Causal and associational language in observational health research: a systematic evaluation

We estimated the degree to which language used in the high profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched and screened for 1,170 articles from 18 high-profile journals (65 per journal) published from 2010-2019. Based on written framing and systematic guidance, three reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as None (no causal implication) in 13.8%, Weak 34.2%, Moderate 33.2%, and Strong 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was “associate” (45.7%). Reviewer’s ratings of linking word roots were highly heterogeneous; over half of reviewers rated “association” as having at least some causal implication. This research undercuts the assumption that avoiding “causal” words leads to clarity of interpretation in medical research

A Methodological Evaluation of Meta-Analyses in tDCS - Motor Learning Research

With transcranial direct-current stimulation’s (tDCS) rising popularity both in motor learning research and as a commercial product, it is becoming increasingly important that the quality of evidence on its effectiveness be evaluated. Special attention should be paid to meta-analyses, as they usually have a larger impact than other types of studies. Here we provide a review of the methodological quality of meta-analyses estimating the effect of tDCS on motor learning

Bachelor Thesis

Messy data for my bachelor thesis including SPSS analysis files. The thesis itself can be accessed here: https://thesiscommons.org/mjvw

Using Multilevel Regression and Poststratification to Efficiently Derive Accurate Norms

Probability sampling can no longer consistently deliver on the promise of representative samples: soaring nonresponse rates increasingly lead to deviations from the population and higher costs of implementation. This is problematic for psychological test norms as they are only useful to the extent that the norm samples are representative of the target population to which one wishes to generalise. Outside of psychology, Multilevel Regression and Poststratification (MRP) is one of the most widely used methods to correct for sampling bias of any type. MRP involves fitting a regularised prediction model to a large and diverse sample and weighting predicted scores by true population values (sourced from census data, for example). Here, we argue that MRP has the potential to make high-quality test norms more accessible to psychologists by substantially reducing the costs of sampling. Using IQ test data from the TwinLife study (N = 10,059, Culture Fair Test, CFT 20-R) as an example, we show that MRP yields IQ scores that differ by up to 19 IQ points (on average by 4.44 points) from those reported in the CFT 20-R manual, which were based on traditional sampling and norming methods. Differences of this magnitude will not infrequently lead to decision errors with potentially life-altering consequences (e.g., about intellectual disability). We contend that psychology could benefit from more widely available, high-quality norms for many aspects of research and practice and provide this tutorial to facilitate adopting MRP as a cost-efficient norming method

An aberrant abundance of Cronbach's values at .70

Early in the replication crisis, evidence of an excess of barely significant p-values provided evidence of widespread p-hacking (e.g., Masicampo and Lalande, 2012; Hartgerink et al. 2016). We highlight comparable excesses of Cronbach's alpha values at common rules of thumb (e.g. .70), suggesting that alpha-hacking is also occurring

A fragmented field: Construct and measure proliferation in psychology

We examined the extent to which constructs and measures have proliferated in psychological science. We integrated two large databases obtained from the American Psychology Association (APA) that they have used to keep track of constructs, measures, and research in the psychological science literature for the past 30 years. Our descriptive analyses finds that (i) thousands of new constructs and measures are published each year, (ii) most measures are used very few times, and (iii) there is no trend towards consensus or standardization in the use of constructs and measures; in fact, there is a slight trend towards even greater fragmentation over time. That is, constructs and measures are proliferating. We conclude that measurement in the psychological science literature is fragmented, creating problems such as redundancy and confusion, and stifling cumulative scientific progress. We conclude by providing suggestions for what researchers can do about this problem