I am truly honored to have been awarded the Elsevier & EAPM Young Investigator Award for my recently published article in the Journal of Psychosomatic Research. I would like to thank EAPM and Elsevier for making this award possible. Also many thanks to my supervisors Prof. Dr. Jelte Wicherts and Dr. Nina Kupper for their support and inspiration. Lastly, special thanks to the late Prof. Dr. Johan Denollet, without whom this study would never have taken place. I dedicate this award to him.
Below is a short summary of a new study I published in the Journal of Psychosomatic Research. In this study, I investigated several methods used to assess a Type D personality effect and showed that the commonly used methods based on personality subgroups may result in false positive Type D effects.
Modeling synergy: How to assess a Type D personality effect
In research on Type D personality, its subcomponents negative affectivity (NA) and social inhibition (SI) are hypothesized to have a synergistic effect on various medical and psychosocial outcomes. As some methods to analyze Type D personality have been criticized, this study investigated whether these methods adequately detect a Type D effect. We used a simulation and two empirical illustrations to investigate each method’s performance (bias, power and false positives) in detecting the Type D effect. Our simulation showed that the two most commonly used methods to assess the Type D effect (subgroup methods) were primarily picking up the presence of NA or SI main effects, indicating that these methods might falsely suggest synergistic Type D effects. Moreover, these methods failed to detect the combined presence of the NA and SI main effects, resulting in significant Type D effects when only one of the NA/SI main effects was present. The method that best detected Type D effects modeled the continuous NA/SI main effects and their statistical interaction in a regression analysis. Reanalysis of two empirical Type D personality datasets confirmed the patterns found in our simulation. This study showed that Type D effects should be modeled with a continuous interaction approach. Other approaches showed either more bias, more false positive findings or lower power. We recommend against using subgroup approaches to operationalize Type D personality, as these methods are biased, regardless of whether the Type D effect is synergistic or additive in nature.
Lodder, P. (2020). Modeling synergy: How to assess a Type D personality effect. Journal of Psychosomatic Research, 109990.
Below is a short summary of a new study I published with my collaborators in the Journal Multivariate Behavioral Research. The study reports a simulation study investigating the performance of various approaches to model interaction effects in structural equation models. It also includes an empirical investigation of how the two personality traits negative affectivity and social inhibition interact in influencing depressive and anxiety symptoms.
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.
Below is a short summary of a new study I published with my collaborators in the Journal of Experimental Psychology: General. In this study we combined the results of dozens of studies investigating how money may affect our thoughts, emotions and behaviors, including the results of 100 unpublished experiments. Overall, this influence of money appeared to be important. However, these results should be interpreted carefully because published studies showed much larger effects than unpublished studies, a phenomenon called publication bias.
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.
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