An Empirical Comparison of Item Response Theory and Hierarchical Factor Analysis in Applications Tot He Measurement of Job Satisfaction

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Journal of Applied ftychology1982, Vol 67, No. 6, 826-834

Copyright 1982 by the Am erican Psychological Association, Inc002 1 -9O1O/82/67O6-O826S00.75

An Empirical Comparison of Item Response Theory andHierarchical Factor Analysis in Applications to the

Measurement of Job SatisfactionCharles K. ParsonsCollege of Management

Georgia Institute of Technology

Charles L. HulinUniversity of Illinois at Urbana-Champaign

Responses to the Job Descriptive Index (JD I; N = 1,349) are used to empiricallycompare a two-parameter logistic item response theory (IRT) model to a hier-archical factor analytic model suggested by Humphreys (1962). A hierarchicalmodel was chosen because the JDI is typically conceptualized as having five ormore dimensions, whereas current IRT estimation procedures are based on the

assumption of a unidimensional trait. The hierarchical analysis allows the in-vestigator to create a general factor based on the common variance among theusual comm on factors. IRT item parameters estimated by a m aximu m likelihoodalgorithm agree very well with the item loadings on the first principal factor anda general satisfaction factor from a matrix of tetrachoric correlations. These resu ltsare consistent with a hierarchical job satisfaction model that has one generalfactor and multiple group factors, as well as a logistic IRT model with twoparameters. The authors conclude that IRT can be applied in the jo b satisfactiondomain, where data are typically multidimensional, to provide evidence aboutthe general satisfaction factor. Implications of this research for applications ofIRT are discussed.

Item response theory (IRT) has been pro-posed as a solution to many measurementproblems (Lord, 1980). The advantages ofIRT models are derived from the followingproperties. First, they provide a scale of mea-surement (called the 6 scale) that is indepen-dent of the particular items included in a

measurement instrument. Second, they pro-vide item parameters that are independentof the sample of persons being measured.Some of the applications of such measure-ment m odels were discussed in H ulin, Dras-gow and Parsons (1982) and Lord (1980),and an introduction of IRT and methods waspresented in Hambleton and Cook (1977).

This research was supported in part by Contract

N00O-14-C-O9O4 from the Office of Naval Research,Charles L. Hulin, principal investigator; and in part bythe Department of Psychology, University of Illinois.The authors thank Michael Levine for his assistance inthe research as well as several anonym ous reviewers whoprovided many helpful comments on earlier drafts ofthis article.

Requests for reprints should be sent to Charles K.Parsons, College of Management, Georgia Institute ofTechnology, Atlanta, Georgia 30332.

Although the IRT models are typically as-sociated with aptitude measurement, Schmitt(1981) noted that various IRT theorists(Ham bleton & Cook, 1977; Lord, 1977;Wright, 1977) have suggested that th e appli-cability of the models to attitude measure-ment is also worth investigating.

Because such applications of the IRTmethods to attitude measurement are rareand the m ethods themselves are probably notwell understood by most researchers, it be-hooves us to examine the extent to which theresults of the newer methods converge withresults from the more traditional methodswhen examining issues of interest in attitudemeasurement. For example, it is useful tocompare both theoretically and empirically

the more traditional factor analysis methodsto the newer IRT m ethods for describing thestructure of attitude items. This article brieflydescribes the similarity and dissimilarity ofthe factor analytic and IRT methods andthen applies both to job satisfaction data toanswer the question concerning the trait thatis basic to a set of observations.

826



ITEM RESPONSE THEORY IN JOB SATISFACTION DATA 827

The Factor Analytic Model

Most factor analysis methods are based ona model that can be described as "a formalmodel about hypothetical com ponents whichaccount for linear relationships that exist be-

tween observed variables" (Mulaik, 1972, p.96; emphasis added).

1In studies of the mea-

surement of job satisfaction, factor analyticmethods are used to determine the numberof hypothetical factors and the relations ofitems to these factors. Because factor analyticmethods are linear, they are theoretically in-correct for discrete variables, such as itemswith limited scored response options. Forexample, the frequently used job satisfaction

measure, the Job Descriptive Index (JDI;Smith, Kendall, & Hulin, 1969) with threeitem-response alternatives, has frequentlybeen factor analyzed in order to answer ques-tions about convergent and discriminant va-lidity (Dunham , Smith, & Blackburn, 1977),similarity of factor structure across subpop-ulations (Golembiewski & Yeager, 1978;Smith, Smith, & Rollo, 1974), and the num-ber of meaningful scales that can be derived

from the JDI (Yeager, 1981).

