An Experimental analysis of clinical judgment

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An Experimental analysis of clinical judgment
Perez, Francisco Ignacio, 1947-
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ix, 83 leaves. : illus. ; 28 cm.


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Clinical judgment ( jstor )
Clinical psychology ( jstor )
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Minnesota Multiphasic Personality Inventory ( jstor )
Philosophical psychology ( jstor )
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Thesis -- University of Florida.
Bibliography: leaves 79-82.
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Over the past three years I have had the good fortune and honor

of working with Dr. Paul Satz, chairperson of this committee. His

long hours of hard work, his persistence, his patience and continuous

support, but primarily his ideas and inspirations have been invaluable.

As a teacher, colleague and friend his contribution to me has been


Special gratitude is extended to Dr. Henry S. Pennypacker, a

truly great colleague and friend, for his continuous support and

encouragement throughout my training. His ideas and work helped me

see the light.

I would also like to express my sincere appreciation to all the

members of my committee, Dr. William Wolking, Dr. Jacquelin Goldman,

and Dr. Hugh Davis for their help in the organization and critique of

this manuscript.

The cooperation of the six judges was instrumental for the success

and completion of this study. I am greatly indebted to Marcia Keener,

Tom Van Den Abell, Lenay Suarez, Carlos Alvarez, Gerald Reynolds and

Brian Lindner for their many hours of hard work.

Many thanks are also extended to Mike Cruse and Bunny Wardlaw for

their help in the preparation of this manuscript.

To my wife, Ginny, I give a special thanks. Without her inspira-

tion, suggestions and moral support both his research and I would be




List of Tables

List of Figures

Abstract . .





APPENDICES . . . . . . . . . .

Appendix A Instructions .. . ...... ...

Appendix B Daily Correct and Incorrect Frqu.n.:.:r

REFERENCES . . . . . . . . .

Biographical Sketch ..........




vii '



1 Experimental Design Schematic . . . . ... 16 & 17

2 Accuracy Ratio Celeration . . . . . .... 25

3 Accuracy Ratio Frequency Multiplier . . . ... 35

4 Record Floor Celeration . . . . . .... 36

5 Record Floor Frequency Multiplier . . . ... 40

6 Frequency Correct and Record Floor Growth Ratios .. 42

7 Frequency Incorrect and Record Floor Growth Ratios .44

8 Accuracy Ratio Total Bounce . . . . .... 45


Figure Page

1 Daily Accuracy Ratio for F 1 . . . . . 26

2 Daily Accuracy Ratio for F 2 . . . . .. 27

3 Daily Accuracy Ratio for P I . . . .... 28

4 Daily Accuracy Ratio for P 2 ........... 29

5 Daily Accuracy Ratio for I I . . . . .. 30

6 Daily Accuracy Ratio for I 2 . . . . .. 31

7 Summary Accuracy Ratio Celerations ......... I

8 Summary Accuracy Ratio
Frequency Multiplier . . . . ..... 3. .
9 Summary Record Floor Celerations . . . ... .

10 Summary Record Floor
Frequency Multiplier . . . . ... .

Abstract of Dissertation Presented to the Graduate
Council of the University of Florida in Partial
Fulfillment of the Requirements for the
Degree of Doctor of Philosophy



Francisco Ignacio Perez

August, 1972

Chairman: Paul Satz, Ph.D.

Major Department: Psychology

The present study provided the first experimental application of

continuous and direct recording of operant methodology to the clinical

judgment process. This novel application attempted to provide initial

answers to four questions: 1) How stable are the daily predictions

made by judges? 2) How does the time involved in making a clinical

judgment influence accuracy? 3) What is the effect of an increase in

available information on clinical prediction? 4) What is the effect

of experience level on clinical judgment?

Six judges, two first year graduate students in Psychology

(F 1; F 2), two Psychology practicum students (P 1; P 2),

and two interns (I 1; I 2) were asked to make daily discrimination

between test protocols belonging to men convicted of first or second

degree murder and those belonging to men convicted of crimes against

property. Levels of information were increased in four phases:

Experiment 1 MMPI's only; Phase 1 New set of MMPI's; Phase 2 -

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starting point for future applications of operant methodology to

the study of the clinical judgment process.


The major emphasis in the field of psychological assessment has

recently turned away from construction and validation of tests toward

a fuller consideration of other factors in the assessment situation.

A large number of studies have been carried out focusing upon various

aspects of the complex process of clinical prediction. Some studies

have been concerned with issues of theoretical importance and others

have also dealt with more practical contributions.

Clinical versus statistical prediction. --One of the main areas

of research since Ieehl's (1954) influential book has been to compare

clinicians to statistical formulae. Meehl (1954) cited 20 relevant

studies comparing actuarial with clinical methods and found that in all

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(Meehl, 1969). Meehl (1965) recently reviewed the relevant research

literature published since 1954, and concluded again that two-thirds

of the fifty studies showed a statistically significant superiority of

actuarial methods.

Holt (1958) criticized Meehl's conclusions on the basis that

several stages are involved in clinical prediction and that Meehl's

comparison focused only on one of these. He argued that the cross-

validation possible with actuarial techniques is not possible with

clinical techniques, and that, therefore, the two methods are not

logically comparable. Holt (1958, 1970) states that the evidence in

favor of actuarial methods may be a function of the experimental

design, which puts the clinician at a disadvantage, rather than the

actual superiority of statistical prediction. An additional problem is

that the clinician has seldom been given the opportunity to incorporate

the actuarial information in formulating his final decision.

Available information. --The information available to the clini-

cian often has been based on non-quantitative data such as interview

material, case history data, and projective tests. Goldberg (1968) has

shown that in such situations, the clinician has been inferior to the

actuarial methods and his judgmental accuracy has decreased both with

increased levels of test information and clinical experience. Shagoury

and Satz (1969) examined the effects of levels of quantitative infor-

mation on judgmental accuracy in a clinical statistical decision-making

task (brain damage versus normal protocols). Judges were provided with

increasing increments of statistical information. The results showed

that judgmental accuracy increased substantially with increased levels

of information. The increase in judgmental accuracy was also shown to

vary with different strategies which the judges utilized during the


Moxley and Satz (1970) asked judges to make postdictive judgments

on the length of stay in psychotherapy (short or long) for a sample of

mental health service clients. Judgments were made under four con-

ditions in which tests and statistical information increased incre-

mentally at each level. It was found that accuracy increased over

levels of information. Moxley (1970) states that if "the clinician

is able to incorporate the statistical information he may equal or

surpass the accuracy of actuarial methods."

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psychosis or neurosis from MMPI data, Goldberg (1965) found that staff

judges and trainees achieved the same accuracy on the average, that

the four best and two worst judges were trainees, and that there was

wide variability on each diagnostician's performance over different

samples. In a more recent study, Perez and Satz (1971) found that

there were slight differences in accuracy between graduate students

in clinical psychology and clinicians in predicting length of stay in

therapy from MMPI profiles. There was a higher overall hit rate for

graduate students than clinicians. Taft (1955) advances the explana-

tion that trained professionals, somewhere in their training, acquire

a "set" which interferes with making unbiased objective decisions.

The process of clinical thinking: cognitive models. --Among the

researchers who have attempted to study and describe the workings of

the clinician's "mind" there seem to be two camps. There are those

who hold that clinical thinking is a mystical, intuitive, and thus

inexplicable process. There are others who theorize that the clini-

cian's thoughts are orderly, logical, definable, and even, in some

cases, deserving of mathematical models.

Among the former is Luft (1950) who has described the process of

clinical judgment as a sifting, screening, and synthesizing of case

materials in "some intangible way." Another is Mann (1956) who in his

review of Meehl's (1954) book emphasized the complexity of human

decision-making and the importance of considering all the factors

involved in judgments.

