing the Questionnaire
At the risk of seeming elementary, it is advisable to emphasize
that questionnaire development must be closely tied to the problem,
hypotheses, objectives, analytical techniques, and available
resources for the proposed research. Just as the research proposal or
plan must reflect a relevant problem, and the hypotheses and objec-
tives must pertain directly thereto, the respondents to be inter-
viewed and the questions to be asked must be relevant to the
research proposal. Beyond relevancy, a questionnaire must include
only those questions that are highest in priority for analysis and








testing. A multitude of questions could be "relevant" or "in-
teresting" but only a limited number can be used fully in the project
because of research resource limitations. Interview time, for exam-
ple, represents a costly resource allocation not only for the research
project, but also for the respondent. The researcher must always
remember that excessive and/or irrelevant questions try the respon-
dent's patience, reduce interviewer-respondent rapport, threaten
credibility of the responses, and waste scarce resources of the
research project.
Difficulties in Interpretation and Communication
The method of obtaining information in a questionnaire is the
spoken word, so language problems must be given special con-
sideration. Obvious problems arise when questions are translated
from one language to another, but even within the same language,
word and expression usage vary with different cultural, social,
educational, and economic classes. Differences of interpretation
may exist between the researcher and the interviewer as well as be-
tween the interviewer and the respondent. There will also be dif-
ferences between respondents in the interpretation of some ques-
tions. The magnitude of this difference will increase as the
heterogeneity of the sample increases.
The complete elimination of language problems is nearly impossi-
ble, but careful consideration of cross-cultural communication dif-
ficulties will minimize problems of interpretation in the question-
naire. Two major cross-cultural communication problems in ques-
tionnaire formulation are differences in terminology and cultural
differences in beliefs and values.
The names by which things are known or the terms which are used
to describe something vary widely even within a language and
culture group [17]. These differences in terminology must be con-
sidered in the construction of the questions. Generally, the lower the
general level of education and the greater the degree of isolation, the
more important it is to take local usage into account. When groups
are isolated, indigenous objects and phenomena frequently have
names which are known only within the groups. Objects or
phenomena which are not indigenous will probably have similar
names, but if they are unknown within the group, they may be dif-
ficult to explain. Terms used by professionals may not be
understood at all by the respondent, yet if the interviewers are
allowed freedom in explaining these unknown terms, an interpreta-
tional bias may arise. In some cases, professionals may be able to
communicate with each other and with the interviewers with one
question, but several questions may be necessary to obtain informa-
tion from the respondents of a survey.








A common example of varied usage in Colombia is the problem
with land measures. Four measures, the hectare, the plaza, the
cuadra, and the fanegada, are used in different parts of the country.
When a survey is conducted, one must construct the questionnaire
to account for this variation or risk interpretational errors in
measuring farm size, crop yields, and other factors associated with
land area.
A different problem arose in Colombia with respect to a question
concerning tapeworms in swine.1 In each of three different depart-
ments (states), a different local term had to be used in the question
to avoid erroneous conclusions which could have been drawn when
determining the respondents' knowledge of the incidence of this
parasite in the survey area.
Unknown or misunderstood differences in beliefs and values are
the other important cause of interpretational problems. An example
can be drawn from interviews with housewives in a Colombian
village where the drinking water was badly contaminated [27].
Many of these housewives realized the importance of boiling their
drinking water and understood the technique involved but still did
not do it. Informal interviews determined how many housewives did
boil water, how many realized they should, and how many
understood the technique, but the survey failed to determine why
the majority did not actually boil water. The difficulty developed
because appropriate questions were not asked due to inadequate
cultural knowledge. The professionals assumed that boiling water
was "good", and based upon survey results and their own nor-
mative considerations, they determined the housewives "should"
have been boiling water. Only after further questioning did the
researcher discover that the villagers believed that water was boiled
only for the very ill. Those who drank boiled water were considered
to be ill, so to avoid this stigma, many who otherwise might have
boiled their water failed to do so.

Designing for Data Retrieval
A questionnaire must lend itself to efficient and useful data collec-
tion, processing and analysis. The allocation of time and resources
between field collection and data retrieval and verification from
completed questionnaires needs careful consideration when design-
ing a questionnaire. Long and detailed qualitative responses to
open-ended questions may be more informative than quick semi-
quantitative responses involving selection of alternatives, but the
time required to correctly obtain, retrieve and analyze one long

'The question is from a questionnaire which provided information for a Masters
thesis 1491.








response may be the same as for twenty short selective response
questions. One must determine which of these two techniques (or
combinations of both) most efficiently provide information while
minimizing interpretation and response errors.
Both collection and verification are facilitated by logic and con-
sistency and by organizing the questions to lead the respondent
with ease through the questionnaire in a manner that stimulates
spontaneity in his responses. If one question asks about his
marketing problems for rice, and is followed by the age of each
member of his family, and the next returns to the price of rice, the
interview becomes burdensome and less effective. One exception to
this rule is important. If one desires to cross-check or verify a par-
ticular response because of interpretational problems or a reluc-
tance to respond accurately, it may be desirable to ask the same
question twice, each time in a different manner and at different
points in the interview.
Dividing the questions into major groups will help reduce respon-
dent fatigue while maintaining interest. Groups of questions that
involve sensitive issues; such as income or profit that may be sub-
ject to taxes, might follow several relatively less sensitive groups of
questions in order to establish rapport and confidence. Sensitive
issues covered prematurely in a questionnaire may destroy the en-
tire interview and thus should be asked toward the close of the inter-
view.
Coding, or preparing the data for computer analysis, is necessary
if the data are to be punched on cards for electronic data processing.
Precoding which refers to specifying on the questionnaire the col-
umn requirements for computer cards, can eliminate the need to
transfer the data to code sheets by permitting the data cards to be
punched directly from the questionnaire. This method requires more
careful editing of the questionnaire but usually reduces retrieval
costs and the chance of human error by removing one transfer of
data. To be more efficient, however, the researcher must be certain
beforehand of how he wants to organize and process the data. The
editing and checking process, on the other hand, helps assure that
all necessary data will evolve from the questionnaire. Precoding is
less advisable for highly qualitative responses or when the re-
searcher has no indication of the range of responses he might expect
for each question.