Item-Response Models

A formal item-response model states a re-lation between the probability of some typeof response by an individual and some char-acteristic of the individual. A factor analyticmodel is one type of item-response model;it describes the linear relation between item

responses and constructs. Recently, in thearea of psychometrics, there has been an in-creasing emphasis on ogive or logistic item-response models (Birnbaum, 1968). In mostapplications the probability of an item re-sponse is a function of a unidimensional at-tribute of the respondent and certain char-acteristics of the item called item parameters.When unidimensionality is assumed, thefunction is called an item characteristic curve(ICC) and gives the conditional probabilitythat a random ly chosen person from the pop-ulation of all people at a given value of thetrait gives a particular response. The notationcommonly used denotes the attribute by (0)and the ICC for the /* item by P, (0).

For the model discussed in this article,P,(0) is given by the formula

P,(6) =1

1 +e~(1 )

The terms a, and b, are called item param-eters, with a, reflecting item discriminationand b, reflecting item difficulty for the I

th

item. D is a scaling factor usually set to 1.702so that this logistic function closely followsa normal ogive curve.

A graphic representation of this model isrepresented by an S-shaped curve. The b pa-rameter reflects the position of the curve onthe horizontal axis (0), and the a parameter

is related to the slope of the curve at its steep-est point. Lower b values shift the curve tothe left and lower a values flatten the curve.

A third parameter is frequently added tothe model when individuals who do not rec-ognize the correct response are able and havea tendency to give a correct response to anitem with probability greater than zero. Theeffect of nonzero value for this parameter onan ICC is most obvious at the low level of6 where the probability of a correct responsedoes not approach zero. (The ICC has a non-zero lower asymptote.) For a job satisfactionquestionnaire, it can be argued that as thelevel of 0 approaches very low values(-•x), the conditional probability of re-sponding positively to an item goes to zerobecause guessing does not occur. Parsons(Note 1) has determined empirically that thisis a tolerably good assumption for the JobDescriptive Index. Therefore, the third pa-

rameter will be set to zero (identical to notincluding it in the model) for the present ar-ticle (see Hulin. Drasgow, & Parsons, 1982.or Lord, 1980, for further discussion on thevarious item response models).

As a final note on Equation 1, it is obviousthat the slope of a linear item response model

1This paper will use the term factor rather than com-

ponent to denote the hypothetical element that is com-

mon to a set of observed variables. The distinction isimportant when applying factor analysis because com-ponents are usually derived to account for the varianceof observed variables, whereas factors are derived to ac-count for covariance among observed variables (seeMu laik, 1972, for further details).



828 CHARLES K. PARSONS AND CHARLES L. HULIN

will be related to the a parameter from thelogistic model. In fact, for the two-param eternormal ogive model (similar to the logisticmodel),

a, = 7 7 = = , (2)

where for the Ith

item, a, is item discrimi-nation and rbi is the biserial correlation be-tween item and unidimensional 0 (Lord &Novick, 1968). For the same model, theseauthors have also specified the relationshipbetween a, and the factor loading of item ion the first principal factor (a,) from thematrix of tetrachoric correlations. Again, as-suming unidimensional 0, this relation is

(3)

This relation clearly shows that when theassumptions of the normal ogive model hold(including unidimensionality), the relationbetween the IRT model and the factor ana-lytic model is simple. Unfortunately, most

data sets do not meet such an assumption.

Unidimensional IRT Models inMultidimensional Data

Applications of the IR T m odel in real datarequire understanding the consequences ofviolating the m odel's assum ptions. In partic-ular, what does it mean to apply this unidi-mensional model to multidimensional data?Reckase (1979) has presented some resultsthat compare such an application of a three-param eter logistic IRT model in data sets ofvarying factorial complexity. As more or-thogonal factors of equal size are present inthe item set, the relation denoted in Equation3 becomes weaker. On the other hand, theestimated a parameters do relate highly tothe loading of one of the orthogonally rotatedfactors. This is quite interesting, because the

convergence of a model that assumes uni-dimensionality with one factor of a modelthat assumes multidimensionality tells uswhat component is being measured by theformer. Based on his results, Reckase alsosuggested that stable parameter estimates forthe IRT model require a first principal com-ponent that accounts for at least 20% of thetest variance.