Perhaps one of the earliest major efforts to demonstrate that the

diagnostic process is capable of truly rigorous investigation was

Hoffman's (1960) study in which he reduced the diagnostic process to

mathematical models. He proposed both linear and configural models,

and suggested that a fruitful approach to research would involve

focusing upon the individual as the unit of research, and studying

his behavior as it relates to each of these mathematical models.

In one analysis of components of clinical inference Hammond, Hursch,

and Todd (1964) applied a multiple regression technique to Brunswik's

lens model. The statistical components derived were used to examine

several types of previous studies. Judges were found generally to

combine cues linearly, but the authors argued that the simple rating

tasks studied are conducive to a linear response system. They

suggested that, for more complex judgments, the lens model of analysis

can prove useful in studying human cognitive processes.

Studying the way in which the clinician processes data, Wiggins

and Hoffman (1968) devised three statistical models, and compared them

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analysis used to study it. Multiple regression analysis suggested that

the judgment process is simple, and that judges used predictor variables

in a linear additive way. The validity coefficient analysis suggested

that judgments are made in a complex, configural way, which agreed with

the judges' subjective impressions of the way in which they utilized

the data.

Feedback and clinical prediction. --Given the discouraging in-

formation that clinical judgments are often inaccurate, a number of

researchers have directed their attention to the use of feedback as a

device to improve accuracy. Feedback always involves giving the subject

some information about his performance in successive trials. Bilodeau

and Bilodeau (1961) say, "Studies of feedback or knowledge of results

(KR) show it to be the strongest, most important variable controlling

performance and learning." Ammons (1956), in surveying the effects of

knowledge of performance, concluded that KR almost universally results

in more rapid learning and a higher level of performance.

There have been many ideas advanced about the role of feedback in

terms of how it may influence work, learning, and performance (e.g.,

Ammons, 1956). Sechrest, Gallimore, and Hersh (1967) present two

hypotheses: a) that feedback operates by providing information by

means of which the subject can adjust his implicit hypotheses; or b)

that feedback serves as a motivational function by convincing and re-

minding the subject that the task is one in which improvement is expect-

ed and possible. According to Underwood (1966), the most dramatic

effects of KR can be shown for tasks in which the precision of the

response is initially very poor, and for which the subject can give

himself at best minimal feedback.

Sechrest, Gallimore, and Hersh (1967) devised three experiments to

provide feedback on predictive accuracy in the expectation that feed-

back could be used to improve performance. Their study stemmed from a

recommendation by Holt (1958) that clinicians should have training

which makes it possible for them to validate themselves as predictors

in much the same way as tests are cross-validated. The "clinicians"

studied were undergraduate students, and the prediction task involved

interpretation of short sentence completion protocols. They found

evidence in all three experiments for the superior performance of those

subjects who received feedback, but the bulk of the evidence suggested

that the feedback effect was attributable to enhancement of motivation

of the subjects, rather than to specific informational value.

Rotter (1967) points out, however, that the Sechrest, Gallimore,

and Hersh study had a number of design limitations. These are: a) the

subjects were undergraduates with little experience and possibly low

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of low-accuracy judges showed substantial improvements for both pre-

dicted criteria; however, the training had no noticeable effect on the

judgments of the high or moderate-accuracy judges.

Perez (1970) studied the effects of feedback on a problem that

clinicians face in their practice -- that of predicting length of stay

in psychotherapy. Sixteen judges, eight clinical psychologists and

eight graduate students in clinical psychology were asked to predict

length of stay in psychotherapy from MIPI profiles. Judges were

randomly divided into a feedback condition and a no-feedback condition.

It was hypothesized that judges in the feedback condition would do

significantly better than judges in the no-feedback condition. The

results, while in the predicted direction, were not significant,

largely because of the high level of initial accuracy on this clinically

relevant task. In agreement with Watley's (1967) finding, inspection

of the performance of each judge individually revealed that feedback

was most beneficial for those judges starting at a low accuracy level.

The clinician behaving. --Clinical psychologists have produced a

widely varied number of hypotheses in response to the evidence that

they have not yet demonstrated their diagnostic prowess.

Hunt, Wittson, and Hunt (1953) have suggested that the confusion

may result not only from lack of ability in the diagnostician, but also

in poorly delineated diagnostic categories and the professional customs

which permit careless diagnosis or inaccurate diagnosis for administra-

tive reasons. Some (Holt, 1958; Sawyer, 1966; and Taft, 1959) have

proposed that the available research comparisons between clinical and

statistical methods are essentially not parallel and are therefore

meaningless. Others (Hunt, Arnhoff, and Cotton, 1954; Hunt and Jones,

1962) have pointed out that the application of formal scoring, mathe-

matical models, and certain statistical treatments to clinical data

tends to distort research findings.

Little (1967) and Rotter (1967) have pointed to such artificial-

ities in research studies as the use of undergraduate judges to re-

present experienced clinicians, the lack of adequate criterion data,

the use of inadequate test data for the prediction required, and in-

complete description of the criterion to be predicted. Some (Payne,

1958; Cole and Magnussen, 1966) have argued that diagnostic test

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or near-equal mediocre guessers." No attempt has yet been made to

provide an answer to this challenge. The precise experimental

methodology of free-operant conditioning, applied to the continuous

and direct recording of the clinical judgmental process, provides a

unique opportunity to study judges making their predictions on a

longitudinal basis.

Experimental analysis of clinical prediction. --Since Sidman's

(1960) influential book, Tactics of Scientific Research, psychology

has witnessed many innovative applications of operant methodology to

traditional problem areas (Ullmann and Krasner, 1965; Ulrich, Stachnik,

and Mabry, 1969, 1970). A most notable example is Lindsley's (1969)

application of the continuous and direct measurement of operant

methodology to the study of traditional psychotherapy. The underlying

thesis of these applications has been that variability is not intrinsic

to the subject matter but, stems rather from discoverable and con-

trollable causes. Any sample of behavior is under the control of a

multiplicity of variables, some of them presumably held constant in a

given experiment, and others simply unrecognized. Sometimes the

variability in a set of data can be located among such factors. Two

subjects may be found to differ in their response to variable A, not

because there is intrinsic variability in the relation between

variable A and behavior, but because they differ in their response to

variable B, which interacts with variable A. The process of tracking

down sources of variability, and thus explaining variable data, is

characteristic of the scientific enterprise (Sidman, 1960).

Sidman (1960) believes that the control of data in research does

not depend upon the amassing of large groups of subjects, or even

large samples from an individual subject. He states that "We must

consider our science immensurably enriched each time someone brings

another sample of behavior under precise experimental control."

Sidman believes that the adequacy of a technique in experimental

psychology should be evaluated in terms of the reliability and pre-

cision of the control it achieves over the independent variables.

According to Sidman (1960), experiments are often carried out

to test the fruitfulness of a new technique. Sometimes the technique

is developed deliberately in order to obtain information that could

not be gained by standard methods; sometimes the technique is simply

tried out of curiosity as to the kind of data it will yield. Technical

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Precise measurement of clinical prediction. --O.R. Lindsley and

his associates have developed the most powerful single tool to measure

human behavior the Standard Behavior Chart. This chart permits the

daily recording of behavior frequencies ranging from 1000 per minute

to one per day; frequencies ranging from one per day to one per

twenty weeks may also be recorded without changing the coordinates of

the chart. This chart, therefore, provides us with a standardized

means of depicting and analyzing frequencies of virtually any human

behavior on a continuous basis. Further, because it is standardized,

it facilitates the comparison of data across different levels of

clinical information as well as comparing different experience levels

and how these variables effect clinical prediction. Even more

importantly, the Standard Behavior Chart greatly facilitates communi-

cation of research findings among scientists.