Pretesting the Questionnaire
Pretesting the questionnaire or checking to see if it will obtain the
information sought, must be accomplished under actual field condi-
tions before beginning the general survey. No amount of intellectual








exercise in the office can substitute for properly testing a question-
naire among the respondents in the area to be surveyed. This phase
will determine the weaknesses in the questionnaire, establish
whether the information sought can be obtained in a useful form,
and also may provide additional information which can help im-
prove the relevancy of the questionnaire.
Whenever possible, the interviewers who will be conducting the
survey and the director of the research project should be involved in
the pretest to assess problems with the questionnaire and with
respondents. Prior to protesting, interviewers should become
familiar with the research project and the questionniare. Each inter-
viewer should be carefully briefed and sent to several of the dif-
ferent areas which have been selected to assure experience with a
variety of respondent types. When each interviewer returns, a
debriefing with the researcher should be performed immediately.
This debriefing should include a review of the completed question-
naire to identify points of misunderstanding, unnecessary repeti-
tion, time difficulties and so on. General reactions by both the
respondents and the interviewers concerning the subject matter and
length of the questionnaire should be discussed.
For some interviews care should be taken in selecting the areas
where the pretest will be performed to avoid the danger of con-
taminating a major target area by pre-testing the questionnaire
within its bounds. Especially in close-knit rural areas, advance
knowledge of attitudinal responses in the final survey could be af-
fected by word of mouth discussion among potential respondents
and those participating in the pretest.
Size of Pretest
The required number of pretest questionnaires depends upon the
research problem, the homogeneity of the survey population, the
data collection and analytical techniques to be employed, the total
number and complexity of the questions to be asked, and the
research resources available for the project. The range may be from
five to one-hundred or more questionnaires. If the population is very
heterogeneous with respect to information required, more inter-
views will be necessary for an adequate pretest than for a question-
naire to be administered to a more homogeneous population.
If the pretest is also to provide desired information about
variance to determine the population homogeneity for sampling
purposes, more pretest interviews may be needed than when only a
test of the questionnaire is desired. For potentially large studies
where general population parameters are unavailable, a rather ex-
tensive pretest can aid in designing the sample. The marginal costs
associated with these additional pretest questionnaires must be








measured against costs associated with sampling errors due to hav-
ing too few respondents. Extra costs resulting from obtaining ex-
cessive questionnaires in the main interview effort may be greater
than the costs for a few additional questionnaires in the pretest.
Information Checking
How a pretest improved a particular question in a survey of set-
tlers in the colonization project in Caqueta, Colombia is illustrated
as follows [48].
Original question:
Where did you live before you moved here?
Improved questions:
In which Department were you born?
Where did you live before moving to Caqueta?
Where did you live before moving to this farm?
The researcher wanted to know from which departments the set-
tlers had migrated to Caqueta, but the original question only re-
vealed information about the respondent's last move which may
have been within Caqueta and not from another department. Even
though questions were added, the results were easier to interpret.
Based on this researcher's information needs, the first question had
no value so he was forced to choose between no question and three
questions.
A pretest will assist the researcher to develop an efficient but
complete questionnaire. A poorly pretested set of questions de-
signed to obtain information about farm management practices and
production costs for potato producers in Colombia illustrates this
point.2 The respondents were asked the number of hectares devoted
to potato production for the most recent year at three different
points in the questionnaire. At the beginning, producers were asked
to give total farm size and land use by crops, pasture, and other
classifications. Later in the interview they reported on potato
plantings for each semester. This information often did not agree
with the first question because of double counting. It was difficult
to verify the response because plantings in the first semester were
usually much larger than in the second, so the extent of the double
counting was impossible to determine. And finally, when queried
about hectares harvested the area given was usually slightly dif-
ferent from that previously used. Could it be assumed that those
hectares not harvested were lost to blight or drought? Perhaps, but
it would be better to be certain that the difference was not in part
due to either intentional or unintentional reporting error. Not only
was the duplication unnecessary, but the questions were am-

2From a portion of a producer questionnaire used in Colombia and summarized in
[37].








biguous. A better pretest could have minimized this discrepancy by
guiding the preparation of one comprehensive and easily
understood set of questions related to hectares of potatoes seeded
and harvested for the past year.
After completing the pretest, responses should be tabulated and
critically reviewed to determine whether the questions were
understood clearly and whether the information which resulted
would help to resolve the problem. This step frequently uncovers
gaps in information, or responses which are not in the best form for
the analyses which are to be undertaken. While the pretest may
identify needed modifications in the questionnaire, it also provides
an opportunity to determine whether or not the responses are in a
form such that the hypotheses can be tested. The researcher should
carefully consider what is being collected in the pretest and how it
can be used in the hypothesis testing and analysis phases of the
research.
Some research advisors insist that their students use pretest data
on a practice run through the analysis which slows the research
process, but only momentarily. Analyzing pretest data can aid
greatly in refining and even specifying the exact analytical tools
which typically are not given too much thought until all of the data
are collected. This may also provide some ideas as to the format for
the final publication. If these benefits are gained from analyzing
pretest data then the saving of time at a later stage in the research
process will more than compensate for the early time loss.
Often a pretest indicates that either too little or too much infor-
mation is being collected. At times, the pretest may show that a cer-
tain type of information will not be available, and therefore, some
modification will need to be made in analyses, hypotheses, or objec-
tives. More commonly, the questionnaire may be gathering more in-
formation than is necessary which suggests that costs of getting
the excess should be carefully considered relative to potential uses
of the information.
Only when protests have shown that interpretational problems
are minimal and that the information can be obtained in the form re-
quired, should the questionnaire be finalized and the complete
survey undertaken.
Time Difficulties
Because timing is so important, the pretest should be used to help
specify interview time problems; time of year, month, week, and day
must be carefully considered to fit the respondents' work schedule.3
3Mail questionnaires help resolve time problems but often limit response quality
and, due to low response rates, require large samples. A bibliography concerning
mail-questionnaire research is found in [42].