The theoretical work by Lord and Novick(1968) shows us the relation between themodels for the one-factor case, and Reckase's(1979) Monte Carlo results show us the re-sults for multiple orthogonal factors. The

correspondence of the IRT analysis of jobsatisfaction items to one extreme or the otherwill reveal the trait underlying the set of re-sponses.

However, there are a multitude of possiblefactor analytic solutions in a set of data. Fac-tor analysis investigations of job satisfactionare usually based on the common factormodel and use factor rotation techniquesbased on some mathematical criterion ofThurstone's (1947) principles of simplestructure. Such rotations explicitly empha-size the differences between sets of items tha tdefine each factor.

An alternate approach is to exam ine a fac-tor that is com mon to all items. The firstprincipal factor is an example of such a factorbut suffers from lack of psychological inter-pretability when multiple factors exist in thedata. Humphreys (1962) has proposed theuse of hierarchical factor models that de-scribe observed variables as a function of ageneral factor and multiple, more specific,common factors. The specific method willbe described in the Methods section of thispaper.

Regarding general factors, Humphreys(1962) and Humphreys and Hulin (Note 2)have noted that the most striking character-

istic of published matrices displaying inter-correlations am ong very diverse measures ofcognitive ability, based on large samples andreliable m easures, are the sizes of the smallestcorrelations. They are typically positive andsuggest the presence of a general factor ofintelligence that is obscured by most com-mon factor analytic procedures. Similar ob-servations can be made concerning factoranalytic studies of job satisfaction. Positive

correlations among the commonly reportedfactorially derived scale scores are the mostobvious and general outcome of any suchstudy. Based on these arguments, the generalfactor, as well as the more specific commonfactors should be considered when compar-ing factor analytic results to IRT results.

As described in the open ing paragraph, theadvantages of IRT to many measurement



ITEM RESPONSE THEOR Y IN JOB SATISFACTION D ATA 829

problems has been thoroughly described else-where. Strict adherence to the assumptionsof the model in the available computationalprocedures would appear to make applica-tions to many research settings impossible.

Longer scales are certainly one alternative,but they are not practical in many organi-zational contexts where research time andquestionnaire space are extremely preciouscommodities. Application of the model andprocedures to a multidimensional item setis useful to the extent that we obtain usefulresults, understand the trait being assessed bythe total item set, and are aware that we areviolating an assumption of the model.

With this caveat, the current study at-tempted to shed light on what trait is beingassessed by the unidimensional IRT modelin multidimensional job satisfaction item re-sponse data . Specifically, two questions wereasked:

1. Is there evidence to support the pres-ence of the general factor?

2. Does an IRT procedure that assumesunidimensionality provide parameter esti-mates that converge with the general factor

or one of the more com monly cited facet fac-tors in the Job Descriptive Index?

the work itself, pay, promotional opportunities, super-visor, and co-workers. Four scales are included in thisstudy (the pay scale was omitted ). Also, three adjectivesfrom the co-workers scale ("easy to mak e enem ies," "n oprivacy," and "h ard to m eet") were not included becausethey were not common to several other samples that

were originally unde r study by Parson s (1980). Therefore60 items from the JDI were used to index job satisfac-tion.

Only subject records with no missing data on the 60JDI items were included. Although Lord (1974) has pre-sented an acceptable solution for estimating both itemand 6 parameters for aptitude tests with omitted re-sponses, it is based on assumptions that clearly are nottenable for responses to the JDI. For instance. Lord(1974. p. 250) stated the assumption that "examineeswish to maximize their expected scores and that theyare fully informed about their best strategy for doingthis." After eliminating records with omitted responses,

the sample consisted of 1,349 response records.

Parameter Estimation

Parameters for the IRT model were estimated fromthe maximum likelihood algorithm, LOGIST (Wood,Wingersky, & Lord, Note 3). LOGIST requires dichoto-mous scoring of i tems, with 1 indicating satisfaction and

0 indicating no satisfaction. The responses scored 0 or1 by the Smith et al. (1969) procedure were scored as0. Responses that would have received a 3 were scoredas 1. The justification for this adjustm ent com es from

Smith et al. 's results demonstrating that question-markresponses (scored 1) were m ore frequently given b y in-dividuals with low satisfaction.

