Looking for a PhD candidate in meta-science

Michèle NuijtenJelte M. Wicherts and I are looking for a PhD candidate to assess the statistical validity of psychological intervention studies. The candidate will apply a wide range of state-of-the-art meta-scientific techniques to studies in the domain of medical and clinical psychology. The project can have immediate impact by proposing concrete ways to improve practices in psychological intervention studies. This interdisciplinary PhD project lies at the intersection of methodology and statistics on the one hand, and medical and clinical psychology on the other hand. Apply now: https://tiu.nu/21539

Successfully defended my doctoral dissertation

On Friday 27 January I was awarded my PhD degree with a cum laude distinction at Tilburg University after defending my thesis: “Medical Psychometrics: A psychometric evaluation of Type D personality and its predictive value in medical research”. The digital version of the thesis can be found here.

It was an unforgettable day! Many thanks to my promotor Prof. dr. Jelte M. Wicherts, my former promotor the late Prof. dr. Johan Denollet, my co-promotors Dr. Wilco Emons and Dr. Nina Kupper, my paranymphs Dr. Tom IJdema and Dr. Robbie van Aert, and the members of the doctoral committee: Dr. Stefanie Duijndam, Prof. dr. Brian Hughes, Prof. dr. Timothy Smith, Dr. Mathilde Verdam and Prof. dr. Jeroen Vermunt.

Type D personality as a risk factor for adverse outcome in heart disease

Type D personality, a joint tendency toward negative affectivity (NA) and social inhibition (SI), has been linked to adverse events in patients with heart disease, though with inconsistent findings. In previous work we have shown that traditional methods used to analyse the link between Type D personality and outcome measures can lead to inaccurate conclusions and that half of the published Type D effects are likely merely effects of only NA or SI. In this new publication in Psychosomatic Medicine, we have contacted Type D researchers across the world with the request if they want to share their data investigating the link between Type D personality and adverse outcome in cardiovascular disease. Our aim was to combine these datasets and analyse them with a statistical method that does not suffer from the limitations of traditional methods. In this individual patient-data meta-analysis, we have combined data from 19 published prospective cohort studies (N = 11151), to investigate the prediction of adverse outcomes by Type D personality in acquired cardiovascular disease (CVD) patients. For each outcome (all-cause mortality, cardiac mortality, myocardial infarction (MI), coronary artery bypass grafting, percutaneous coronary intervention, major adverse cardiac event (MACE), any adverse event), we estimated Type D’s prognostic influence and the moderation by age, sex, and disease type. In CVD patients, evidence for a Type D effect in terms of the Bayes factor (BF) was strong for MACE (BF = 42.5; OR = 1.14) and any adverse event (BF = 129.4; OR = 1.15). Evidence for the null hypothesis was found for all-cause mortality (BF = 45.9; OR = 1.03), cardiac mortality (BF = 23.7; OR = 0.99) and MI (BF = 16.9; OR = 1.12), suggesting Type D had no effect on these outcomes. This evidence was similar in the subset of coronary artery disease (CAD) patients, but inconclusive for heart failure (HF) patients. Positive effects were found for NA on cardiac- and all-cause mortality, the latter being more pronounced in males than females. Across 19 prospective cohort studies, Type D predicts adverse events in CAD patients, while evidence in HF patients was inconclusive. In both CAD and HF patients, we found evidence for a null effect of Type D on cardiac- and all-cause mortality. We call for future individual patient data meta-analyses to reanalyse the link between Type D personality and other outcome measures. This will shed light on whether it is really Type D personality, or merely one of its underlying personality traits NA or SI that is driving previously published associations.

How to assess the temporal stability of psychological constructs


In this new publication in the Journal of Research in Personality, we illustrate how to assess temporal stability of psychological constructs. We discuss common methods based on a review of the personality traits negative affectivity and social inhibition. Most methods ignore the non-normal distributions and measurement error in the questionnaire item scores. We illustrate how to handle these issues using three longitudinal latent variable models. We further highlight the importance of testing the often overlooked assumption of longitudinal measurement invariance. Lastly, we apply several longitudinal measurement invariance models, univariate and multivariate latent growth curves models, and latent trait-state occasions models to data from 2625 cancer survivors, to assess the temporal stability of negative affectivity, social inhibition, depression, anxiety, across a period of four years.

Why researchers should not ignore skewness and measurement error in questionnaire scores


Recently I participated in a study for which many scientists were asked to analyse the same dataset to investigate whether the association between religion and wellbeing depends on cultural norms. The paper reporting those results is now published (The MARP team, 2022), together with several commentary articles. In my commentary I showed why researchers should not ignore skewness and measurement error when running statistical analyses based on constructs measured with questionnaire item scores. Researchers commonly study interaction effects between constructs measured with items on an ordinal measurement level and skewed score distributions. Here, I show that ignoring skewness in item scores often produces biased regression estimates, especially for interaction effects. I simulated 800 datasets with scores on two independent (X and Z) and one dependent (Y) latent variables. In the structural model I simulated a main effect (X) and an interaction effect (X*Z) on Y. When generating item scores from a factor model, I varied item skewness by using symmetrical or asymmetrical threshold parameters. Simulation parameters were based on empirical estimates (The MARP team). I analysed each simulated dataset using three methods: (1) a linear regression on sum scores, (2) a structural equation model (SEM) and (3) a SEM for ordered categorical items (CATSEM). Sum score regression produced underestimated effects, especially interactions. SEM was primarily biased when item scores were skewed. CATSEM produced the least biased estimates. These findings highlight the importance of inspecting item score distributions and questionnaire reliability before choosing a statistical model. Researchers are recommended to use CATSEM when testing associations between latent variables that are measured with skewed ordinal item scores. Not doing so risks underestimated associations, especially when estimating interaction effects