If, for example, one is proposing to interview truckers at check sta-
tions or consumers at a retail market the time element may be more
critical than in interviews with producers. Retailers usually prefer
granting interviews when the fewest customers are in the store. For
small retailers, a daytime interview can be three or four times as
long as an uninterrupted nighttime interview; but if an appointment
cannot be obtained for non-working hours, as is often true, the more
lengthy daytime interview may be necessary. Agricultural pro-
ducers probably will find harvest time to be inconvenient.
Numerous examples can be cited but few guidelines can be
specified for timing interviews because each survey and often each
interview represents a different situation. In any case, should the
respondent feel that the questionnaire is completely irrelevant and a
waste of time, the responses will lack credibility even when the in-
terviewer is fortunate enough to complete the interview.

Selecting and Training Interviewers
A well-designed and pretested questionnaire will not yield good
results if administered by an interviewer who is poorly trained or
who has a disagreeable, non-cooperative and culturally or class
biased attitude toward the respondent. The contact made with the
respondent will not only influence the interview but also influence
the respondent's evaluation of the entity for whom the interviewer
is working, and his acceptance of action programs that might evolve
from the research recommendations. Requisites of a good inter-
viewer include: 1) an interest in and an understanding of the
research project, 2) an interest in people and the ability to com-
municate that interest and sincerity to a respondent, 3) a will-
ingness and ability to follow instructions and definitions without
regard for personal beliefs or convictions, and 4) in general, a good
public relations attitude.
Training is necessary to familiarize both experienced and inex-
perienced interviewers with the problem to be studied, the objec-
tives of the research project and the organization sponsoring the
work, the questionnaires, the respondent selection procedures and
the interviewing techniques. An interviewer's manner of asking
questions must be objective and he must be neutral and honest in
recording responses. The most important goal of the survey is to get
an unbiased opinion or bit of data for each question. Whatever the
interviewer thinks of the respondent and his opinions should not in-
fluence interviewer objectivity.
Interviewers should be instructed carefully on methods for in-
troducing the research project to the respondent. A most difficult
question to answer is a respondent's inquiry about the legitimacy








and legality of the study. Letters of introduction and any other
documents from official and credible sources should always be in the
possession of interviewers. To establish respondent confidence, the
introduction used by the interviewer and the letter itself should ex-
plain that the interview is confidential, and that the information
sought will be used to determine attitudes and characteristics of
groups for comparative purposes, and not to illustrate characteris-
tics of specific respondents.
Although the interview must be objective, the interviewer must
be given flexibility to obtain a useful response. He should record
specific comments and not vague or meaningless generalities such
as "because it is interesting" or "I like it because it is good," Why is
it interesting? Why is it good? The "I don't know" response is one
of the most difficult to manage. One is not sure whether the respon-
dent really did not know, whether he did not understand the ques-
tion, or whether he did not want to respond for various reasons. The
respondent should be encouraged by the interviewer to believe that
an "I don't know" response is not an admission of ignorance. A sim-
ple guess or an estimate calculated to suit what the respondent feels
the interviewer wants is unacceptable. The respondent, however,
should not be encouraged to give an "I don't know" response when
he most likely can give a meaningful and honest reply.

Verifying Primary Data
Verification of the data after it is collected is necessary before
data processing and analysis begin. The researcher must become
fully aware of the limits and potential uses of his data based upon
the objectives of his research. A careful review of data with well per-
formed revisions at this stage of the research process will reduce the
chance of costly errors.
Numerous and detailed verification activities are necessary so the
researcher can be confident that the data best measure the
phenomena needed to fulfill the research objectives, given the
resource limitations placed upon the project. The verification and
preparation process must consider techniques for handling missing
observations, falsified data, completely inaccurate measures, and so
on, as well as the necessary conversions where measuring units have
differed. Often the required transformations, corrections, and
general manipulations performed on data are dictated by the type of
facilities available for processing. Computer processing is not
necessary for all research, particularly when projects are small and
calculators are readily available. Both time and monetary costs for
each alternative should be considered along with the type of
analysis desired and the degree of accuracy needed. But regardless







of the processing facilities and techniques to be employed, well
specified methods for verifying and coding data will improve effi-
ciency in research resource use.
During field data collection and while verifying and coding
results, the researcher should accurately record his collection and
verification techniques. This record, commonly called a code book,
is also oriented to describing data collection and verification tech-
niques and it may also include some data refinement such as fre-
quency distributions, means, standard errors and so on. It not only
serves as a guide to his present work, but also serves to indicate in
detail what the data represent for use in further research efforts.

