Institute of Sociology NASU

CONSTRUCTION OF THE INDEX BASED ON THE GSR-5 ITEMS USING THE GRADED RESPONSE MODEL

stmm. 2022 (3): 25-39

DOI https://doi.org/10.15407/sociology2022.03.025

RUSLANA MOSKOTINA, PhD in Sociology, 1st Category Specialist, Laboratory of Applied Sociological Research, Faculty of Sociology, Taras Shevchenko National University of Kyiv (4-d, Glushkova Avenue, Kyiv, 03022)

rmoskotina@ukr.net

https://orcid.org/0000-0002-2195-3121

This article is about building of the index for GSR-5 items (questions). They а measure general attitudes towards the general welfare state. The simplest way is to calculate an additive index (a total score of the respondent’s answers to GSR-5 items). Such an index is easy to interpret but it has some limitations. Firstly, it assumes that all the questions have the same weight. But it is logical to suppose that this is not always the case. Secondly, it is expected that the distances between the neighboring answer options are the same for each item. However, if we are dealing with variables that are measured on an ordinal scale this condition may not be fulfilled. Therefore, we need an appropriate tool allows us to construct an index that overcomes the limitations are mentioned above. One such a tool is a graded response model; it is designed to work with variables that are measured on an ordinal scale. First of all, it is found out is there appropriate to construct an additive index for GSR-5 items. After building the single factor CFA model (confirmatory factor analysis model) with the same factor loadings for each question it turned out that this model does not show an acceptable fit to the data. Thus, the calculation of the additive index for GSR-5 items is not appropriate. Therefore, there is a need for an alternative model. Since GSR-5 items are measured on an ordinal scale a unidimensional graded response model (GRM model) is applied. It shows a good fit to the data. With the GRM model it is possible to build the index which takes into account different weights of the questions and distances between the answer options for each item. The index is constructed with the graded response model shows more variability than the additive index. In addition the graded response model (in order to facilitate interpretation) allows us to represent values of the latent variable as the additive index values. This is the advantage of the graded response model compared to confirmatory factor analysis models. The latter can also be used as tools for constructing additive indices but they do not provide the transformation of latent variables from the one scale to another.

Keywords: graded response model, GSR-5, latent variable, confirmatory factor analysis, additive index.

References

Bean, G.J., Bowen, N.K. (2021). Item response theory and confirmatory factor analysis: Complementary approaches for scale development. Journal of Evidence-Based Social Work, 18 (6), 597–618.

Bean, J. (2021). Using R for Social Work Research. Retrieved from: https://bookdown.org/bean_jerry/using_r_for_social_work_research/

Cai, L., Hansen, M. (2013). Limited-information goodness-of-fit testing of hierarchical item factor models. British Journal of Mathematical and Statistical Psychology, 66 (2), 245–276. DOI: https://doi.org/10.1111/j.2044-8317.2012.02050.x

Cai, L., Monroe, S. (2014). A new statistic for evaluating item response theory models for ordinal data (CRESST Report 839). Los Angeles: University of California, National Center for Research on Evaluation, Standards, and Student Testing (CRESST).

de Ayala, R.J. (2022). The theory and practice of item response theory. S.l.: Guilford Press.

Dembitskyi, S. (2016). Sociological tests: the essence and validation. [In Russian]. Sociology: theory, methods, marketing, 3, 140–155. [=Дембицкий 2016].

Dembitskyi, S. (2019). Development of sociological tests: methodology and practice of its application. [In Ukrainian]. Kyiv: Institute of Sociology, National Academy of Sciences of Ukraine. [=Дембіцький 2019].

Dembitskyi, S. (2022). General attitudes towards the general welfare state: concept and measurement. [In Ukrainian]. Preprint provided by the author. [=Дембіцький 2022].

Golovakha, E., Panina, N. (1997). Integral index of social well-being (IISS): Design and application of a sociological test in mass surveys. [In Russian]. Kyiv: Institute of Sociology, National Academy of Sciences of Ukraine. [=Головаха, Панина 1997].

Meade, A.W., Lautenschlager, G.J. (2004). A comparison of item response theory and confirmatory factor analytic methodologies for establishing measurement equivalence/invariance. Organizational Research Methods, 7 (4), 361–388. DOI: https://doi.org/10.1177/1094428104268027

Mindrila, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data. International Journal of Digital Society, 1 (1), 60–66.

Noe-Grijalva, M., Polo-Ambrocio, A., Gómez-Bedia, K., Caycho-Rodríguez, T. (2022). Spanish Translation and Validation of the COVID Stress Scales in Peru. Frontiers in psychology, 13, 840302. DOI: https://doi.org/10.3389/fpsyg.2022.840302

Nugent, W.R. (2017). Understanding DIF and DTF: Description, methods, and implications for social work research. Journal of the Society for Social Work and Research, 8 (2), 305–334. DOI: https://doi.org/10.1086/691525

Paek, I., Cole, K. (2020). Using R for item response theory model applications. S.l.: Routledge.

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. Retrieved from: https://www.R-project.org/

Samejima, F. (1997). Graded response model. In: W.J. Van der Linden, R.K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.

West, S.G., Taylor, A.B., Wu, W. (2012). Model fit and model selection in structural equation modeling. In: R.H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). S.l.: Guilford Press.

Received 10.08.2022