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In today's world, lifelong learning (LLL) is a key element of individual and societal success. However, despite knowing potential determinants of LLL, we do not yet understand how they interact to facilitate LLL. Therefore, the present study aims to verify the usefulness of the Theory of Planned Behavior (TPB) in predicting LLL. We applied a survey data-based approach by building proxies of TPB components for LLL based on the German PIAAC dataset. Our TPB-based path models explained both participation in non-formal LLL and engagement in informal LLL in a large heterogeneous sample of the German working population, also when controlling for influential socio-demographic determinants of LLL. Thereby, our results provide first evidence that TPB lends itself as core of a LLL process model that can serve as a basis to integrate further well-studied determinants of LLL participation and then can be tested in longitudinal multi-level studies.

Here you find Mplus code, which is used for the development and validation of measurement instruments (questionnaire, test, items, scale) for the social sciences. The description of the analyses carried out with the code can be found in the appendices A1 to A5 of the ZIS Publication Guide. Each code includes comments to guide users through the code. We provide the data set "example1" to run the code.



We provide:

Code for testing the dimensionality of scales comprises exploratory factor analysis, exploratory structural equation modeling (SEM), and confirmatory factor analysis (tau-congeneric and tau-equivalent). For the description of the analyses, see appendices A1 to A2 of the ZIS Publication Guide.


Code used to estimate reliability comprises the estimation of split-half reliability, retest reliability, reliability coefficients for single-factor models (Cronbach's Alpha, McDonald's Omega/Raykov's Rho, AVE [Average Variance Extracted]), and bi-factor models (Omega-H, ECV [Explained Common Variance]). For the description of the analyses, see appendix A3 of the ZIS Publication Guide.


Code for measurement invariance testing within SEM. For the description of the analyses see appendix A5 of the ZIS Publication Guide.

Here you find R code, which is used for the development and validation of measurement instruments (questionnaires, tests, items, scales) for the social sciences. The description of the analyses carried out with the codes can be found in the appendices A1 to A5 of the ZIS Publication Guide. Each code includes comments to guide users through the code. We provide the data set "example1" to run the code.



We provide:

Code for testing the dimensionality of scales comprises exploratory factor analysis, principal component analysis, and confirmatory factor analysis (tau-congeneric and tau-equivalent). For the description of the analyses, see appendices A1 to A2 of the ZIS Publication Guide.


Code used to estimate reliability comprises the estimation of split-half reliability, retest reliability, reliability coefficients for single-factor models (Cronbach's Alpha, McDonald's Omega/Raykov's Rho, AVE [Average Variance Extracted]), and bi-factor models (Omega-H, ECV [Explained Common Variance]). For the description of the analyses, see appendix A3 of the ZIS Publication Guide.

Code for measurement invariance testing within SEM. For the description of the analyses see appendix A5 of the ZIS Publication Guide.

Here you find Stata code, which is used for the development and validation of measurement instruments (questionnaires, tests, items, scales) for the social sciences. The description of the analyses carried out with the code can be found in the appendices A1 to A5 of the ZIS Publication Guide. Each code includes comments to guide users through the code. We provide the data set "example1" to run the code.


We provide:

Code for testing the dimensionality of scales comprises exploratory factor analysis, principal component analysis, and confirmatory factor analysis (tau-congeneric and tau-equivalent). For the description of the analyses, see appendices A1 to A2 of the ZIS Publication Guide.

Code used to estimate reliability comprises the estimation of split-half reliability, retest reliability, reliability coefficients for single-factor models (Cronbach's Alpha, McDonald's Omega/Raykov's Rho, AVE [Average Variance Extracted]), and bi-factor models (Omega-H, ECV [Explained Common Variance]). For the description of the analyses, see appendix A3 of the ZIS Publication Guide.

Code for measurement invariance testing within SEM. For the description of the analyses see appendix A5 of the ZIS Publication Guide.