Verifying and Using Secondary Data
The researcher usually has no control over secondary data
measurement but he has the responsibility for checking very
carefully to determine exactly how it was derived, the nature of the
aggregations made and if the data will meet the needs of his
research [65]. Secondary sources can be classified into two major
types: 1) regular and long-run measures of phenomena such as
prices, income, production, rainfall, and temperature; and 2) less
regular and, in general, short-run measures of phenomena more
often associated with another person's research such as crop
response to improved inputs, consumer attitudes, and management
characteristics.
Time series data can often be difficult to use and verify because
they are secondary and not necessarily designed for a specific
research project. Because time series frequently cover extended
time periods, collection is performed by an agency which is responsi-
ble for maintaining continuity in the collection process. No par-
ticular person is responsible for collecting these data over the years,
but certain procedures are established and revised occasionally to
accurately and adequately measure the particular phenomena under
study. Nevertheless, definitions should be checked carefully so that
the researcher can decide what the data in question are supposed to
measure, and since he has no control over the collection of these
data, he must satisfy himself that they in fact do measure the
phenomena in which he is interested.
Comparability may be the most common difficulty in using time
series data. This refers to 1) comparability of different time series
which purport to measure the same phenomena and 2) comparabili-
ty of different periods within the same series. For example, due to
plant breeding and food processing programs, the corn produced
and marketed fifty years ago and the corn produced and marketed
today are virtually two different products. A price series for corn








covering this period of time is not comparable during the entire
period. Coupled with product changes in a price series is the equally
difficult problem of non-comparability in currency values caused by
price inflation or deflation in the national economy.
A serious comparability problem, and one that is difficult to
detect within a time series, is a change in definition. For example, a
change in quality standards will affect a price series, but may not
necessarily be specified. A change in the point of measurement such
as the location of price determination from the farmer-assembler
level to the wholesaler-retailer level will influence the value of the
economic variable in question. In census data one may find a change
in the definition of a farm. Similarly, changes in the population of a
city may in part be due to changes in the boundary lines.
Non-comparability between series attempting to measure the
same phenomena is common particularly in developing countries
because time series measurement systems have often not been
standardized under the authority of one agency. Unfortunately, in
many cases these entities neither clearly define the phenomena to be
measured nor the precise points and times of measurement. And
when the points and times are defined, a change in definition may
not be spelled out after it is made.
As an example, consider the separate series of potato production
data compiled by the Caja Agraria (a public agricultural credit agen-
cy) in Colombia and IDEMA (a public agricultural marketing agen-
cy) [36, p. 230]. These series are vastly different, with the production
data of IDEMA averaging about 40 percent lower than the data of
the Caja Agraria. This difference is probably due to differences in
methods of measurement of the series. The Caja Agraria primarily
develops their series from producer loan data, while that of IDEMA
is based on measures taken in rural markets. Thus, Caja Agraria
more closely estimates total production and IDEMA measures the
production entering commercial channels. If the researcher is in-
terested in the volume of commercial sales of potatoes, the IDEMA
data probably are more accurate. On the other hand, if the re-
searcher wishes to estimate per capital potato consumption, in-
cluding both rural and urban consumers, the IDEMA series would
under-estimate this variable by about 40 percent less losses and
seed requirements.
Where a time series is needed but unavailable or possibly inap-
propriate for a specific problem, cross-section data sometimes can
be substituted. Cross-section data (experimental and non-
experimental) represent phenomena measured only at one point in
time or a limited number of times. One means of using cross-section
data to simulate longevity is in questionnaire research where
respondents recall information concerning an event at present, last








year, five years ago, and so on. Care must be observed in inter-
preting these measures because of what has been termed a telescop-
ing bias. That is, the tendency to completely overemphasize or
underemphasize certain phenomena by projecting present condi-
tions to previous points in time while forgetting others that may
have influenced these observations.


Summary
The success of non-experimental data collection rests upon the
ability of the researcher to accurately sample the defined population
and, once the sample is drawn, communicate with the selected
respondents. In designing the questionnaire which is the means of
obtaining information from the respondents, two important com-
munication problems must be considered: 1) differences in ter-
minology between various groups, and 2) cultural differences in
beliefs and values. Pretesting the questionnaire under actual field
conditions can provide information both about its effectiveness as a
data gathering tool and information on the population which can
help in establishing sample size.
As in the case of experimentation, guidelines for optimum sample
selection procedures and questionnaire design in non-experimental
research can be stated, but in applied research it is important that
the researcher remain flexible in his attitude. Scientific perfection
can serve as a norm, but the researcher must remember that for the
client, it is almost always better to have some information to help
him make his decisions than to have no information except that the
researcher is still designing a better questionnaire or trying to
decide on the best means of choosing the respondents.








CHAPTER VI


DATA UTILIZATION WHAT DOES IT ALL MEAN?
The real skill of the applied researcher comes into play after the
collection of the data has been completed. Experience and imagina-
tion have a particularly high payoff in the analysis and the inter-
pretation of the data and can make a difference between a useful
project and one which ends up in a file drawer. It is in this process
that the researcher finally comes down to the point of determining
what the data entail; data do not "speak for themselves" but must
be interpreted and analyzed. The researcher must draw conclusions
from the analysis and in the end make recommendations to his
client to help in resolving the problem that originated the project.
This, of course, is the reason for undertaking applied research in the
first place. No amount of planning, no elegant data collection pro-
cedures, and no sophisticated analyses are going to help the re-
searcher who is too timid when the moment of truth arrives to
utilize all his information, draw meaningful conclusions and make
appropriate recommendations to the client.'
When this moment arrives, the client is expecting a useful
product and the researcher is the most knowledgeable person
available to him. At the conclusion of this project, the researcher
should know more about the subject being studied than anyone else
with whom the client has contact. If this is not so, the client should
have gone elsewhere for his information. And if the client had not
needed the information he would not have contacted the researcher
nor utilized the other research resources. Hence, the researcher
must assume that his knowledge is vital to the client and that the
client desires the fullest utilization of the resources which have been
expended by the project. Also, because of the nature of applied
research, the researcher is usually facing a deadline, so additional
data collection is seldom possible. Conclusions and recommenda-
tions must be made on the basis of the data at hand because that is
the best information that is or will be available within the allowable
time and resource restrictions.
Rather than cover the myriad of analytical procedures available
to the researcher, which are presented in detail in a variety of good
sources, this chapter treats the more personal aspects involved in
the interpretation of research results.2 These are the aspects which
might be called in part, the art of research, and which also might be

2A sampling of basic texts covering analytical techniques includes (13, 20, 26, 6,
321.
'For references particularly concerned with interpretation and communication of
research results see [21. 44, 57. 78].








