jordan

The data stem from the large-scale assessment study PISA 2006
(Organisation for Economic Co-Operation and Development, 2009) where
competencies of 15-year-old students in reading, mathematics, and science
are assessed using nationally representative samples in 3-year cycles.
In this eacademicample, data from the student background questionnaire from the
Jordan sample of PISA 2006 were used. Only data of students with complete
responses to all 15 items (N = 6,038) were considered.

Estimation of interaction (i.e., moderation) effects between latent variables
in structural equation models (SEM).
The supported methods are:
The constrained approach (Algina & Moulder, 2001).
The unconstrained approach (Marsh et al., 2004).
The residual centering approach (Little et al., 2006).
The double centering approach (Lin et al., 2010).
The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000).
The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable)
The constrained- unconstrained, residual- and double centering- approaches
are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches
are estimated via 'modsem' it self. Alternatively model can be
estimated via 'Mplus' (Muthén & Muthén, 1998-2017).
References:
Algina, J., & Moulder, B. C. (2001).
<doi:10.1207/S15328007SEM0801_3>.
"A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3."
Klein, A., & Moosbrugger, H. (2000).
<doi:10.1007/BF02296338>.
"Maximum likelihood estimation of latent interaction effects with the LMS method."
Klein, A. G., & Muthén, B. O. (2007).
<doi:10.1080/00273170701710205>.
"Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects."
Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010).
<doi:10.1080/10705511.2010.488999>.
"Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies."
Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006).
<doi:10.1207/s15328007sem1304_1>.
"On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables."
Marsh, H. W., Wen, Z., & Hau, K. T. (2004).
<doi:10.1037/1082-989X.9.3.275>.
"Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction."
Muthén, L.K. and Muthén, B.O. (1998-2017).
"'Mplus' User’s Guide. Eighth Edition."
<https://www.statmodel.com/>.
Rosseel Y (2012).
<doi:10.18637/jss.v048.i02>.
"'lavaan': An R Package for Structural Equation Modeling."

Kjell Solem Slupphaug

modsem

Latent Interaction (and Moderation) Analysis in Structural
Equation Models (SEM)

Mehmet Mehmetoglu

Matthias Mittner

jordan function

A data frame of fifteen variables and 6,038 observations:
enjoy1
indicator for enjoyment of science, item ST16Q01: I generally have fun when I am learning &lt;broad science&gt; topics.
enjoy2
indicator for enjoyment of science, item ST16Q02: I like reading about &lt;broad science&gt;.
enjoy3
indicator for enjoyment of science, item ST16Q03: I am happy doing &lt;broad science&gt; problems.
enjoy4
indicator for enjoyment of science, item ST16Q04: I enjoy acquiring new knowledge in &lt;broad science&gt;.
enjoy5
indicator for enjoyment of science, item ST16Q05: I am interested in learning about &lt;broad science&gt;.
academic1
indicator for academic self-concept in science, item ST37Q01: I can easily understand new ideas in &lt;school science&gt;.
academic2
indicator for academic self-concept in science, item ST37Q02: Learning advanced &lt;school science&gt; topics would be easy for me.
academic3
indicator for academic self-concept in science, item ST37Q03: I can usually give good answers to &lt;test questions&gt; on &lt;school science&gt; topics.
academic4
indicator for academic self-concept in science, item ST37Q04: I learn &lt;school science&gt; topics quickly.
academic5
indicator for academic self-concept in science, item ST37Q05: &lt;School science&gt; topics are easy for me.
academic6
indicator for academic self-concept in science, item ST37Q06: When I am being taught &lt;school science&gt;, I can understand the concepts very well.
career1
indicator for career aspirations in science, item ST29Q01: I would like to work in a career involving &lt;broad science&gt;.
career2
indicator for career aspirations in science, item ST29Q02: I would like to study &lt;broad science&gt; after &lt;secondary school&gt;.
career3
indicator for career aspirations in science, item ST29Q03: I would like to spend my life doing advanced &lt;broad science&gt;.
career4
indicator for career aspirations in science, item ST29Q04: I would like to work on &lt;broad science&gt; projects as an adult.

jordan: Jordan subset of PISA 2006 data

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