Latent crossed-lagged regression mo

Latent crossed-lagged regression models. The aforementioned latent change SEM analyses provide patterns and estimates
of the sources of continuity and change, but they are not informative about the directionality of effects between two variables (e.g.,
between perceived control and Big Five personality traits). To
address this issue, we ran a series of bivariate latent autoregressive
models known as crossed-lagged regression models (McArdle,
2009). These models (seeFigure 3) allow estimations of prospective effects from one variable to another and vice versa controlled
for initial correlations between the variables and their specific
rank-order continuity. In addition, the models allow estimations of
change correlations.

Hasil (Bahasa Indonesia) 1:

Model regresi menyeberangi-lag laten. Analisis SEM perubahan laten tersebut memberikan perkiraan dan polasumber-sumber kontinuitas dan perubahan, tetapi mereka tidak informatif tentang directionality efek antara dua variabel (misalnya,antara dirasakan kontrol dan ciri-ciri kepribadian lima besar). Untukmengatasi masalah ini, kami berlari serangkaian bivariate laten autoregressiveModels dikenal sebagai model regresi menyeberangi-lag (McArdle,2009). model ini (seeFigure 3) memungkinkan estimasi calon efek dari satu variabel lain dan sebaliknya terkendaliuntuk awal korelasi antara variabel dan khusus merekaurutan peringkat kontinuitas. Selain itu, model memungkinkan estimasi darimengubah korelasi.