According to Wolking and Schwartz (1972), there are several

features of the precise behavioral measurement system which makes it

distinctive from traditional statistical measures. These are: (1)--

The basic unit of measurement is frequency, which is defined as the

ratio of the number of behaviors emitted divided by the number of

minutes during which the behavior has been observed. Thus, the in-

escapable dimension of time is made an integral part of the basic

datum. (2)--A very important feature of this system is that it

measures behavior directly. Direct measurement involves: defining

the behavior so precisely that it can be counted with high reliability

(pinpointing); counting the number of occurrences of the behavior and

the number of minutes of observation and making a permanent record of

both (recording); and finally, calculating the frequency of the


behavior observed by dividing the number of movements by the number

of minutes. (3)--There should be continuous recording that is, the

movement should be measured daily or every time the behaver engages

in the behavior, if it is less than daily. (4)--The most important

feature of this system is the graphic representation of the daily

rates, which provides a unique opportunity for rapid and accurate

communication and comparison of facts about behavioral processes.


In considering the present multifaceted design, a number of

research questions arise. The present knowledge in the area of

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3. What effect will experience level have on clinical
prediction? Previous research indicates in general,
that as experience level increases, accuracy de-
creases. The application of the present methodology
provides a more powerful technique to compare each
judge independently.

4. How does the time involved in making a clinical
prediction influence judgmental accuracy?
Researchers have not previously studied this
question. The use of rate in analyzing judgmental
accuracy provides a most sensitive and natural
measure, for it takes into consideration the
amount of time spent in making a clinical judgment.



To insure the best control of this relevant variable, judges with

similar backgrounds and experience with diagnostics were chosen. Six

judges participated, representing three levels of experience. Level

1--Two first-year graduate students (F 1 F 2); Level 2--Two practicum

students (P 1 P 2); Level 3--Two interns (I 1 1 2). All judges

were chosen from the University of Florida and are currently enrolled

as graduate students in clinical psychology. Each judge was his own


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non-homicide (for instructions, see Appendix A). Shagoury (1971)

found that a discriminant function analysis correctly classified 83%

of the total sample. Following the free operant methodology of con-

tinuous and direct recording, each judge was asked to make their pre-

dictions on a daily basis. The research consisted of two experiments.

Experiment I. --The purpose of this experiment was to study the

stability of the daily predictions made by judges using MMPI's only.

This area has received no attention in the experimental literature.

Operant methodology, with its unique feature of continuous and direct

recording, provides a most powerful tool to study this phenomenon.

Sidman (1960) defines a stable, or steady, state as one in which

the behavior in question does not change its characteristics over a

period of time. Two major types of experimental interest in steady-

state behavior have developed. One of these may be termed "descriptive"

and the other "manipulative." Experiment I is a purely descriptive

study in which a set of experimental conditions are maintained over an

extended period of time, providing an account of both the stable and

the transitory aspects of the resulting behavior. This form of re-

search is fundamental to the establishment of behavioral control

techniques, and of baselines from which to measure behavioral changes.

The data yielded by such an experiment do not relate an aspect of
behavior to several values of a manipulated independent variable.

Rather, the resulting curves show some aspect of behavior as a function

of time in the experimental situation. It is the characteristics of

behavior in time, under a constant set of maintaining conditions, which

are of major interest. According to Sidman (1960), the descriptive


investigation of steady-state behavior must precede any manipulative

study. Manipulation of new variables will often produce behavioral

changes, but, in order to describe the changes, we must be able to

specify the baseline from which they occurred. Otherwise, we face

insoluble problems of control, measurement, and generality.

A major problem faced in experiments involving the manipulation

of steady-states is that of deciding whether the behavior in question

has stabilized. According to Sidman (1960), there is no assuredly

final answer. He states that "The utility of data will depend not on

whether ultimate stability has been achieved, but rather on the reli-

ability and validity of the criterion. That is to say, does the

criterion select a reproducible and generalizable state of behavior?

If it does, experimental mar,;pul r.i ..: n of : r.3 -: r. r. a: .J r...j b.,

the criterion, will yield da a r.0r0t "..i .: r i., I nrd .j,1r, .i. : :,l.. r.)

other situations. If the s- .m ,,-: r.: ,r rior, : i .. u o,

failures to reproduce and to iepi iz r.e :.,: r.ut r: i i, trh. L p ,: l

findings will reveal this fact

How does one select a r.E i.,-: r r .rit i I i,. i i :. n.

to Sidman (1960), no rule to f,,il.w, fr. L trh critri .' :r, ,ill .i.-;.r..i up-

on the phenomenon being inve:tcir- ~; t- iup:r tr- i. ..i i .:.r : pf ,r-ici.rr,

control that can be maintain. . I .er- cr iFi ,,i i.:,. t .rm .,u.i. -

steady-state behavior are e>.ter i u:eruit L, icCJ.i.r

over an extended period of .iT.-. ui rr n. char .- in ri. e.-.' ,,,enar,.

conditions, it is possible -o ,l vI r.iiTm- ;, i. :. i,f

stability that can eentuall, -be r :ir.r.inr l: ia irr.'trion ci r then ,ie

selected on trne c 1 :. of the:: :,r: ar.icr: Th. A. e l um O:f tli

criterion choscr, .:r, L.b- .o iriir .. .a t., r.h c.r,1arlirnL;: I , t hi L:ul il ]

data. If the steady-state criterion yields orderly and replicable

functional relations, it may be accepted as adequate.

Procedure for Experiment I. --Judges were presented with 20 MMPI

profiles, 10 belonging to homicides and 10 to non-homicides (base rate

of .5). They were asked to discriminate between the profiles of

homicides and non-homicides. Each judge was presented with the same

set of profiles on a daily basis until stability of prediction was

reached. The criterion of stability was orderliness in the data.

Experiment II. --This experiment consisted of a systematic

replication of Experiment I plus the study of the effects of adding

new information on clinical judgment. Four phases were involved:

Phase 1 -- Systematic replication of Experiment I. According to

Sidman (1960), the soundest empirical test of the reliability of data

is provided by replication. The application of continuous and direct

recording provided a unique opportunity to attempt to replicate the

findings of Experiment I.

Phase 2 -- Phase I was used as baseline data to study the effects

on clinical judgment of adding new information, in this case Rorschach

protocols, to MMPI profiles. The research previously reviewed (Gold-

berg, 1968; Shagoury and Satz, 1969; Moxley and Satz, 1970) seems to

indicate that as more information is available to the clinician,

judgment accuracy increases. The present methodology provided a more

powerful technique to study this phenomena on a daily basis instead

of the previous one session studies.

Phase 3 -- This phase was identical to the previous phase except

that biographical and EEG data was added to the existing information.

Phase 2 was used as baseline data.

Phase 4 -- This phase was identical to the two previous phases

except that a summary of the findings of a multivariate analysis on

the data as found by Shagoury (1971) was provided to each judge to

assist in making his judgment. Phase 3 was used as baseline data.

Orderliness of data was the criterion used for termination of this


Procedure for Experiment II. --Phase 1 -- Judges were asked on

a daily basis to predict homicides from non-homicides using a new set

of 20 MMPI profiles with base rate of .5. This phase was discontinued

when stability was reached. The criterion of stability was orderliness

in the data as well as comparison with stability in Experiment I.

Phase 2 -- Judges continued making their daily predictions. In

this phase, the MMPI profiles of Phase 1 and the appropriate Rorschach

protocols were utilized. Orderliness of data was the criterion for


Phase 3 -- This phase was identical to Phase 2, exc't rii-, )iuj -:

made their daily predictions with the addition of biogr.phi'cIl itj

and EEG reports.

Phase 4 -- Judges continued making their daily pre.ictror.:. bur

this time a summary of the relevant findings, as found :., 'ulri. rijte

analysis on the previous personality, biographical and .:loloicil .jtij,

was given to each judge (Shagoury, 1971).