called subjective analysis or, by the purist, personal viewpoints and
judgements. We prefer to think of subjective analysis as flexibility
in ones attitude toward the scientific procedure and the approach
discussed in this book. This is the attitude that allows the re-
searcher to look beyond the numbers which result from the analysis.
It is this attitude that allows the researcher to completely milk the
data and draw out all the information which might be of help to the
client. Furthermore, this attitude encourages the good applied
researcher to insist on a role in the interpretation of the statistical
or other analyses which have been used (either by him, by another
person, or by a computer) rather than accept these impersonal
results without question.3
Applied research is not useful to the client when the researcher
reports that he had to go back for more data so has no conclusions,
or that based on such and such a level of confidence there is no
significant relationship between the variables. It is more useful to
report that, although a relationship is not strong, and high con-
fidence cannot be placed in the conclusion, there is a tendency
toward a particular relationship and that because this response is
logical, the relationship can be used in resolving the problem. Or
perhaps, just as strong a statement could be made for there not be-
ing a relationship between certain variables. The client is depending
on the researcher to draw a conclusion and make a recommendation.
The client, then, armed with this best estimate of the researcher,
ultimately makes the decisions to be taken to alleviate the problem.
Because the client must make a decision, it is also necessary that
he understand the information which the researcher presents as the
results of the research. Too often the investigator writes his report
as if he were communicating only with other professionals and
thereby ignores the needs of the client toward whom the presenta-
tion must be directed.
In this chapter, two factors associated with the utilization of data
in applied research are discussed. First, an attitude of flexibility in
the analysis of data prevents the researcher from becoming boxed in
by tradition to the point that he is unable to understand what his
data are trying to tell him. A lack of flexibility on the part of the
researcher can impede the complete interpretation and full utiliza-
tion of the data so that the client does not achieve maximim benefit
from his investment in the research undertaking. The second aspect
of data utilization to be covered is the presentation of the results in
a form such that the client can adequately understand the implica-
tions of the project and use the results accordingly in his decision-
making process.

'For a lengthy debate concerning the role of social science research in prediction
and prescription see [45, 59].








Flexibility in Interpretation
Flexibility in the interpretation of the data and the analyses of the
research project does not mean their manipulation to achieve
predetermined results. This defeats the purpose of a project under-
taken to resolve a problem. Flexibility refers to the capability to
really comprehend what the data and the analyses mean and how
the relationships which they express can be used to advantage by
the client in making a decision.
In close conformity to the needs and desires of the client, the ap-
plied researcher should utilize all his training and experience as well
as the knowledge gained from the current project in order to provide
useful information. This includes a complete examination of the
results to determine their meaning as well as their reliability. Over-
emphasis on measures of reliability and insistence on rigid
standards frequently set for more optimum conditions or different
problems, reduce the ability of the researcher to explore the data in
more detail and to fully understand the meaning of the results.
Meaning of the Results
A common fault in the research process is to accept results of the
analyses as something sacred, even if they do not appear logical. An
important aspect in the research process is the selection of choice
criteria, the measures of performance, efficiency or success which
serve as guides in the theoretical construction of the problem state-
ment. The performance criterion which should have been of concern
to the researcher in our opening dialogue of this book was the in-
crease in the total production of certain crops in his country for
which trade agreements had just been made. An agronomist work-
ing on the development of a new variety may have as a performance
criterion the resistance of the crop to a certain disease. The same
agronomist also, of course, has a secondary criterion, that of in-
creasing production per unit of land area in which the crop is grown.
A farm economist usually considers the maximization of profit to
some resource base as the most relevant performance criterion to
use.
Blind adherence to a predetermined set of performance criteria
and the lack of flexibility in considering alternatives can frequently
obscure the real nature of the problem which is being treated. Three
examples of errors in interpretation owing to the misuse or the
misunderstanding of the performance criteria will be discussed.
These examples should provide the researcher with some ideas of
the kind of flexibility that is needed in the interpretation of his
research results.
Example 1.4 The performance of potato producers in Colombia, as
4Taken from [371.








Flexibility in Interpretation
Flexibility in the interpretation of the data and the analyses of the
research project does not mean their manipulation to achieve
predetermined results. This defeats the purpose of a project under-
taken to resolve a problem. Flexibility refers to the capability to
really comprehend what the data and the analyses mean and how
the relationships which they express can be used to advantage by
the client in making a decision.
In close conformity to the needs and desires of the client, the ap-
plied researcher should utilize all his training and experience as well
as the knowledge gained from the current project in order to provide
useful information. This includes a complete examination of the
results to determine their meaning as well as their reliability. Over-
emphasis on measures of reliability and insistence on rigid
standards frequently set for more optimum conditions or different
problems, reduce the ability of the researcher to explore the data in
more detail and to fully understand the meaning of the results.
Meaning of the Results
A common fault in the research process is to accept results of the
analyses as something sacred, even if they do not appear logical. An
important aspect in the research process is the selection of choice
criteria, the measures of performance, efficiency or success which
serve as guides in the theoretical construction of the problem state-
ment. The performance criterion which should have been of concern
to the researcher in our opening dialogue of this book was the in-
crease in the total production of certain crops in his country for
which trade agreements had just been made. An agronomist work-
ing on the development of a new variety may have as a performance
criterion the resistance of the crop to a certain disease. The same
agronomist also, of course, has a secondary criterion, that of in-
creasing production per unit of land area in which the crop is grown.
A farm economist usually considers the maximization of profit to
some resource base as the most relevant performance criterion to
use.
Blind adherence to a predetermined set of performance criteria
and the lack of flexibility in considering alternatives can frequently
obscure the real nature of the problem which is being treated. Three
examples of errors in interpretation owing to the misuse or the
misunderstanding of the performance criteria will be discussed.
These examples should provide the researcher with some ideas of
the kind of flexibility that is needed in the interpretation of his
research results.
Example 1.4 The performance of potato producers in Colombia, as
4Taken from [371.