Method

Subjects

Data consisted of responses to questionnaires by non-ma nag enal personn el performin g a variety of jobs in alarge international merchandising company. Usableques tionn aires were received from 1,632 em ployees dis-tributed among 41 units around the United States. Re-

turn rates for questionnaires were approximately 95%.The surveys were administered by organizational staffmembers. Participation was voluntary and participantscompleted the surveys on company time. They werecollected and mailed to university researchers. Confi-dentiality of responses was assured although identifyinginform ation was requested in order to com plete a follow-up study no t relevant to the present one. In this sample,59% of the respondents considered themselves full-timeworkers and the other 4 1% were part time. Thirty per-cent of the sample was male, the average age was 36.5years, and average tenure was 6.6 years. Further descrip-tion of the subjects, questionnaire, and results can befound in Miller (1979).

Variables and Selection of Data

The Job Descriptive Index (Smith et al.. 1969) wasused as the measure of job satisfaction. The JDI is aseries of adjective checklists that assess satisfaction with

Factor Extraction and R otation

Previous factor analysis studies of the JDI have beenbased on Pearson product-moment correlations andhave not considered that the observed variables are bet-ter described as discrete than continuous. Tetrachoriccorrelations are another index of association sometimesused in test develop men t and can be used if two observedvariables, such as items, are scored dichotomously.Then, if it is reasonable to assume that the trait under-lying each item response is continuous, "it is possibleto secure an estimate of what the correlation could beif the underlying traits were continuous and normallydistributed o r if they were so mea sured a s to give no rm aldistributions" (McNemar, 1969, p. 221). Because Lordand N ovick (1968) have shown the relation between fac-tor analysis of tetrachoric correlations and the normalogive item response m odel, tetrachorics were used in th isstudy. This latter requirement excluded the use of con-firmatory factor analysis (such as Joreskog's, 1970, max -imum likelihood procedure) to test the fit of the hier-archical factor model (Lord. 1980, p. 21).

All factoring is based on the 60-item tetrachoric cor-relation matrix with the maximum correlation of eachitem w ith the other items in the diagonal as com m una l-lty estimates (referred to as the reduced matrix). Theprincipal axes method (H arm an. 1967) was used to ex-tract principal factors.

The direct oblimin routine (Jennnch & Sampson,





ITEM RESPONSE THEOR Y IN JOB SATISFACTION DATA 8 3 1

Table 4

LOGJST a, Principal Factor Loadings, and Hierarchical Factor Matrix for 60 JobDescriptive Index Items

