Investigation of the factor structure of the Turkish version of the State-Trait Anxiety Inventory
Öz
Aim: Confirmatory factor analysis (CFA) and Rasch Analysis are commonly used methods to examine the structure of the psychological scales. In this study, it is aimed to evaluate the factor structure Turkish version of the State-Trait Anxiety Inventory by using statistics based on the Rasch model and CFA.
Methods: The State-Trait Anxiety Inventory (STAI) was used for the analysis. Of the study group, 186 (46.5%) were male and 214 (53.5%) were female. Unidimensionality was investigated using a Rasch-based principal component analysis (PCA) of the residuals, chi-square tests, item fit statistics, and other statistics. CFA has also been applied to test the hypothesis of a one-factor solution.
Results: The item-trait interaction chi-square statistic was 342.344 for the state scale (p<0.001) and 381.247 for the trait scale (p<0.001). For the state scale, 16.00% of the t-tests for the PCA were
significant at the 5% level, while 19.50% were significant for the trait scale. The fit residuals of items 4, 8, and 18 on the state scale were over the +2.5 threshold, while the fit residuals of items 23, 24,
and 34 on the trait scale were above the +2.5 threshold. Similarly, the scale structure evaluated by CFA was conditioned to be inadequate goodness-of-fit.
Conclusion: This study found that neither the trait nor the state scale of the STAI met the unidimensionality assumption. Consequently, both the Rasch analysis and CFA have been verified as succeeding tools in assessing the scale sub-dimensions and determining whether the response items can be utilized for a total scale score.
Anahtar Kelimeler
anxiety;, confirmatory factor analysis, Rasch analysis, the State-Trait Anxiety Inventory, unidimensionality
Destekleyen Kurum
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Kaynakça
- Reckase M. Multidimensional item response theory: Statistics for Social and Behavioral Sciences. New York, US: Springer; 2009.
- Brown TA. Confirmatory factor analysis for applied research. New York, US: Guilford Publications; 2014.
- Brown TA, Moore MT. Confirmatory factor analysis. In: Hoyle RH, editor. Handbook of structural equation modeling. New York, US: Guilford Publications, 2012; p. 361-79.
- Bryant FB, Yarnold PR. Principal-components analysis and exploratory and confirmatory factor analysis. In: Grimm LG, Yarnold PR, eds. Reading and Understanding Multivariate Statistics. Washington DC, US: American Psychological Association, 1995; p. 99-136.
- Thompson B. Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington DC, US: American Psychological Association; 2004.
- Harrington D. Confirmatory factor analysis. New York, US: Oxford University Press; 2009.
- Medvedev ON, Landhuis CE. Exploring constructs of well-being, happiness and quality of life. PeerJ. 2018;1(6):e4903.
- Siegert RJ, Jackson DM, Tennant A, Turner-Stokes L. Factor analysis and Rasch analysis of the Zarit Burden Interview for acquired brain injury carer research. J Rehabil Med. 2010;42(4):302-9.
- Forkmann T, Gauggel S, Spangenberg L, Brähler E, Glaesmer H. Dimensional assessment of depressive severity in the elderly general population: psychometric evaluation of the PHQ-9 using Rasch Analysis. J Affect Disord. 2013;148(2):323-30.
- Wardenaar KJ, van Veen T, Giltay EJ, den Hollander-Gijsman M, Penninx BW, Zitman FG. The structure and dimensionality of the Inventory of Depressive Symptomatology Self Report (IDS-SR) in patients with depressive disorders and healthy controls. J Affect Disord. 2010;125(1):146-54.