The measures used in this study, frequency of correct predictions

and frequency of incorrect predictions, were plotted on Standard

Behavior Charts (Behavior Research Co.). Plotting linear data on a

log scale provides one with a picture of proportional changes in

behavior frequencies rather than absolute changes (Koenig, 1972).

Information that the frequency of occurrence of a given behavior has

doubled or halved is considerably more valuable than information that

the frequency of occurrence of the behavior ias changed by one

arbitrarily defined unit.

In order to understand the present results, it is necessary to

briefly familiarize the reader with the Standard Behavior Chart as

well as the current procedures of data analysis.

Chart scales. --The horizontal dimension across the bottom of the

chart represents calendar days. Each chart runs for 140 consecutive

days or 20 weeks. The vertical dimension up the left side of the chart

is the scale of frequencies or rates. The unit of measurement is

movements per minute.

Record floor. --The record floor is the lowest measurable per-

formance frequency other than zero. The record floor is found by

dividing the number of minutes in the time sample into one, the smallest

number of movement cycles that can be observed. The record floor sets

the lower limit of the sensitivity of the chart as a measurement system


for each day. Below the floor is an area of record blindness. The

symbol of the record floor is a horizontal dashed line at the computed

level of the floor for a given day.

Celeration. --Few statistical measures are available for des-

cribing continuous changes in behavior over time. Therefore,

researchers interested in continuous observation and recording of

behavior have developed several new measures for this purpose (Koenig,

1972). Frequencies displayed on the Behavior Chart are usually either

5.:.:.r il i 1 i ,r I ) ir .:.:.: I ri j i rc -n 1 i i : k i ii .: C e,: : i. l i .: I : i .: n

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; k11. r, l ,.ed i ,: -Jth : f L r,]-. I .I ,I ,f L. : f i :r .. J i r. C i1r,,, .

:ir.; rh 1., : i .: ,qru, : j.r r i r r.o. .',: '.. .L,-

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,,iej:t, re. it> p :r. '.r:c.. ] ,.n l,.. ,-; ,:.j. r ,- i [ ,: ,i, i .r ic, ,,[ h I : ..:11 irlI. i ,:,ri

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Th. .i::ul ., 1 i i,:. 1 d;. filr 1 r, tr, t, reer iv ue i.,
.: i ri i i. i : T c:.:uL i;., ru j'E u,:, fj ,L Ic .:r,

.luJ, I: i CrI .l 'f F r -jrir. [ r l i .ir T cr r ti fr ) r,
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indicates that the frequency correct is equal to the frequency in-

correct. A value less than one indicates that the frequence incorrect

is higher than the frequency correct, and a value greater than one

indicates that the frequency correct is higher than the frequency

incorrect. The reader might want to convert these values into

percentage (e.g., x 1.0 = 50%; x 9.0 = 90%). The accuracy ratio

celebration measure provides the opportunity to compare the celebrating

effects of adding new information to the clinical judgment process for

each judge as well as across judges.

Figures 1 through 6 present graphically the daily accuracy ratios

for each judge. Table 2 shows a summary of the accuracy ratio celera-

tions per phase for each judge. Inspection of Table 2 shows that the

accuracy ratio celebration coefficients ranged from i 1.56 Movements

per minute per week (M/m/w) to x 2.27 e/m/w. Figure 7 shows a graphical

summary of the accuracy ratio celebrations across judges. Overall, there

was essentially no acceleration or deceleration of accuracy over time.

There were four exceptions. Figure 3 shows that P l's accuracy

accelerated x 2.27 M/m/w in Exp 1 (MMPI's only) and x 1.6 M/m/w in

Phase 2 (IIMPI's + Rorschachs). Figure 6 shows that I 2's accuracy

accelerated x 1.51 M/m/w in Phase 2 (MMPI's + Rorschachs) and decelerated

a 1.56 M/m/w in Phase 4 (MMPI's + Rorschachs + Biographical Data +


Accuracy Ratio Frequency Multiplier

To measure the effects of a new procedure on the first day of a

phase, the frequency multiplier, or step, is used. The frequency

multiplier gives a measure of the increase or decrease of frequency

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correct or frequency incorrect the first day new information is added

to the clinical judgment process. It is a comparison of the last data

point of the old phase with the first data point of the new phase.

A frequency multiplier of x 1.0 Movements per minute per day (M/m/d)

indicates that there has been no increase or decrease in accuracy with

the introduction of new information to the clinical judgment process.

A step of x 2.0 M/m/d indicates that accuracy has doubled with the

introduction of a new phase. A step of 2.0 Ni/m/d indicates that

accuracy has halved with the introduction of a new phase.

Figure 8 shows graphically the steps for each judge as new phases

were introduced. The measures for each phase from left to right belong

to: F-1; F-2; P-1; P-2; I-1; I-2. Table 3 presents a summary of the

accuracy ratio frequency multipliers. Inspection of Table 3 indicates

that the accuracy rjti.., fr-.qu-n.: mAultirpli;er rns-.a froi-m = ?.0 1 'n'd

to x 4.0 M/m/d. '"re:. :r. '...i ure: t l on. i _. Figu.- : in...d c a ;

that the maximum a.:.:~al- Trr, .-t :.: a.r. .... r. in r r .. .J i.t :., .:.

Phase 4 (formulas:. Tri, i1.1D, n ,:.f n .e- f.:., .:hi ) c.:. .ju .

overall the least cr, is. 5 ..:'u c, .::p[ i'.o, i : h,::- _CCurc,

decreased 3 M/m/ Tr,. j.j ir 1 :,'n I, n.: I in- .et ,i" lil i :i .

well as the additi':'" *,f hi :_ It i.: ~ c.i', i *l.j. .r':Ju,.:3 r :, t:

momentary decrease. in .j:ij.:.

Record Fl::r .el.-r: i :, n Cf r ,, .: c.

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therefore, a meast ef i'. n.: e,:c rd fl .:.. :elr t i : :., ,: t.

judge are located ii' I..'"r.di F.. TI t.l J :h, ; m u r i ,i f t .- r .' o d'

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floor celebrations per phase for each judge. Inspection of Table 4

indicates that the record floor celebration coefficients ranged from

1 1,21 M/m/w to x 3.22 M/m/w. Figure 9 shows a graphical summary of

the record floor celebrations. Overall, there was clearly an increase

in the efficiency of the judges' daily predictions. The maximum

acceleration in efficiency (x 3.22 Ti/m/w) was obtained for I 2 in

Phase 4 (MMPI's + Rorschachs + Biographical Data + Formulas). The

maximum deceleration of efficiency (+ 1.21 M/m/w) was observed for

F 1 in Exp 1 (MMPI's only).

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. :u..3 =r , .:. rLr.:,: J r ,:, Ir .:. fTu ti pl i r, :i,- Ir :,:.. I .: i.:. ,:, r

T.=t-Li '. :Cj .; t ': r.C r, i .,:...r.1 i"l,.....,r' fri" ,- u r,:, frn iCifii r: ("r:i[ ._l

fr',:,. i ..' l l I [. In 'r .1. Fiq jr- 1.1 irirJic:.r : that in .=1i

iC .ip[ .1. l t r. .1:-.. o r .'f r y i *crc 'ai' n o u:.) iF i, t r3 wr t

r iu:r i *:r, in .ffi r,.- . Tr, : c :.t i nr, :'.: c .urr J ir. F ii r-in t -

dr [ I L.:n j_ f th:: I, i..r' i : 1 di ti f.i, :h p.r O C r.I a 1 t t ,

:i rii;'rcv [ in. .( r f I. II m -l in -: .ct .i n ..f F -i Le 10 t t

Ctet L .r i-*i ..:rall Jr.i a i; 5 in P1 I -.. ric r 0a -Jrr w1 iuth I....