Item

1

23

4

5

6

7

8

9

10

11

12

13

14

1516

17

18

19

20

21

22

23

24

25

26

27

2829

30

31

32

34

35

36

37

38

39

40

41

4243

44

45

46

47

48

49

50

51

52

53

54

55

5657

58

59

60

a,

.517

.342

.850

.677

.853

.432

.768

.179

.796

.602

.503

.280

.651

.032

.321

.171

.244

.843

1.053

.724

.907

.903

1.071

.844

.657

.873

1.109

.532

.943

.888

.787

.743

1.087

.616

.559

.515

1.073

.689

.878

1.209

1.127.020

.712

.885

.512

.724

.438

.498

.677

.682

.440

.817

.385

.659

.539

580

.690

559

.559

PF

.484

.381

.625

.530

.614

.427

.594

.215

.609

.473

.493

.300

.532

.048

.353

.204

.290

.613

.576

.472

.589

.599

.584

.597

.486

.542

.615

.465

.634

.584

.590

.589

.679

.523

.470

.470

.650

.539

.605

.666

.682

.057

.566

.555

.494

.563

.430

.479

.513

.544

.444

.621

.389

.570

.476

.457

.558

.526

.513

G

.459

.371

.574

.509

.529

.407

.510

.148

.502

.428

.428

.235

.508

.030

.271

.218

.229

.568

.432

.345

.447

.466

.444

.451

.351

.403

.463

.365

.445

.399

.423

.405

.483

.357

.291

.346

.446

.358

.418

.455

.489

.047

.404

.381

.400

.433

.318

.379

.390

.420

.338

.478

.282

.448

.352

.339

.424

.407

.386

Fl

-.041

-.045.036

.035

.108

-.065

.100

.167

.198

.044

.087

.072

-.010

.078

.210-.082

.117

.017

.075

.029

.094

.080

.046

.149

.081

.046

.079

.352

.706

.726

.542

.654

.596

.430

.673

.386

.785

.699

.575

.728

.519

.180

.531

.667

-.097

.050

-.047

-.024

.019

-.024

-.077

.049

.035

.008

.010

.065

-.021

-.012

.065

F2

-.027

-.020.065

.043

.160

-.025

.097

.132

.149

.106

.122

.085

-.068

.053

.057

.026

.060

.037

-.068

-.012

.040

.016

-.045

.100

.056

-.033

.017

-.121-.031

-.047

-.040

-.026

.070

.151

.009

-.056

-.057

-.035

.103

.044

.187-.079

.054

-.023

.497

.610

.711

.509

.665

.668

.607

.627

.487

.575

.693

.585

.666

.590

.542

F3

.100

.024-.001

-.032

.029

.124

.117

-.024

.057

-.049

.002

.154

.058

-.052

.052-.089

.079

.020

.846

.768

.662

.633

.831

.515

.655

.749

.785

.109-.011

-.038

.141

.062

.112

.141

.011

.154

-.004

-.015

.068

.021

.074-.178

.024

-.026

.119

-.025

-.054

.024

-.076

.006

.065

.027

.040

.028

-.066

-.070

.065

.043

.029

F4

.546

.478

.617

.573

.427

.483

.404

-.023

.322

.434

.362

.083

.632

-.013

.103

.353

.102

.632

.035

-.031

.067

.133

.062

.072

-.046

.005

.044

.205

.049

.007

.062

.006

.033

-.071

.140

.088

.007

-.045

-.027

-.026

.036

.082

.048

.003

.133

.067

-.055

.100

.028

.046

-.003

.081

-.054

.110

-.037

-.014

.018

.056

.017

Note. PF = first principal factor; G = general factor, Fl = supervision satisfaction facet factor, F2 = co-workerssatisfaction facet factor; F3 = promotions satisfaction facet factor; F4 = work satisfaction facet factor.



g32 CHARLES K. PARSONS AND CHARLES L. HULIN

Table 5Correlations O/LOGIST a With TransformedFactor Loadings

Factor

First principal factorSatisfaction

GeneralSupervisionCo-workersPromotionWork

a

.966

.853

.374-.081- .402

.036

facet factors. Correlations for the loadings on

the oblique facet factors would be identicalto those for the facet factors in the hierar-chical structure.

Clearly, the general factor is the psycho-logically interpretable factor that is most rep-resentative of 8 from the IRT analysis. Thecorrelations for the first principal factor alsodemonstrate that the theoretical relation forunidimensional items also holds very well forthe multidimensional items of the JDI. Be-

cause these correlations only reflect the sim-ilarity of patterns of loadings and a, the rootmean squared difference for these two itemindexes were also computed and are .057 forPF and . 167 for G. Finally, the means for thevarious indexes are .528 for a, .507 for PF,and .392 for G. As can be seen, there is atendency for the a to be larger than whatwould be expected from the transformedloadings.

Discussion

The hierarchical factor matrix provides apattern of factor loadings that is psycholog-ically interpretable and can be compared tothe IRT results. Regarding its interpretabil-ity, the addition of the general factor to thealready moderately well fitting four-orthog-onal-factor solution could be criticized formaking the matrix less parsimonious (moreparameters). The obvious response to theobjection is that the orthogonal facets havelittle substance in empirical observation. Inaddition, parsimony is not the sole or eventhe overriding goal of science. If this were thecase, then four-principal factors (without ro-tation) would be desirable because they ac-count for the maximum possible variance

with this number of parameters. Anothercriticism could be based on using second-or-der factoring rather than simply leaving thefactor intercorrelations to be interpreted. In

this case, it can be argued that the higherorder factoring is both a more meaningfuland parsimonious solution because it in-volves a reduction of the factor intercorre-lation matrix. The Schmid-Leiman trans-formation simply used this reduction to de-fine a general factor, to orthogonalize thefacet factors, and to define all factors in termsof observed variables. The main advantageof the hierarchical factor solution is that itillustrates that items on different scales doshare common variance and this commonvariance can be effectively summarized byone second-order factor. This latter pointwould not hold if the oblique rotation hadresulted in factor intercorrelations that werenot described by a single factor. That is, asingle general factor is not a necessary out-come from the hierarchical transformationprocess.

Because of the clear results from the hi-erarchical solution, the interpretation of theIRT analysis is clear. The item discrimina-tion parameters describe the relation of thegeneral factor, however, we choose to extractor represent it, to the probability of endorsingthe items. On the other hand, from the per-spective of IRT, the dim ensions of the latentspace represented by the four facet factorsare also related to the probability of item en-dorsement. When and if satisfactory esti-mation procedures become available, thesedimensions can be incorporated into amultidimensional item response model forthe JDI.