Fear of COVID-19 predicts vaccination willingness

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Together with colleagues I investigated the psychological predictors of the willingness to get a COVID-19 vaccination.  Our study was recently published open access in the Journal of Anxiety Disorders. We specifically examined whether fear of COVID-19 predicts vaccination willingness. We followed 938 participants for 14 months and measured their fear for COVID-19 in April 2020 and vaccination willingness was measured in June 2021. Approximately 11% of the participants indicated that they were not willing to get vaccinated. Results of a logistic regression showed that increased fear of COVID-19 predicts vaccination willingness 14 months later, even when controlling for several anxious personality traits, infection control perceptions, risks for loved ones, self-rated health, previous infection, media use, and demographic variables. These results show that fear of COVID-19 is a relevant construct to consider for predicting and possibly influencing vaccination willingness. Nonetheless, sensitivity and specificity of fear of COVID-19 to predict vaccination willingness were quite low and only became slightly better when fear of COVID-19 was measured concurrently. This indicates that other potential factors, such as perceived risks of the vaccines, probably also play a role in explaining vaccination willingness.

Comparing the findings of two popular methods used to estimate a Type D personality effect


In this new study published in General Hospital Psychiatry, we examined the differences between two methods that are commonly used in the literature to estimate a Type D personality effect. Type D personality, operationalised as high scores on negative affectivity (NA) and social inhibition (SI), has been associated with various medical and psychosocial outcomes. The recent failure to replicate several earlier findings could result from the various methods used to assess the Type D effect. Despite recommendations to analyse the continuous NA and SI scores, a popular approach groups people as having Type D personality or not. This method does not adequately detect a Type D effect as it is also sensitive to main effects of NA or SI only, suggesting the literature contains false positive Type D effects. Here, we systematically assess the extent of this problem. We conducted a systematic review including 44 published studies assessing a Type D effect with both a continuous and dichotomous operationalisation. The dichotomous method showed poor agreement with the continuous Type D effect. Of the 89 significant dichotomous method effects, 37 (41.6%) were Type D effects according to the continuous method. The remaining 52 (58.4%) are therefore likely not Type D effects based on the continuous method, as 42 (47.2%) were main effects of NA or SI only. Half of the published Type D effect according to the dichotomous method may be false positives, with only NA or SI driving the outcome. Our findings suggest that a large part of the Type D literature should be reanalysed to find out whether the results remain unchanged when using the continuous method.

Latent logistic interaction modeling of skewed ordinal item scores


In this new study published in the journal Structural Equation Modeling we focused on three popular methods to model interactions between two constructs containing measurement error in predicting an observed binary outcome: logistic regression using (1) observed scores, (2) factor scores, and (3) Structural Equation Modeling (SEM). It is still unclear how they compare with respect to bias and precision in the estimated interaction when item scores underlying the interaction constructs are skewed and ordinal. In this article, we investigated this issue using both a Monte Carlo simulation and an empirical illustration of the effect of Type D personality on cardiac events. Our results indicated that the logistic regression using SEM performed best in terms of bias and confidence interval coverage, especially at sample sizes of 500 or larger. Although for most methods bias increased when item scores were skewed and ordinal, SEM produced relatively unbiased interaction effect estimates when items were modeled as ordered categorical.

You can download the article here.

How to assess a Type D personality effect (part 2)



A new study I published in the journal Personality and Individual Differences is a sequel to my study published in the Journal of Psychosomatic Research, earlier this year. Type D personality has been associated with various medical and psychosocial outcomes. Type D’s underlying personality traits negative affectivity (NA) and social inhibition (SI) are hypothesized to either additively (NA + SI) or synergistically (NA ∗ SI) affect an outcome. As some of the methods used to assess a Type D effect have been criticized in the past, this study aimed to investigate for all commonly used methods their tendency of producing false positive Type D effect. 324,000 datasets were generated using a Monte Carlo Simulation. Each dataset was analyzed using various methods to assess a Type D effect. Each method’s performance was assessed in terms of absolute bias and the percentage of false-positive findings. An online application was developed where readers can easily experiment with this simulation. Our simulation showed that all commonly used methods risk producing false-positive Type D effects. The only method with adequate false-positive rates included the continuous NA and SI main effects, as well as their quadratic effects and their interaction. All commonly used methods to assess a Type D personality effect showed inflated false-positive rates in realistic simulation scenarios. All earlier research based only on these methods should be reconsidered.