in most places, has been measured by research and extension
specialists in tons per hectare simply because of professional tradi-
tion. Experimental yields per hectare have improved annually since
the early 1950's to the point where they now at least triple and often
quadruple average producer yields. Yet yields on both commercial
and subsistence farms have risen only slightly over the same period.
The research and extension programs are often under criticism for
not stimulating at least part of the yield increases which are known
to be possible. At the same time farm research reveals that both
large and small producers are appying fertilizer and pesticides at
levels near to those recommended, and many are also using im-
proved seed. Total potato production has increased to keep pace
with population growth mainly through the dedication of more land
to the crop, but yields per hectare remain embarrassingly low.
Should producers, agronomists, and extension specialists be embar-
rassed? Maybe not.
Except for the few potato farmers who rent land for potatoes, land
represents a relatively low cost input to even the small producer.
The small farmer in the potato regions usually produces only this
one crop as a cash crop. Hence, he considers all his good land as
potential potato land, and if feasible, he usually has marginal land
that can also be put into potatoes. The farmers use as much of their
best available land as necessary to consume their seed supply. It is
this seed supply which is an expensive input in the eyes of the pro-
ducer. This is because during the year he has the alternatives of sell-
ing or eating the potatoes which are held in storage, and some of
which he is saving for seed. As the time for seeding gets nearer, the
price normally rises and the potatoes tend to spoil, creating even
more temptation to sell the small amount of seed stock remaining.
The purchase of seed, if necessary, creates a real hardship on him
and his family. Hence, at planting time a producer has only a limited
supply of good seed available. As a result, even if he has a small
farm, he may have more land available than seed to plant on it.
Thus, it is not yield per hectare which most interests the potato
producer but yield per amount of seed used. He even reports his
yields in a yield-seed ratio such as 20:1. This goal (the farmer's per-
formance criterion) implies wider spacing between rows and be-
tween plants than is recommended by agronomists who consider
yield per hectare as a performance criterion. The result is a higher
product-seed ratio, but a relatively low product-land ratio. Never-
theless, the potato producer is reacting rationally to his resource
situation and neither he nor his scientific advisor should be embar-
rassed by low per hectare yields. In fact, the product-seed ratio has
improved substantially in large part because of the technical
assistance the producer is receiving. But at the same time, many








research and extension workers remain frustrated because they are
relying on an inappropriate, though traditional, performance
criterion.
Example 2.5 A similar situation has occurred in West Pakistan
where irrigation is necessary for crop production. Historically,
water, whether provided by canals or by Persian wheel wells, has
been a much more limiting resource than land even though the
average farm size in many areas is only about seven acres. Contrary
to recommendations, farmers were not using sufficient quantities of
irrigation water to obtain highest yields per acre. Rather they
spread the available water over more land so as to obtain maximum
production per unit of water (they still were not able to use all of
their land at any one time). Before more water was made available,
an increase in water application per acre would have reduced the
amount of land they could irrigate and would have reduced the crop-
water ratio, resulting in less total crop for the very limited quantity
of water. The gain in yield per acre clearly was not an appropriate
goal. Of course with more water and the use of new technical inputs,
increased production per acre became a relevant goal.
Example 3.6 Returning to Colombia, some questions have arisen
recently concerning productivity measures for dairy farms. Perfor-
mance of a dairy herd is traditionally measured by the average
pounds of milk produced annually per cow for the entire herd in-
cluding dry cows. The goal of a dairy improvement program is
usually to maximize this average. This performance criterion re-
quires that breeding programs develop large cows with well
developed udders and other characteristics conducive to high yields
per animal. It has also been assumed in Colombia that protein is
lacking in the rations for dairy cattle.
Preliminary research shows, however, that the very hilly, moun-
tainous and rainy pasture conditions found in Colombia may require
both different nutrition and breeding research programs. The large
dairy cow cannot move about in the mud and hills nearly as well as a
small one. Maybe three small cows could better adapt to these con-
ditions than two large cows while better utilizing the same amount
of pasture even though yield per cow would be sacrificed. Of course
milking costs, veterinary costs and so on for alternative herd sizes
should be considered. As to nutrition, the energy requirements for
the cows appear to exceed most expectations making energy rather
than protein the most important deficiency in the usual ration.
These preliminary conclusions have resulted because of a flexible at-

5Experience by Peter Hildebrand on an assignment with Tipton and Kalmbach,
Inc., Engineers in West Pakistan, 1964-1966, and reported in [33].
6Research notes reported by Rex Rehnberg and L. C. Garrison, Universidad Na-
cional Instituto Colombian Agropecuario, Medellin, Colombia.








titude on the part of the researchers interpreting data which initial-
ly appeared not to be very logical.