I i II



ao -




0 00 0, -L) 1 0 L
00 0 to -

I I i I I -- I I I i I *



o <4.


w r
0 L 0 0

0. I D

Q.*.i _


0 L-I 0


-cN kG



S -c


o- a

u- a1 N tO

o a

U <

1 C

Q- m

to co C- (D
4. .4. .4. 4.

om a C
** ~ 4 *4* .4*

C 0o o

3- N i-
A4. .I. *I* .4.

0 t ON

2.4. ++ I I


-~u IN~ -
IlU Oa :~

addition of Phase 2 (Rorschachs). The second most noticeable decrease

in efficiency occurred with the addition of Phase 4 (formulas).

Frequency and Record Floor Growth Ratio Effectiveness

The growth ratio is used to assess the relationship between two

celebrations. In the present study we are interested in the relation-

ship between celebration correct and celebration record floor as well

as celebration incorrect and celebration record floor. In other words,

the growth ratio provides a measure of the relationship between the

celebration of the time spent in making the daily predictions and the

celebration of the daily correct and incorrect frequencies. The growth

ratio is independent of both the initial frequencies and the two

celebrations. It is therefore, a measure of effectiveness. A growth

S i I.'. O r 1.1.r1 i i'..J r : j rh i rh ? .'. i i r .:.r f'. i .r .:.;-r I .: r ri.,n r. -

c .rr ', :. r F .. r ~ : i .. r r. r i .: r. ., ., e h qr r. h l, .r

r'c. E. ) r or.: Ir..: ir. j r -.:r r :r. r.:f c.; I r i r:. .:f I -. r .. i i .:nr
i n ,: : r'r. r i: ,jru r r : w r r i i:.r , r. s ,d T :, ii r, r i :

i ir [H ,- ,*.:.1 ic i ,r i p,-. -r n1 c:rr ,.: r r i r .1:. .

T i , : .'r,,:,; : 6 r ,,: T.;. u,: r ,:u ".,r'r.-r r. r'.J r.,:.r'd l' *,.,:.r t.lrt',i, [ nr r ntr,-,"
p.r F ,i:. rr ,.:,h iu.C. in: -..: :. .L. T I ,bi. :' rC .i r .: ,I 4 C

T,,. i t'. r ,nr ,.,: [,. ... t'r.C. -. r:l. ,,, r,, rh,:l r ,c .' -,,-i o t ; r r .i. : 1,

L ., I l,',i i. rd i.-4 *r fl, :. rl ;i : r ,,. 'liLh: ). i t: a i nr l

S*i '', I [ : Or' r -. i;, :- : ( l'r i l C.r:_ rn ,-h: I frn.1 i i n

f ] ( i.1 + :,r:.:h ,.-I, : I_,'C ,.,r +.r cl, i I.' t F .,r'TIjI : I.

0 01

o ,

-m 0
-I a)
C- -a)

an 0

U- -

aO 0 C

0 0 1 0
o o o

co o

CO 0

-~ C'~ C'

Table 7 shows the frequency incorrect and record floor growth

ratios per phase for each judge. Inspection of Table 7 indicates that

the growth ratios ranged from .56 to 1.32. Overall, there was

essentially no difference between the incorrect celebration and record

floor celebration. There were five exceptions. P 1 obtained a

growth ratio of .56 in Exp 1 (MMPI), .78 in Phase 1 (MMPI's only) and

.74 in Phase 2 (MMPI's + Rorschachs). I 2 obtained a growth ratio

of .84 in Phase 2 (MMPI's + Rorschachs) and 1.32 in Phase 4 (MMPI's +

Rorschachs + Biographical Data + Formulas).

Accuracy Ratio Total Bounce Variability

In order to assess the variance around celebration lines on the

b[eha i.,i.r Ch,ir[. t ,,,n (119 1 h,; .:,.;c:l p d rthe rt l bcjrr.. ,' w im ure .

cO finnd trhe tctil bounce ine i: .lr,-n ,r parll .l to rh. c lera ticr'

1 ini Lrir.u.j.)h [h- frequ.nrji, rirthe: ar L,; t r.. Trh n irie i; .1l r n

p rall l tI o th .:c l. raj tior lir: I .h.rth :-u h Ith: fr.qu,'nc. far[th;i L t,.lo:i

it. There .li tnce be ?t.., [thc:-. twl Ou r I int. : pr.:: : .1 a- ,i rael ,:.

jef ini: lh. [.:.C i] t.:.urice ir..jrd the c.: ier .,r l ir. l ..ri ig I, ]'n '2)i

h a h.wn r i that r.h prc.pc.rtli n' .r rl ir: i 3Irounr. r.r :t [r, ihr. liri of

ieler-tir in fre'r uncir.: u:u.a l, r.-m. .rn: cor; [ r regrardle of the

,al f r.he frle. 4 r,: C :. Trhiu:, [ .1 t-.: .unC I: uIei1 .i a iel.iE ure of

hri. ; .:u .n r t. r i tL Of t[h. 1 Il, pr'dC t itE .' ; pr.:ert.; :ucTT.j Eof [Ih ai ura, r'tiO [totr l tOunce per

phiL:e for e..:r, jUij rIn:ecti ori or f Tt.le r.:',.E : r tha ture alI

tiour..:, r.anqed fo ,n I ) to I I 11.': Th.: hi.jhE C total t.,uncEl of r

..aL t.r. in..l t.. i i r.r the aJd i r icn ...f Ph, a 2 I(PC.r.:ihach ) .

tO 0
) o a
n3 ,

O 0 a,
n3 i

'0 cr0

0 0

D 0 0 C
cn o

0 0 0 0
03 C)

r- oa a

So -

0 o C. cJ







r- 0

I- a





1 i






0 S

cC 4



o a
o oM
m c

x x

o a
o N-

o o ao

. -
xx x x


i i i :

H- * X X


A total bounce of x 1.0 indicates that there is no variance around

the line of best fit. P 1 obtained a total bounce measure of x 1.0

in all phases except Exp 1 (MMPI's). In comparison with Koenig's
(1972) data 1, the present results indicate that the accuracy ratio

total bounce for each judge is considerably below the average. This

can be taken as a powerful indication of stability of accuracy in

daily judgments,

1. Koenig (1972) investigated 13,941 human behavior projects
deposited in the Behavior Bank and found that the average
total bounce was x 5.9.


The present study provided the first experimental application of

continuous and direct recording of operant methodology to the clinical

judgment process. This novel application attempted to provide initial

answers to four questions: 1) How stable are the daily predictions

made by judges?; 2) How does the time involved in making a clinical

judgment influence accuracy?; 3) What is the effect of an increase in

available information on clinical prediction?; 41 What is the effect

orf e ..r'i nc;- 1 .. i or, .:1 nical J;,iu r..ntL Th, prr nt ri'uits re

l:h :ucc:.,l i, r.hin r. t e r'r ,T. u,,ork ,:f tr,.:e q ;t r.n:

Thie re ul..: it .',-n:r:tri[,: 1. nurit.r fli t'f r r, r r,-i:.tte .u In :o'r..

c :-'.:,, i .-t :ff. :t r cr O, r rr ri 1r- e.'u:L r 4-.r:hi; r'mr i h, iT, ,

trie o. r il i ,.i i..::ur .:, I-...1 ,i.r m t j : Ht'owe..F.r, :o eri of t~e

findrin w-r'i. jrL i ipei.r,-j, pirEi.:ul.rl rth- u.rali rn ,lIgible effc r.

of .j.d inr Lo r.h, c:liricail juJ.l,r. pro.:.-, Ine r. IEl

al'o I c 'l rn:.i t i -1 i ru T C .-r f r, i.. ff-.C Ti-.:. were: 1) Th. .dir.:r.

ajn i t..:i[i i r.,pli: i.,ir, ,.ro-:. rn itii n ju ge : r. th; at ilit,

Of r.h. i .il pr .Jic ir. ,n. .;r.: : r:. -:, -. Th- r-plic:aci r o-f he

ircr.3:.. in effi.:i.rnc,.u pr i: for ,.ea jujge: rand, 31 Trh.

r.pli d:atI-rn of r l i.C of is-nti l .ji .ff.r..hrn. : bcti .n .: .l ritIln

fr; u-nrii.-: nr .:r ei-riti:on r'cor. floor. Thr.,' finlingl prom ld- a

sC5rtinL point for tfturw re:. r.:r.i n clinicAl jui.T nrr ',h1 ,:-

t.,ia[i c r. .] ripllr, ir.i.n of Lh pric .-n[ E :tu]., ill prf, oi.j


reliability and generality of these results.

