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Gepubliceerd in: Quality of Life Research 6/2019

09-03-2019

Supporting construct validity of the Evaluation of Daily Activity Questionnaire using Linear Logistic Test Models

Auteurs: Núria Duran Adroher, Alan Tennant

Gepubliceerd in: Quality of Life Research | Uitgave 6/2019

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Abstract

Purpose

Construct validity is commonly assessed by applying statistical methods to data. However, purely empirical methods cannot explain what happens between the attribute and the instrument scores, which is the core of construct validity. Linear Logistic Test Models (LLTMs) can provide such explanation by decomposing item difficulties into a weighted sum of theoretical item properties. In this study, we aim to support construct validity of the Evaluation of Daily Activity Questionnaire (EDAQ) by using item properties accounting for item difficulties.

Methods

Dichotomized responses to the EDAQ were analyzed with (1) the Rasch model (to estimate item difficulties), and (2) LLTMs (to predict item difficulties). Seven properties of the items were identified and rated in ordinal scales by 39 Occupational Therapists worldwide. Aggregated metric estimates—the weights used to predict item difficulties in LLTMs—were derived from the ratings using seven cumulative link mixed models. Estimated and predicted item difficulties were compared.

Results

The Rasch model showed acceptable fit and unidimensionality for a sample of 42 locally independent EDAQ items. The LLTM plus error showed significantly better fit than the LLTM. In the former, three of the seven properties were not significant, and the corresponding model including only the significant properties was used to predict item difficulties; they explained 77.5% of the variance in estimated item difficulties.

Conclusion

A satisfactory theoretical explanation of what makes an activity of daily living task more difficult than another has been provided by a LLTM plus error model, therefore supporting construct validity of the EDAQ.
Literatuur
1.
go back to reference Nunally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill Nunally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill
3.
go back to reference American Psychological Association, American Educational Research Association, & National Council on Measurement in Education (1954). Technical recommendations for psychological tests and diagnostic techniques. Washington DC: American Psychological AssociationCrossRef American Psychological Association, American Educational Research Association, & National Council on Measurement in Education (1954). Technical recommendations for psychological tests and diagnostic techniques. Washington DC: American Psychological AssociationCrossRef
9.
go back to reference Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. København: Danmarks Paedagogiske Institut. Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. København: Danmarks Paedagogiske Institut.
10.
go back to reference Stenner, J. A., & Smith M. III (1982). Testing construct theories. Perceptual and Motor Skills, 55, 415–426.CrossRef Stenner, J. A., & Smith M. III (1982). Testing construct theories. Perceptual and Motor Skills, 55, 415–426.CrossRef
11.
go back to reference Stenner, J. A., Smith M. A. III, Burdick D. S. (1983). Toward a theory of construct definition. Journal of Educational Measurement, 20(4), 305–316.CrossRef Stenner, J. A., Smith M. A. III, Burdick D. S. (1983). Toward a theory of construct definition. Journal of Educational Measurement, 20(4), 305–316.CrossRef
12.
go back to reference Arthur, G. (1947). A point scale of performance tests. New York: Psychological Corp. Arthur, G. (1947). A point scale of performance tests. New York: Psychological Corp.
14.
go back to reference Baghaei, P., & Kubinger, K. D. (2015). Linear logistic test modeling with R. Practical Assessment, Research and Evaluation, 20, 1–11. Baghaei, P., & Kubinger, K. D. (2015). Linear logistic test modeling with R. Practical Assessment, Research and Evaluation, 20, 1–11.
15.
go back to reference Janssen, R., Schepers, J., & Peres, D. (2004). Models with item and item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models. New York: Springer. Janssen, R., Schepers, J., & Peres, D. (2004). Models with item and item group predictors. In P. De Boeck & M. Wilson (Eds.), Explanatory item response models. New York: Springer.
18.
go back to reference Kubinger, K. D. (1979). Das Problemlöseverhalten bei der statistischen Auswertung psychologischer Experimente. Ein Beispiel hochschuldidaktischer Forschung. Zeitschrift für Experimentelle und Angewandte Psychologie, 26, 467–496. Kubinger, K. D. (1979). Das Problemlöseverhalten bei der statistischen Auswertung psychologischer Experimente. Ein Beispiel hochschuldidaktischer Forschung. Zeitschrift für Experimentelle und Angewandte Psychologie, 26, 467–496.
19.
go back to reference Zeuch, N., Holling, H., & Kuhn, J. T. (2011). Analysis of the Latin square task with linear logistic test models. Learning and Individual Differences, 21, 629–632.CrossRef Zeuch, N., Holling, H., & Kuhn, J. T. (2011). Analysis of the Latin square task with linear logistic test models. Learning and Individual Differences, 21, 629–632.CrossRef
24.
go back to reference Andrich, D., Sheridan, B., & Luo, G. (2010). Rasch models for measurement: RUMM2030. Perth: RUMM Laboratory Pty, Ltd. Andrich, D., Sheridan, B., & Luo, G. (2010). Rasch models for measurement: RUMM2030. Perth: RUMM Laboratory Pty, Ltd.
25.
go back to reference Smith, E. V. Jr. (2002). Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. Journal of Applied Measurement, 3(2), 205–231.PubMed Smith, E. V. Jr. (2002). Detecting and evaluating the impact of multidimensionality using item fit statistics and principal component analysis of residuals. Journal of Applied Measurement, 3(2), 205–231.PubMed
26.
go back to reference Fischer, G. H. (1995). Rasch models: foundations, recent developments, and applications. New York: Springer.CrossRef Fischer, G. H. (1995). Rasch models: foundations, recent developments, and applications. New York: Springer.CrossRef
27.
go back to reference Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Thousand Oaks: Sage Publications, Inc. Kline, T. J. B. (2005). Psychological testing: A practical approach to design and evaluation. Thousand Oaks: Sage Publications, Inc.
29.
go back to reference Agresti, A. (2012). Analysis of ordinal categorical data (2nd ed.). Hoboken: Wiley. Agresti, A. (2012). Analysis of ordinal categorical data (2nd ed.). Hoboken: Wiley.
30.
go back to reference De Boeck, P., Bakker, M., Zwitser, R., & Nivard, M. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software. 39(12), 1–28.CrossRef De Boeck, P., Bakker, M., Zwitser, R., & Nivard, M. (2011). The estimation of item response models with the lmer function from the lme4 package in R. Journal of Statistical Software. 39(12), 1–28.CrossRef
31.
go back to reference De Boeck, P., Cho, S. J., & Wilson, M. (2016). Explanatory item response models. In A. A. Rupp, & J. P. Leighton (Eds.), The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications. New Jersey: Wiley. De Boeck, P., Cho, S. J., & Wilson, M. (2016). Explanatory item response models. In A. A. Rupp, & J. P. Leighton (Eds.), The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications. New Jersey: Wiley.
32.
go back to reference Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software; 67(1), 1–48.CrossRef Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software; 67(1), 1–48.CrossRef
35.
go back to reference Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer.
37.
go back to reference Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.CrossRef Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman & Hall.CrossRef
38.
go back to reference Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 1). New York: Wiley.CrossRef Draper, N. R., & Smith, H. (1998). Applied regression analysis (Vol. 1). New York: Wiley.CrossRef
Metagegevens
Titel
Supporting construct validity of the Evaluation of Daily Activity Questionnaire using Linear Logistic Test Models
Auteurs
Núria Duran Adroher
Alan Tennant
Publicatiedatum
09-03-2019
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 6/2019
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-019-02146-4

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