Reliability of Results
The degree of reliability of research results is nearly always
assessed by statistical tests of significance which utilize confidence
limits or levels to judge how much reliability one may put in the
results. A common research practice is to set confidence limits
before data collection is undertaken and then to reject results which
cannot be measured to that fixed degree of precision. For example, a
null hypothesis is accepted if the tests of significance that are used
reach a certain level of confidence and rejected if that level is not
reached. Terms in an equation fit by regression analysis are ac-
cepted at certain levels of significance and rejected below these
fixed levels. In analysis of variance, differences in responses to
various treatments must meet certain levels of significance before it
is said that the differences are real and significant.
For some research projects, however, significance tests may be
impossible and even unnecessary depending upon the nature of the
problem and resources available to resolve it. McPherson notes, for
example, that economists, when adjusting experimental data for
predicting a production function, can rely upon the experience of
specialists in several basic science disciplines for knowledge about
unmeasured environmental and agronomic conditions which in-
fluence crop production. "The fact that 'expert's' estimates would
not be subject to statistical tests of significance does not mean
necessarily that such estimates for particular purposes would be
less accurate than estimates derived from sources that lend
themselves to statistical tests." [67, p. 804].
In other research projects, strict adherence to predetermined
levels of significance is necessary in order to preserve objectivity
and protect the client from unnecessary risk. Experimentation with
drugs is a common example where very high significance levels
must be maintained if the drugs are for human use.
But there is also a great deal of research, particularly applied
research, where adherence to the strict observation of high
significance levels can unduly restrict the amount of information
which is made available to the client, and can, in fact, be detrimental
to his best interests. Consider, for example, the case of a low cost
practice which appears to increase the yield of a particular crop, but
the increase in yield is not significant at the 90 percent or 95 percent
levels of confidence. If we tell our client that there was no signifi-
cant increase in yield, he will probably decide against its use. But if
the cost of the practice is low, then the financial risk to the producer
is also low (assuming that there is no reduction in yield due to the








use of the practice). It might well be that the producer is willing to
accept this risk if he has even a 50/50 chance of getting an increase
in yield that would be worth 10 times his investment if, in fact, there
really was an increase. In this case, wouldn't it be better for the
client to receive information saying that there was an increase in
yield in most cases with the use of the practice, but it requires a
reduction in confidence limit to, say, 70 percent to be a statistically
significant increase?
A 70 percent confidence level, in essence, means that if this same
experiment were repeated 10 times under the same conditions
chances are that at least 7 of the 10 experiments would indicate an
increase in yield. Note that neither a 70 percent nor a 95 percent con-
fidence level tells us that there will or won't be an increase in yield
on any particular farm. That effect is only implied by conducting
the experiment under conditions similar to those of the client or
clients. But if there is a valid similarity between the experiment and
the conditions under which the client will use the practice, then the
costs and the probable returns of the practice to the producer (the
economics of the practice) will ultimately determine if the client will
utilize that particular practice on his farm.
In applied research, it is important to remember that the client is
waiting for conclusions and recommendations. If the research pro-
cess has been thorough, then no additional information will be
available to help the researcher in making a conclusion unless addi-
tional research is conducted. If the researcher insists on additional
research before he is willing to make any conclusions and recom-
mendations then it will mean a delay before the farmer (client) can
put the practice into effect. This represents potential profit foregone
by the client (if in fact a change would have been profitable) and
should definitely be considered by the researcher and by the client
before making a decision not to draw any conclusions from the
results of the completed project. When necessary and appropriate,
one can utilize sensitivity analysis to determine the economic costs
of making the wrong decision [1, pp. 39-45, 54, 66].
Although the decision to repeat the research may mean a delay of
only one year to the client, the economic consequences as well as the
decision not to draw a conclusion are related to the so called Type I
and Type II errors which can be made in accepting and rejecting
hypotheses. A hypothesis is stated in such a form that it is either
true or false. The research process generates the evidence uoon
which the researcher will decide whether or not to accept the
hypothesis as stated. But any acceptance or rejection of a mean-
ingful hypothesis is always associated with a probability of making
an error (it is virtually impossible to be 100 percent sure that a
hypothesis is true or that it is false). A Type I error is committed








when a hypothesis is rejected (thought by the researcher to be false)
when, in fact, it really is true. If there truly is a response to a prac-
tice and the research hypothesis states that there is, then the re-
searcher is committing a Type I error if he advised the client that
there is no response because his research indicated that the
hypothesis was false. Note that the same consequences follow if the
researcher, because of adherence to a too high confidence level is un-
willing to decide and hence advises the client that he is unable to
determine if there is a response.
In this same example, the researcher is committing a Type II er-
ror if he accepts the hypothesis (thinking it to be true) when, in fact,
it is false. In this case he advises the client that the practice results
in an increased yield when, in fact, it does not. The consequences to
the client of the two types of error are obviously different.
If the client follows the conclusions (and presumably the recom-
mendations) of the researcher then in the first case (Type I error),
the client does not adopt the practice which would have increased
yield and foregoes potential income which he would have earned if
he had invested in the practice. In the case of a Type II error, the
client invests cash in the practice without results and loses the
money invested. The Type II error can be even more serious if the
practice actually reduces yields (the true effect is negative and not
zero).
There is an important relationship between the two types of er-
rors such that a change in the confidence level in an attempt to
reduce one type of error increases probability of making the other
type of error. Too much concern with the Type II error (the one most
commonly considered) can raise confidence levels so high that a
Type I error becomes highly probable. When a researcher wants to
be very sure that there is a positive response before he recommends
a practice, he may, in effect, be advising his client that there is no
response even though one does exist. This is usually the case when
the only recommendation made from an experiment is that it should
be repeated with better control, or that a survey must be repeated
with a larger sample or better questionnaire.
The need for flexibility in applied research should be obvious from
the above example. A researcher may not need to involve the client
in decisions about specific tests once the general confidence
guidelines have been specified. But a good dialogue throughout the
data interpretation process may be important if new questions arise
concerning the overall confidence desired. For some problems which
may be extremely difficult to research or for specific hypotheses,
the client may be willing to accept a recommendation which should
hold true only a majority of the time. Other issues may be more
critical and require very high levels of confidence. But neither the








client nor the researcher should automatically insist on high con-
fidence levels because to do so may result in the loss of a great deal
of useful information.
Finally, the end result of an applied research program may not
with any degree of confidence resolve the problem to which it was
directed. Lest this statement seem completely out of context after
all that has been said in the book, we should hasten to add that few
problems are totally resolved. In many cases, the end result of
research is the identification of other related problems and possibly
hypotheses concerning their resolution. Often, when reporting ap-
plied research results, definitive recommendations which may help
resolve the problem are accompanied by a new question or
hypothesis. This is true because applied research is a continuous
quest for knowledge which appears to have few bounds beyond time
and resource restrictions.


