How to model interactions between latent variables?

Below is a short summary of a new study I published with my collaborators in the Journal Multivariate Behavioral Research.

Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety

Several approaches exist to model interactions between latent variables. However, it is unclear how these perform when item scores are skewed and ordinal. Research on Type D personality serves as a good case study for that matter. In Study 1, we fitted a multivariate interaction model to predict depression and anxiety with Type D personality, operationalized as an interaction between its two subcomponents negative affectivity (NA) and social inhibition (SI). We constructed this interaction according to four approaches: (1) sum score product; (2) single product indicator; (3) matched product indicators; and (4) latent moderated structural equations (LMS). In Study 2, we compared these interaction models in a simulation study by assessing for each method the bias and precision of the estimated interaction effect under varying conditions. In Study 1, all methods showed a significant Type D effect on both depression and anxiety, although this effect diminished after including the NA and SI quadratic effects. Study 2 showed that the LMS approach performed best with respect to minimizing bias and maximizing power, even when item scores were ordinal and skewed. However, when latent traits were skewed LMS resulted in more false-positive conclusions, while the Matched PI approach adequately controlled the false-positive rate.

Lodder, P., Denollet, J., Emons, W. H., Nefs, G., Pouwer, F., Speight, J., & Wicherts, J. M. (2019). Modeling Interactions Between Latent Variables in Research on Type D Personality: A Monte Carlo Simulation and Clinical Study of Depression and Anxiety. Multivariate behavioral research, 1-29.

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Does money affect our thoughts and behavior?

Below is a short summary of a new study I published with my collaborators in the Journal of Experimental Psychology: General.

A Comprehensive Meta-analysis of Money Priming

Research on money priming typically investigates whether exposure to money-related stimuli
can affect people’s thoughts, feelings, motivations and behaviors. Our study answers the call for a comprehensive meta-analysis examining the available evidence on money priming. By conducting a systematic search of published and unpublished literature on money priming, we sought to achieve three key goals. First, we aimed to assess the presence of biases in the available published literature (e.g., publication bias). Second, in the case of such biases, we sought to derive a more accurate estimate of the effect size after correcting for these biases. Third, we aimed to investigate whether design factors such as prime type and study setting moderated the money priming effects. Our overall meta-analysis included 246 suitable experiments and showed a significant overall effect size estimate (Hedges’ g = .31, 95%CI = [0.26, 0.36]). However, publication bias and related biases are likely given the asymmetric funnel plots, Egger’s test and two other tests for publication bias. Moderator analyses offered insight into the variation of the money priming effect, suggesting for various types of study designs whether the effect was present, absent, or biased. We found the largest money priming effect in lab studies investigating a behavioral dependent measure using a priming technique in which participants actively handled money. Future research should use sufficiently powerful pre-registered studies to replicate these findings.

Lodder, P., Ong, H. H., Grasman, R. P., & Wicherts, J. (2019). A comprehensive meta-analysis of money priming. Journal of Experimental Psychology: General.

Click here to download the preprint

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