The Mokken Scale: A Critical Discussion

Roskam, Edward E.; Wollenberg, Arnold L. van den; Jansen, Paul

doi:10.1177/014662168601000305

Cited by 26 publications

(24 citation statements)

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“…See Mokken (1971) for a thorough review of this nonparametric item response theory model. More recently, several discussions about the usefulness of the Mokken scale analysis haveappeared in the literature (Jansen, Roskam, & Van den Wollenberg, 1984;Roskam, Van den Wollenberg, & Jansen, 1986;Sytsma, 1984Sytsma, , 1986. Further refinements of the model were reported by Molenaar (1982Molenaar ( , 1983a and Sytsma and Molenaar (1987).…”

Section: Further Elaborations and Discussion On The Mokken Scalementioning

confidence: 99%

A FORTRAN 77 program for a nonparametric item response model: The Mokken scale analysis

Kingma

Taerum

1988

Behavior Research Methods, Instruments, & Computers

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A nonparametric item response theory model-the Mokken scale analysis (a stochastic elaboration of the deterministic Guttman scale}-and a computer program that performs this analysis are described. Three procedures of scaling are distinguished: a search procedure, an evaluation of the whole set of items, and an extension of an existing scale. All procedures provide a coefficient of scalability for all items that meet the criteria of the Mokken model and an item coefficient of scalability for every item. Four different types of reliability coefficient are computed both for the entire set of items and for the scalable items. A test of robustness of the found scale can be performed to analyze whether the scale is invariant across different subgroups or samples. This robustness test serves as a goodness offit test for the established scale. The program is written in FORTRAN 77. Two versions are available, an SPSS-X procedure program (which can be used with the SPSS-X mainframe package) and a stand-alone program suitable for both mainframe and microcomputers.The Mokken scale model is a stochastic elaboration of the well-known deterministic Guttman scale (Mokken, 1971; Mokken & Lewis, 1982;Mokken, Lewis, & Sytsma, 1986). The Mokken model is applied to dichotomous items for which one alternative is designated as positive with respect to the latent variable of interest (attitude, ability, etc.). This model belongs to the general family of the popular item response theory models (Mokken & Lewis, 1982). The Mokken model has been shown to be very useful for scaling social attitudes, political knowledge, political participation, and political efficacy (Gillespie, Ten Vergert, & Kingma, 1987a, 1987bLippert, Schneider, & Wakenhut, 1978; Mokken, 1969a Mokken, , 1969bStokman, 1977;Stokman & Van Schuur, 1980). In psychological research, the Mokken model has been applied successfully to the construction of developmental scales of Piagetian concepts (Kingma, 1984; Kingma & Loth, 1985;Kingma & Reuvekamp, 1984;Kingma & Ten Vergert, 1985; Kingma & Van den Bos, in press), as well as to cumulative scaling of problem solving tasks (Henning, 1976) and sleep quality (Mokken & Lewis, 1982). The purpose of this paper is to describe a user procedure FORTRAN 77 program (which can be used with SPSS-X) and a stand-alone Fortran 77 program of which both mainframe and MS-DOS versions are available. These programs, both named Mokscal, perform the Mokken scale analysis. Before presenting a review of the program and the various statistics, we describe in shorthand the underlying assumptions of the Mokken model, which are crucial for the appreciation of this rather unknown model. THE MOKKEN MODELThe Mokken model treats the attitude, the ability, or any other variable of interest as a single latent trait on which the person's location is represented by the parameter (J and the item's location is represented by the parameter o. Given a reasonably unidimensional set of items-that is, one dominated by the latent variable measured-the person's parame...

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Section: Further Elaborations and Discussion On The Mokken Scalementioning

confidence: 99%

A FORTRAN 77 program for a nonparametric item response model: The Mokken scale analysis

Kingma

Taerum

1988

Behavior Research Methods, Instruments, & Computers

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“…When highly scalable (or high discrimination) items are required, c needs to be high. Coefficient H also tends to be higher when the dispersion of items is larger (Roskam, Van den Wollenberg, & Jansen, 1986). For more information on the effect of c on dimensionality results, see Hemker et al (1995), Molenaar and Sijtsma (2000), and van Abswoude et al (2004).…”

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Assessing Dimensionality by Maximizing H Coefficient–Based Objective Functions

Abswoude

Vermunt

Hemker

2007

Applied Psychological Measurement

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Mokken scale analysis can be used for scaling under nonparametric item response theory models. The results may, however, not reflect the underlying dimensionality of data. Various features of Mokken scale analysis-the H coefficient, Mokken scale conditions, and algorithms-may explain this result. In this article, three new H-based objective functions with slight reformulations of Mokken scale analysis in the unidimensional and multidimensional cases are introduced. Deterministic and stochastic nonhierarchical clustering algorithms reduced the probability of obtaining suboptimal solutions. A simulation study investigated whether these methods can determine the dimensionality structure of data sets that vary with respect to item discrimination, item difficulty, number of items per trait, and numbers of observations per test. Furthermore, it was investigated whether deterministic and stochastic algorithms can generate approximately global optimal solutions. The method based on the average within-scale H i combined with a stochastic nonhierarchical clustering algorithm was the most successful in dimensionality assessment.