Presentation of the Results
By now the need for drawing conclusions from applied research
and making recommendations to the client should be obvious to the
reader. The researcher is the expert to whom the client is looking for
help. If the reseracher is unwilling to fulfill this role, there is little
justification for his having undertaken the project.
We have encouraged flexibility and the use of the researcher's ex-
perience and his opinions in the interpretation of the data and in the
analyses. This is important to the client. But the client is also enti-
tled to a clear exposition as to what part of the conclusions result
from the conjecture of the researcher and what part is attributable
to the pure analysis of the data. As a scientist, the researcher is
obligated to report the results of the research project honestly and
objectively. As a professional hired to provide information for
resolving problems, he is also obligated to interpret these results in
accordance with the needs of the client.
The final stage in the applied research process is the communica-
tion of the results to the client. It is as essential that the client com-
prehend what the researcher has to tell him as it is for the researcher
to understand the meaning of the data. The researcher is a scientist
whose job it is to understand complicated analyses and confusing
data. The client has other interests and ordinarily does not have the
same training, so the researcher must report his findings in a
language that the client will understand.
In many cases it is useful to the client to have the results
presented in more than one form. He is entitled to, and should
receive, a complete report on the project including a review of the








problem as finally defined, the hypotheses and the objectives. These
all appear in the final report much as they were written in the pro-
ject proposal. In discussing the conclusions, it is necessary to refer
to the hypotheses and show which were accepted and which rejected
and how each affected the final conclusions. A clear exposition of
this part of the research process provides the client with the basis
for understanding the recommendations made by the researcher.
The extent to which the researcher should detail the analyses and
data collection procedures the more technical aspects of the
research depends on the familiarity of the client with these
techniques and his ability to understand them. Frequently, the
researcher can make one technical report and another, briefer report
can be written in non-scientific language, excluding the technical
details. The detailed report can be used by the client or another pro-
fessional to verify the validity of the research procedures should the
need arise or to serve as a guide to further research, while the
shorter report will usually be more functional for the client to use in
his decision-making process.
The majority of applied research in agriculture serves farmers as
the principal clients through the activities of an extension service.
Many farmers are not interested in how the recommendations came
to be made but they need to know what the recommendations entail
and what they may mean if adopted. The farmer depends on the
researcher and the extension specialist or agent to interpret the
results of the research process. In this case it is most useful to
prepare a research report which presents only the specific recom-
mendations and the nature of the effect or response which can be ex-
pected if the recommendations are followed. An excellent procedure
is for the researcher to present results in a form directed toward ex-
tension specialists and agents and then work with the extension per-
sonnel to prepare a non-technical publication for use in the exten-
sion programs.
A thesis is a research report whose language and content are
directed necessarily toward other researchers primarily the stu-
dent's thesis committee. Few theses are usable in their entirety as a
research report for more popular distribution, but they can serve as
a basis for the generation of a series of documents, each directed
toward a different client or audience. Articles for technical journals
can report the factors of scientific interest which occurred in the
research process. One or more popular reports or bulletins can be
directed toward the ultimate clients or intermediate groups such as
the extension service. Finally, leaflet type publications which pre-

'Research, extension and education can and must blend together in addressing
current and future problems. The need and approach are documented in many
sources including [41, 44, 47, 51, 57, 78].








sent the recommendations in simplified form can also be prepared if
the research is applicable to a wider audience.
Regardless of who the client is, the need to present him with the
results of applied research cannot be over-emphasized. Too often,
especially in institutional research where the client tends to be an
impersonal group not known directly by the researcher, the results
of completed projects are not prepared in a form which is useful to
the client. When this occurs, the client can become disenchanted
with the research institute and financial difficulties often result.
Hence, one of the best ways to assure a continuing source of
research funds and a demand for research services is to present the
client with a useful research product.

Summary
The culmination of the applied research project is the presenta-
tion of the results to the client. The form in which they are
presented will depend on the ability of the client to understand the
nature of the research process. Many clients will be concerned only
with the recommendations of the researcher and what they can
mean to him if he were to follow them. Other clients will desire a
complete research report including a review of the problem being
studied, the hypotheses and the objectives of the project. The
sophisticated client will also want information on the data col-
lecting procedure and the analyses which were used.
Even the sophisticated client depends on the researcher for an in-
terpretation of the results and will want to consider the researcher's
recommendations in the decision-making process. At the termina-
tion of the project, the researcher should be considered as the expert
in the subject and he should use his knowledge and experience ac-
cordingly in interpreting the results for the client, who expects the
full utilization of the resources which he has invested in the project.
In this book, we have described the process of planning and ex-
ecuting applied research undertaken for the resolution of a specific
problem. Planning includes the specification of the problem in such
a form that it is researchable within the resource limitations facing
the researcher, the formulation of testable hypotheses which sug-
gest meaningful solutions to the problem, and the delineation of the
objectives which describe what is expected to be achieved by the
project. The execution of the project includes the collection and
analysis of data, the drawing of conclusions, and the making of
recommendations.
Throughout the book, the justification for applied research is con-
sidered from the point of view of service to a client. The client's need
to make a decision imposes a deadline on such research and the im-








portance of the problem along with the nature of the research to be
conducted, determine the quantity of resources required for the pro-
ject and available to the researcher. Within these time and resource
limitations, the researcher must strive to create a product useful to
the client. With this book, we hope we have presented the researcher
with an approach to research under these conditions which will help
him to become efficient and therefore to be of greater service to his
clients.








REFERENCES CITED


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Bulletins & Journals


35. Allin, Bushrod W. "Theory: Definition and Purpose", Journal
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pp. 409-417.
36. Andrew, Chris O. "Improving Performance of the Production-
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43. Buse, R. C. "Increasing Response Rates in Mailed Question-
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Implications for Economists in the West An Extension




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