Stability of Daily Predictions

It has been eighteen years since Meehl (1954) stated that "Pre-

sumably some kind of longitudinal study is needed to find out whether

and to what degree the 'good' clinician is stably such, rather than

being merely the momentarily luckiest fellow among a crew of equal or

near-equal mediocre guessers." The present study provides a partial

answer. The results indicate that in all cases (judges and phases)

except one, the individual predictions were stable. The exception
was I 2, with the addition of Phase 2 (Rorschachs). However, since

the accuracy level for most judges across phases was 50%, these results

have to be interpreted with caution. A stable 50% accuracy level is

easy to maintain. In our sample of judges, only P 1 (See Figure 3)

maintained stability above 50% accuracy across phases. He was the only

steady "good" clinician that could be identified. Inspection of each

chart indicates that F 2's predictions (See Figure 2) in Phase 4

(MMPI's + Rorschachs + Biographical Data + Formulas) were stable above

50% accuracy as well as P 2's (See Figure 4) in Phase 2 (MMPI's +

Rorschachs). These findings indicate that, in the present sample of

judges, when a judge was identified as "good" (identified by con-

sistently predicting correctly above chance) at least in one phase,

his predictions were stable across that phase. Future longitudinal

research should identify these "good" clinicians before attempting

to replicate the present findings.

Efficiency of Daily Predictions

The use of frequencies in analyzing judgmental accuracy provided

a most sensitive and natural measure of efficiency, for it considered

the amount of time spent in making a clinical judgment. The present

results showed that, overall, there was a clear increase in the

efficiency of the judges' daily predictions. It was also shown that

efficiency decreased when new information was added to the clinical

judgment process. Inspection of Table 10 shows that the maximum

decrease in efficiency was obtained with the addition of Phase 2

(Rorschachs). This can be taken as an indication that the integration

and interpretation of the Rorschachs combined with the MMPI's required

the most time and consequently the maximum drop efficiency. It is

interesting to note that the maximum decreases in efficiency in Phase

2 (MIPI's + Rorschachs) occurred, in all cases (judges within Phase 2)

except one, with the medium (P 2) and high ;.rinr.cel i i;

I 2) judges. These judges had knowledge in tr. ir tirprE~t!in of

the Rorschach; therefore, a decrease in eff`'enir.: i: wn ind i.tflr.

that they were making use of this knowledge. Th.n :.,)nd iT,:t n.:.tie-

able decrease in efficiency occurred with th in o..f u nr. J

(formulas). Once more, the maximum decrease in iic.:in. or Curr-.3

with the medium (P 1; P 2) and high experi-rc..j (I I, I 2)

judges. It seems like the least experienced jud).e: (F 1, F .;,

presented with a novel set of information, dic'ijde not to, pFr.a Ic.uc

additional time in attempting to integrate trn: w,, in.-.rrm.raion.

Effectiveness of Daily Predictions

The effectiveness ratio (growth) provided a measure of the

relationship between the celebration of the time spent in making the

daily predictions and the celebration of the daily correct and in-

correct frequencies. The present results indicate that there was

essentially no difference between either the correct celebration and

record floor celebration (See Table 6) or the incorrect celebration and

record floor celebration (See Table 7). This indicates that, overall,

most judges within each phase expended less time in making their

daily predictions as the phase progressed, but their accuracies were

uniquely stable within and across phases. That is, they became more

efficient without a concomitant increase or decrease in accuracy.

Two judges were the exception. P 1 (See Figure 3) increased in

efficiency and accuracy throughout Exp 1 (MMPI's) and throughout

Phase 2 (MMPI's + Rorschachs). I 2 (See Figure 6) increased in

efficiency and accuracy throughout Phase 2 (MMPI's + Rorschachs) but

decreased in accuracy and increased in efficiency in Phase 4 (MMPI's

+ Rorschachs + Biographical Data + Formulas).

Levels of Information Across Levels of Experience and Accuracy of

Daily Predictions

The accuracy ratio celebration measure and the accuracy ratio
frequency multiplier provided the opportunity to compare the celebrating

effects of adding new information to the clinical judgment process

for each judge as well as across levels of experience. The present

results indicate, in general, that there was essentially no

acceleration nor deceleration of accuracy over time within a phase.

The results also indicate that across phases, the maximum accelerating

steps were obtained with the addition of Phase 4 (formulas). The

addition of Phase 2 (Rorschachs) produced overall the least change

in accuracy. The addition of Phase I (new set of MMPI's) as well as

the addition of Phase 3 (biographical data) produced the most initial

decreases in accuracy. Individual differences were observed across

phases between judges. These individual differences are discussed

according to levels of information.

Exp 1 MMPI's Only. --Most judges predicted at a 50% accuracy

1'>.-.1 (Fi-.,urjr 1 t.rou.)h '.l. Tr.r. c .- r.e t[wo e, C pri r,:, t.. h c:uL 'rri .

wi th mT.-iiu,. e rper' r ic, d jud. : P I I ,. F .)ur.- 2) nri '. e.: .e r, :

accuri' : r, un the : c:...1r. ,l i fl rri : [E 1 Ti r.I rh r, i ) U r. i .) r rn irp.J

3[taL.e till th .:n.j ,f tr,: prha::. F ( :. F i.u,.: 4) pr.:dict d .:or,-

:i: tenr. l, It.elow .:,rce r,.J rhi- ,cur' :., .lii.1 no:c c.:.ler I t, ;.:r- r. e

Prr :,e 1 rtl i': 'nrri, --l.::.t juJ : pr ,- 1 i ar. C ~':. accura .,

lI. > l IF:ur.-: I [ihrou..h i Trh..r a: n .i .-.ti rr. P I (i.

Fijurie 31 predic:r;. s i 5 '. j,:.:uri C .. L.i O:n fO:uir r: rhe fie Ia,:

in rth: : cu'rd ,-el uf rri: i h,:e. T ble. E in-lici[ e: that (h. intro-

, u:, ,, I-,' -i,- 1I pr'dluc-dl n.T er r. .lerre, :e ir aCcurC : ilr brh

Of th. rc:.rn-. (f I, F ): in onre ilK.ii. um.-e p. riern.:e

jud.: (F II ; an.l iri, in, il. ,-.- p rire e-l ju .g (i I). .lr,:c tt.,-re

M; ,-,,.; ,.,ill n r,, c.l, 'ratiO1 in ace 'ri:y ir, ['.i I pra (.Ee Fliure 21,

n :.',. i-'ecr: -ce ri,:.' p r,' narri r .

Phase 2 MMPI's + Rorschachs. --Three out of six judges pre-

dicted mostly at a 50% accuracy level (Figures 1 through 6). The

addition of Phase 2 (Rorschachs) had no initial effects (See Figure 8)

nor celebrating effects (See Figures 1 and 2) on the non-experienced

judges (P 1; P 2). These judges predicted mostly at a 50% accuracy

level. The addition of this phase produced no initial effect in

p 1 (See Figure 8), but his accuracy increased above 50% on the
second week in this phase. P 2's accuracy increased initially with

the addition of this phase (See Figure 8), and remained at 60% (See

Figure 4) for the rest of the phase. The addition of Phase 2 had no

initial (See Figure 8) nor celebrating (See Figure 5) effects on I 1,

I 2's judgments became unstable with the addition of Phase 2 (See

Table 8). The initial effects on I 2 was an immediate decrease in

accuracy (See Figure 8), but accuracy accelerated (See Figure 6) with-

in the phase to a terminal accuracy of 60%.