The current findings are important be-cause they suggest that IRT can be appliedas a reasonably well fitting measurementmodel for the JDI and probably other ques-tionnaire measures of job satisfaction that

can be scored validly in a dichotom ous m an-ner. This conclusion does not exclude thedevelopment of item sets that better fit theunidimensional IRT model or the experi-mentation with the shorter scales of facet sat-isfaction to determine whether or not furtherimprovements can be made.

At the same time, these data strongly sug-gest the necessity for developing estimation



ITEM RESPONSE THEORY IN JOB SATISFACTION DATA 833

procedures for multidimensional item re-sponse theories that will describe responsesto each item of both a general 0 and a facet0. It must be emphasized that the appeal ofa multidimensional model over one that em-

phasizes and uses only the general factorfrom the hierarchical solution will depend onthe goals of the researcher and the uses towhich the resulting scales are to be pu t. If theaim is the prediction of behavioral responsesreflecting general acceptance or rejection ofa work situation, such as turnover or absen-teeism, then the use of job satisfaction scoresreflecting the general factor will probablyprovide predictive power equal to that gen-

erated by a multidimensional approach. Onthis poin t, we could not expect a general fac-tor to outperform a multiple regression com-posite of JDI scales in accounting for vari-ance in a specific criterion. However, the gen-eral factor may account for more variancein a wider range of behavioral responses thanany one weighted composite of JDI scales.In addition, in small samples, the general fac-tor would no t be subject to the sampling errorthat severely limits cross validation of leastsquares regression equations. Humphreysand H ulin (Note 2) have comm ented on thissame point in the domain of ability mea-surement and job performance prediction.

The fit of the IRT discrimination indexes,derived assuming local independence andunidimensionality of 0, to the loadings of theitems on the general factor from the hierar-chical factoring suggests minimal violencemay be done to our data by fitting it to a

general unidimensional m odel. So long as weare aware that assumptions are being madein this approach that are not entirely correct,our informed violation of these assumptionsshould not mislead us.

However, if the aims of the researcher aremore specific, such as to test specific hy-potheses about attitudinal or affective cor-relates of specific behaviors—voting for unionrepresentation in National Labor Relations

Board elections, being absent on specificdays, or volunteering to work overtime—then more complex multidimensional mod-els are required. Similarly, if the aims of aninvestigator are interventions designed to in-crease levels of job satisfaction in an orga-nization, then again, multidimensional mod-

els are required to provide evidence aboutwhich specific factors in the work situationshould be changed. We can operate as re-searchers or practitioners with either modeldepending on our aims without making as-

sumptions that we have learned much aboutspecific causes of job satisfaction when weuse a general factor approach or that weknow m uch about the antecedents of behav-iors reflecting general acceptance/rejection ofa job when we use multidimensional m odels.

Perhaps most importantly, this study hasdemonstrated the convergence of evidencefrom three quite different approaches to thestudy of the meaning of different item re-

sponses on job satisfaction questionnaires.Convergence among measures based on thefirst principal factor, on the general factorfrom a hierarchical factor model, and froma unidimensional latent trait model are en-couraging. The results of this study providesome evidence for interpreting what is beingestimated by 0s from the JDI. Both the ne-cessity and lim itation of future developm entsstressing multidimensional IRT models injob satisfaction have been pointed out. Re-finements of the model will generate m oreresearch aimed at specifying the usefulnessof general and specific job satisfaction mea-sures.

Reference Notes

1. Parsons, C. K. Empirically investigating the two pa-rameter logistic model for a job sa tisfaction question-naire. Paper presented at the meeting of the Ameri-can Institute for Decision Sciences. Boston, Novem-

ber 1981.2. Humphreys, L. G., & Hulin. C. L. The construct of

intelligence in the historical perspective of classicaltest theory. Paper presented at the Educational Test-ing Service Symposium on Construct Val idi ty,Princeton. N.J., October, 1979.

3. Wood, R. L., Wingersky, M. S., & Lord, F. M. Acomp uter program for estimating ability and itemcharacteristic curve parameters (ETS RM 76-6) .Princeton. N.J.: Educational Testing Service, 1976.

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Dunham, R. B.. Smith, F. J. , & Blackburn, R. S. Val-idation of the Index of Organizational Reactions withthe JDI, MSQ, and Faces scales. Academy of Man-agement Journal, 1977, 20 , 420- 432 .



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Received March 5, 1982 •

Documents

An Empirical Comparison of Item Response Theory and Hierarchical Factor Analysis in Applications Tot He Measurement of Job Satisfaction