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“…two parameters logistic -modelling difficulty and discrimination only) would have resulted in poorer fit to the data, likely resulting in exclusion of more items. Overall, such exclusions would have led to smaller scales that tend to be less reliable and precise [192]. Finally, the IRSF left asymptotes were all 0, which suggests absence of guessing (i.e.…”

Section: Summary Of Nirt Analysismentioning

confidence: 99%

“…Under the assumption of monotonicity of the IRSF, it has been shown that Hij>0 for all (i,j), and Hi>0 [190], however the reciprocal does not hold, thus Loevinger's Coefficient cannot be used to confirm monotonicity of the IRSF. In addition, Loevinger's Coefficient is sensitive to population variance, item difficulty, discrimination and presence of redundant items in the scale making their interpretation also difficult [191,192]. However, it is commonly accepted that items satisfying Hij>0 for all (i,j), i ≠j,and 0.3<Hi<0.4 form a "weak…”

Section: Score Description Three Months Test-retest and Psychometricmentioning

confidence: 99%

“…Thus, one may prefer to use MSA for its simplicity, or when trying to reduce the length of a questionnaire. The counterpart being that MSA relies on rather arbitrary criteria (see [191] for further discussion on the interpretation of Loevinger's coefficients) and, like PIRT, may reduce reliability and precision of the instrument [192]. …”

Section: Sphere-21 Mean Scores Reliability and Psychometric Propertiesmentioning

confidence: 99%

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Breaking down the genetics of depression using brain endophenotypes

Couvy‐Duchesne¹

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The Queensland Brain Institute 2 Thesis AbstractThe objective of the work presented in this thesis was to extend our understanding of depression aetiology and to identify brain MRI endophenotypes of depression. Our work builds on previous research that highlighted the genetic relationships between stress, sex and depression. In addition, MRI measurements are heritable and do not relying on selfreport. As such, they represent promising endophenotypes that could help break down further the genetics of depression.Preliminary work involved reporting depression prevalence in a young adult Australian twin sample (N=2,773) as well as comorbidity with affective and substance use disorders (Chapter 1). In addition, we demonstrated the good psychometric properties of the SPHERE questionnaire, which provides self-reported measures of anxiety-depression and chronic fatigue. Properties of the scores include: i) stochastic ordering on the sum score, ii) limited sex and age bias through adolescence, iii) high internal consistency, iv) good 3 months test-retest, v) moderate heritability, vi) significant association with lifetime MDD, social anxiety and alcohol dependence diagnoses (Chapter 2).In an older sample of Australian twins (N=5,221, Chapter 3), we performed a direct test of the diathesis-stress hypothesis in depression, which states that stressors can mediate the effect of genetic predisposition (diathesis) on the disease risk. We used Polygenic Risk Scores as a measure of diathesis and report a significant interaction with personal stressful life events, supporting the idea that stress and predisposition have a multiplicative effect on the depression liability scale. This interaction may be different between sexes as suggested by the stratified analysis.We then performed 3 analyses of resting-state fMRI from which we showed that: 1) Head motion (HM) is reliable and heritable in addition to being a major confound in fMRI analyses. However, this is unlikely to greatly confound twin studies of resting-state functional connectivity (FC). As a by-product of the analysis we showed that FC in the Broca's areas networks exhibited low to moderate test-retest and heritability (Chapter 4).2) HM is phenotypically and genetically associated with ADHD scores of Inattention and Hyperactivity/Impulsivity. This leads to most head motion image processing options to reduce power and/or to induce a sampling bias in RS-fMRI studies of ADHD. We discussed ways of circumventing this problem, via volume pruning or multivariate analysis (Chapter 5). 33) Nuisance regression (global signal regression, CompCor and HM regression) options in RS-fMRI processing have a moderate impact on the test-retest of regional homogeneity. Independently of the processing options, the low to moderate intra-class coefficients estimated in our sample may be due to insufficient scanning time (5mins 20s) and would impair our statistical power to detect RS-fMRI endophenotypes of depression (Chapter 6).Rather, we focused on structural brain images and contributed sample...

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The Mokken Scale: A Critical Discussion

Cited by 26 publications

References 10 publications

A FORTRAN 77 program for a nonparametric item response model: The Mokken scale analysis

A FORTRAN 77 program for a nonparametric item response model: The Mokken scale analysis

Assessing Dimensionality by Maximizing H Coefficient–Based Objective Functions

Breaking down the genetics of depression using brain endophenotypes

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