Phase 3 MMPI's + Rorschachs + Biographical Data. --Most judges

predicted at a 50% accuracy level (Figures 1 through 6). With the

non-experienced judges, the addition of Biographical Data produced

an initial decrease in accuracy for F 2 (See Figure 8), but no

effects were found for F 1 (See Figure 8). The addition of this

phase produced an initial decrease in accuracy for both of the medium-

experienced judges. P l's predictions (See Figure 3) remained

stable at 60% accuracy and P 2's predictions (See Figure 4) remain-

ed stable at 50% accuracy. The addition of this phase had no effects

on I 1 (See Figure 5), his predictions remained at 50%. I 2's

predictions (See Figure 6) decreased initially from 60% to 50% accuracy

with the new phase, and remained stable at this level.

Phase 4 MMPI's + Rorschachs + Biographical Data + Formulas.

--The addition of this phase produced overall the maximum increase in

accuracy of all phases. The addition of this phase elicited an initial

increase in accuracy for the two non-experienced judges (See Figure 8).

F 1's accuracy (See Figure 1) increased to a maximum of 70% but

decelerated within this phase. F 2's accuracy increased initially

(See Figure 8) to 60% (See Figure 2) and occasionally to 70%. The

addlit ion .:,f1 h '. .1 I f.:, i r L : ) pro...ucl d t h. r i.Tiu', , :j in

a:C 'ji a. u, Ef r t r..3rn--.p ,rri cn.: jJd..::. F.,:r F I rt...e Ii.)aj r.: ) the

'ddii r. ior i, t 'h ',nj1 a: E.1r,: lu. 'C-d ri, h r an irilLitil t.:(. Irn ..:iur aIC,

( Fi ur, -) n:,r' 3 CEl; rj 3 .rn .J f.', [. Hi: pr+. 3idr i -iI: rt iT. i ri.i.3

(:t'"lF .it f Jc:uri.:,. Th I a- f cc 'rr: d fr : C 2 (2H O ui r. 4),

ut in trill *:,: h i: ri pr .-ti ,ri: r Ti, n, .j :[, .l e ,I[ I i:Curi ,.
F i- .lur'- L h r,,Ii ,: a l :, r, .j t or : ri, ,r, l.3i r Jn ,.', :,jI ,:) E.:,I, .ju:3C. '
Figure Ini..j.L. L; hr r..: Ildition cf fhe 'frrnal;.) Jpr.duced

rno ini ] l 'ffe: ,:t n 1 ,ri jr. incri:r. in i:,:C r i ,.r I r .

I I': i.:C urI:., r r.: r.~.4 l ,on: nr I t rt .:ee fLui- 3). [ F' :

accur.c, *l.: d ] r i d f'r:'i )r. iniL ,] i '. C r I c o t, rnlrir l .i 6

.iCl:ur 3 (:,.-a F jur r. l.

TO :u.Tir3ri ,r h,: i l i:,or. :f r n w I i n l: 1 Inr'. nr'i r to L he il p.r.:.c : d i nJ o c t irti nI .-. :r i:E i: : r, .:, i.: r', :,:.

pha: Tre :i r -i, Li C .r-ti : w.are Lhu r'e-I i o: n i f the ilr, r.,a.r in

.: w'C:u I :O irri 1 T r, :C .' i ) fr'r bot non-.-.: ri n.:e~'l :judjl,: I.F I,

F with th.e iad it i i:n o, f P I. I riu1.l. Ai ..:, wit h th.: Ij.ji iLi0

oi Pri.i. 4 I Ir.ijII ) i I ccurca., InCrCdE d to a m aid um iurif u '.I.

Lith t LE riTair..i ir.) cf 6, I I : IrF1 irtr [ing t note that lth-

two non-experienced judges increased in accuracy from 50% to 70% with

the addition of Phase 4 (formulas). Shagoury (1971) found that these

formulas predicted accurately 83% of the sample of homicides. It

seems, from these results, that non-experienced judges (F 1; F 2)

tended to ignore the actual test protocols and looked for the relevant

cues provided by the formulas. The same could be said for I 2.

Nevertheless, no judge approximated the overall accuracy of the


On the basis of the proceeding findings, a few general comments

can be made. Some of these comments may, at present, lack generality.

This study is only a first attempt to apply the single-subject re-

search methodology of experimental analysis to study the clinical

judgment process. Future replications of these findings will provide

the final test of the reliability and generality of the present data.

The present results are in conflict with more traditional studies

of increase in levels of information. These studies (Shagoury & Satz,

1969; Moxley & Satz, 1970) found that accuracy increased as levels

of information increased. It should be pointed out, however, that

the kind of information presently used was in part different from the

two previous studies. In these two studies, the information used was

quantitative (Z scores; base rates; conditional probabilities; etc.),

and in the present study, some information (MMPI's; Rorschachs) was

qualitative, and some (biographical data; formulas) was quantitative.

It is interesting to note that, in the present study, Phase 4

(formulas), which was purely quantitative data, produced the maximum

increase in accuracy. Future applications of experimental analysis

to clinical judgment should use quantitative data only, so that a

better comparison between these studies can be accomplished.

The present results indicated that the "good" clinician

(identified by consistently predicting correctly above chance) is

stably "good" on his clinical judgment, and not merely the "momentari-

ly luckiest fellow among a crew of equal or near-equal mediocre

guessers" as stated by Meehl (1954). This finding was replicated

across phases for P 1, the only "good" clinician that could be

identified, as well as within phases for F 2 and P 2. This find-

ing is intriguing and warrants the need for more longitudinal studies

of "good" judges.

A d1':.:.:.' ra.' r. ri :jl r. ',j : tree .:. er'rc ie ir..-, l w I a ur c f T)o: C

j.,J'd e: in the pr:re:. T i".T le. ha.:.ur.. 1,19l'1) .:.r,.l tr, r l i:-

criiTi'nant frn:[ion r ,n.i l,:l: dl: rinir.ra t,. i C,'ur i. ( ,f rne [.:.[ il

.ple. l t ju'liei .Jil.:r A riIrst.dJ iet .,c'ri n..i. e; :, ri rnor.-rn:. ,-i.: ..'

t l.' )CCur ,, c h the :,;:[ jij.m. e r,. in.: n c i i 1 r, .:.f )

c'Cur Tcy. A ruTL.r :.' in terrpr[t n : nl [.: ,,; pr *.i. 3 [tc F.lain

th:c i:ult: i'. r L: rh Ln .Sniotr .pl :ofi C5s: -r.:n,:.:en c.:iuld

h -'S e b..n tr.h. :ri. .i ':.: : he. dI .:' lt fur..:t.'r, jrn., thur s,

th : Ii-:.: riff.:l u r.j .3 i; ic iiiri e. A :-urind p:, ti i it, : teri

(peci l tr ning ri r. t ri.i J, t C ..nL. in the .'i li .]b Ini f orm.ls1:.orn

[.: S'. i .;.cu'lte : i : rtn, i.:.n. Thl: p.:. :: i tL ii ': in[ -. L.,

[E *E r.: Er i i.:.r. rr, |r ptr1tnct I '. El haI a nc. noi c i.:E 5 -. f t: r ,n

l'J ,Cur' :,. F uture ri'; .:r:h : h ulJ tL:[. Chi t : '.::' Iti ..

u:. in,- f c .: ci11, tr a i n Ji] d'i: E[.) .' :l'cr', r. t.-t zerrr

h',.mic l nl rn.:.-riu-.:.ii.j, e I cj. pr rt:,t:O.:ol: n.j l.ts [th. 3.:.:Ur' .

their' preliccti:r: ic h 3 nr. .:jrpl A th;rd nl wri t threi:tenin:r


hypothesis, previously proposed by Meehl (1956), is that clinical

judgment is not one of the talents of the clinician and therefore he

should relinquish the role of clinical judgment to the more accurate

computer. Recent findings by Blumetti (1972) provide the most

convincing argument against this proposition.
Most importantly, the present study brought a new sample of

human behavior, in this case clinical judgment, under precise and

continuous measurement. This was accomplished through the uniqueness

of the Standard Behavior Chart.





Phase I

This is a research study investigating clinical judgment. You

will be presented with 20 Minnesota Multiphasic Personality Inventory

(MMPI) profiles of inmates at Raiford State Prison. Ten of the twenty

MMPI profiles belong to men convicted of first or second degree murder.

The remaining ten profiles belong to men convicted of crimes against

property; as breaking and entering, robbery, forgery or arson, but

not of any crimes against the person (as assault). (That is, base

rjt, c .51.

four tall a: rto Er, ro .11:cr J ir.aL [ erv. &rn t[h iIna pr.-,f l -1 :

.: hl l Li I oF,-i o r.lh o l':iT'i c i -'r :.up a" ri ,ricr- d) r,.:, ]- it ; pusi i'le

to corr c r.* l c ;i f, .)il il prorlil .. [ i: ri., [h. i r ,,ur par -

dicticn .i i i in : TiC j. L r. h ip u [:i brto r-r:r.jnrl d :.r, :p .:[ Of r, e.

,'. ,i ppli-i t. p: ir clinical


Phase II

In this phase you will be presented with 20 cases of inmates

at Raiford State Prison. Each case in the folder has the appropriate

HMPI and Rorschach protocol. Ten of the 20 cases belong to men

convicted of first or second degree murder. The remaining 10 profiles

belong to men convicted of crimes against property; as breaking and

entering, robbery, forgery or arson, but not of any crimes against

the person (as assault). (That is, base rate = .5).

Your task is to try to discriminate between the cases as to which

belong to the homicide group and which do not. It is possible to

correctly classify all the profiles. It is hoped that your prediction

will in some way help us to understand one aspect of the decision

making process as it is applied by psychologists in clinical settings.


Phase III

This phase is similar to the previous phase except that each

case in the folder has the appropriate MMPI, Rorschach and biograph-

ical data. Ten of the 20 cases belong to men convicted of first or

second degree murder. The remaining 10 cases belong to men convicted

of crimes against property; as breaking and entering, robbery, forgery

or arson, but not of any crimes against the person (as assault).

(That is, base rate = .5).

Your task is to try to discriminate between the cases as to which

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Phase IV

In the past several weeks you have been making decisions based on

MIPI, Rorschachs and biographical data. The purpose of the present

phase is to provide you with the optimal salient findings (Shagoury

and Satz, 1971) of the statistical analysis, performed on the 60

protocols of which the present 20 is a random sample, as found by the

computer. No variable by itself was discriminatory. However, when

the data was subjected to a multivariate analysis, the following

variables in some combination (i.e., linear) were shown to correctly

classify 80% of the sample (only 7 homicides and 3 controls were mis-

classified, yielding a valid positive rate of 70% and a false positive

rate of 10%).

These are the salient variables as found by the computer:

Variables Confidence Value (T)

Goldberg Score 7.51 *

M Responses 17.74 *

Total Rorschach Responses 1.70

Percentage of Human Content 4.46

Percentage of Minus Responses 20.72 *

Percentage of Whole Responses -28.50 *

Sum C -8.60 *

Variables (Continued) Confidence Value (T)

Total Pathological Content Responses 8.41 *

I.Q. -6.33 *

Grades Completed -4.27

Prior Felony Convictions -4.50

Prior Misdemeanor Convictions -3.13

T 12, 47 5.44, p .05

Summary of Table: The homicide group showed a higher Goldberg

score (+7.51), more M responses (+17.74), a higher percentage of minus

responses (+20.72), a lower percentage of W responses (-28.50), a

l, .er C,i, C (-3.61l), ,",r'e r :(. ,n:e: .:.F path.1 .3;l:.c l C.:rntrnt ( ?2.41),

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There were 11 cases of severe abnormalities in EEG. Five of them

occurred in the homicide group and six in the non-homicide group.

However, every homicide case with a severely abnormal EEG was

associated with personality disorganization, whereas only one case in

the non-homicide group with a severe abnormality in the EEG was

associated with personality disorganization. Borderline abnormalities

in EEG were not discriminatory with a trend toward more abnormality in

the EEG in the non-homicide group.

The following rule can be stated:

If the biographical-personality variables point to
disorganization and the EEG is severely abnormal,
consider higher probability of homicide behavior.
However, if the biographical-personality data is
not disorganized, and the EEG is severely abnormal,
consider the likelihood of non-homicide.

Your task at this time is to consider these variables (Computer

and interaction) and make your predictions as to which profiles are

homicide and which are not.

Goldberg's Scores for MMPI Profiles

The MMPI data was evaluated for the degree of personality dis-

organization by means of Goldberg's (1965) formulation. The Gold-

berg formula is a quantitative equation based upon the following


X = L + Pa + Sc C(y + Pt)

If the Goldberg value is high (X 55) the S is classified psychotic,

and if the Goldberg value is low CX 35) the S is classified neurotic.

Intermediate Goldberg values are considered indeterminate. Using these

cut-off values, Goldberg found a hit rate of 74%, with valid positive

rate of 62% and false positive rate of 18%. It has proven to be one

of the better decision rules for differentiating psychotic from

neurotic profiles.

Goldberg scores for your sample of 20 protocols:

Protocol # Score

44520 34

4'37 4-


75S.52 31

151 .0 4P

lil24, 64

4611c. 3J.

1? 3"S6 "S

35661 53


47" 3')3 I

;1:1; 31

1.63 4;

-.1346 i9
2 7' ),


Daily Correct and Incorrect Frequencies

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Francisco I. Perez was born in Havanna, Cuba, May 21, 1947.

He came to the United States in October, 1960. He graduated from

Belen Jesuit Preparatory School, Miami, Florida, in June, 1965, and

received his Bachelor of Arts in psychology from the University of

Florida in June, 1969.

In June, 1969, he enrolled in the Graduate School of the

University of Florida where until the present he has pursued his

. :l t. r'. e,.re o: Ilt r.,r *:.i ..rt ;n. r c,' ,'.:. :r F'n'. lru:.:.rr,.

ui r.nn; h i i r i .t r ,r.: r. i, n : iu ,. j i ru.: hi .. ,r : l.i:; t.-r I:., I

rr: ,t i ,r in F,. V:. ." ,:.hi in L,-ccTiL.r, I''"

C u r r .n l. i r t i i 3 rr i ,J r.I [ rig f'-5 r F 'r I . :.r .1 n i H1 1l ,:,n r. r, : ,

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.

Paul Satz, Chairmn
Professor of Psychology and Clinical

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.

. -
Professor of Psychology

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.

,H ,i ,. . .

Professor of Psychology and Clinical

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is fully
adequate, in scope and quality, as a dissertation for the degree of
Doctor of Philosophy.

Jaauelin R. Goldman
Associate Professor of Psychology
and Clinical Psychology

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

Tilliam D. Working
Associate Professor o ucation

This dissertation was submitted to the Department of Psychology
in the College of Arts and Sciences and to the Graduate Council, and
was accepted as partial fulfillment of the requirements for the degree
of Doctor of Philosophy.

August, 1972

Dean, Graduate School