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In mixed methods reviewing, data from quantitative and qualitative studies are combined at the review level. One possible way to combine findings of quantitative and qualitative studies is to quantitize qualitative findings prior to their incorporation in a quantitative review. There are only a few examples of the quantification of qualitative findings within this context. This study adds to current research on mixed methods review methodology by reporting the pilot implementation of a new four-step quantitizing approach. We report how we extract and quantitize the strength of relationships found in qualitative studies by assigning correlations to vague quantifiers in text fragments. This article describes (a) how the analysis is prepared; (b) how vague quantifiers in text fragments are organized and transformed to numerical values; (c) how qualitative studies as a whole are assigned effect sizes; and (d) how the overall mean effects size and variance can be calculated. The pilot implementation shows how findings from 26 primary qualitative studies are transformed into mean effect sizes and corresponding variances.

In the Netherlands, the implementation of mandatory procedures in which prospective students do a final check on their initial higher education program choice (so-called matching procedures), were introduced to improve student-program fit. We argue that prospective students who lack feelings of fit with the program during these matching procedures are less likely to finalise their enrolment. Using data of 13 programs at four Dutch universities, the association between various matching procedures and finalising enrolment, and finalising enrolment before and after the implementation of matching were examined. Enrolment rates were lower in programs with more intensive matching procedures and higher in pre-matching cohorts than in matching cohorts, indicating the potential value of pre-enrolment fit checks. In conclusion, this study gives indications that it can be worthwhile to invest in guiding prospective students in their program choice by obliging them to test their fit with the program through intensive matching procedures. [ABSTRACT FROM AUTHOR]

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The Bayes factor provides a method for quantifying the relative evidence for two competing hypotheses that are both instantiated by specific statistical models with prior distributions on the parameters. This general approach can be used to address many specific, theoretically relevant research questions. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: whether a randomized experiment has an effect or not (point null hypothesis), whether an effect is inside or outside a range of negligible effect sizes (interval hypothesis), whether a set of means follows a specific order (informative hypothesis), whether a set of studies jointly corroborate a theoretical claim (evidence synthesis), which variables are most relevant for prediction (variable selection), and which model provides the best account of latent processes (cognitive modeling). We elaborate what each application entails, give illustrative examples with reproducible files for the software R and JASP, and provide an overview of key references and software with links to other applications. We concluded with a discussion of the opportunities and pitfalls of the Bayes factor. [ABSTRACT FROM AUTHOR]

In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this article is therefore to develop a Bayesian model in which a linear regression analysis on current data is augmented with the reported regression coefficients (and standard errors) of previous studies. Two versions of this model are presented. The first version incorporates previous studies through the prior density and is applicable when the current and all previous studies are exchangeable. The second version models all studies in a hierarchical structure and is applicable when studies are not exchangeable. Both versions of the model are assessed using simulation studies. Performance for each in estimating the regression coefficients is consistently superior to using current data alone and is close to that of an equivalent model that uses the data from previous studies rather than reported regression coefficients. Overall the results show that augmenting data with results from previous studies is viable and yields significant improvements in the parameter estimation. (Contains 4 footnotes, 3 tables, and 6 figures.)

To locate objects in the environment, animals and humans use visual and nonvisual information. We were interested in children's ability to relocate an object on the basis of self-motion and local and distal color cues for orientation. Five- to 9-year-old children were tested on an object location memory task in which, between presentation and test, the availability of local and distal cues was manipulated. Additionally, participants' viewpoint could be changed. We used a Bayesian model selection approach to compare our hypotheses. We found that, to remain oriented in space, 5-year-olds benefit from visual information in general, 7-year-olds benefit from visual cues when a viewpoint change takes place, and 9-year-olds do not benefit from the availability of visual cues for orientation but rely on self-movement cues instead. Results are discussed in terms of the adaptive combination model (Newcombe & Huttenlocher, 2006). (Contains 2 tables and 2 figures.)

Null hypothesis testing (NHT) is the most commonly used tool in empirical psychological research even though it has several known limitations. It is argued that since the hypotheses evaluated with NHT do not reflect the research-question or theory of the researchers, conclusions from NHT must be formulated with great modesty, that is, they cannot be stated in a confirmative way. Since confirmation or theory evaluation is, however, what researchers often aim for, we present an alternative approach that is based on the specification of explicit, informative statistical hypotheses. The statistical approach for the evaluation of these hypotheses is a Bayesian model-selection procedure. A non-technical explanation of the Bayesian approach is provided and it will be shown that results obtained with this method give more direct answers to the questions asked and are easier to interpret. An additional advantage of the offered possibility to formulate and evaluate informative hypotheses is that it stimulates researchers to more carefully think through and specify their expectations. (Contains 2 tables and 3 figures.)

In this article, a Bayesian model selection approach is introduced that can select the best of a set of inequality and equality constrained hypotheses for contingency tables. The hypotheses are presented in terms of cell probabilities allowing researchers to test (in)equality constrained hypotheses in a format that is directly related to the data. The proposed method is investigated by several simulation studies and shows good performance. Software that allows researchers to apply the Bayesian approach to their own data is also provided. (Contains 4 figures and 14 tables.)

The authors tested the hypothesis that a match between the gender relevance of a stressor and one's sex or gender role identification would elicit higher cardiovascular responses. Healthy female and male undergraduates (n = 108) were exposed to two stressors: the Cold Pressor Test (CPT) and the n-back task. Stressor relevance was manipulated to be masculine or feminine relevant or gender neutral. Data were analyzed using a Bayesian model selection procedure. The results showed stronger cardiovascular responses for the CPT in the case of a gender match effect. In contrast, results for the n-back task revealed stronger cardiovascular responses for sex and gender mismatch effects. These discrepant match and mismatch effects are discussed in terms of differential task appraisal (i.e., threat vs. challenge). Additional results (a) support the success of measuring gender role identification indirectly by means of the Gender Implicit Association Test, (b) do not show that the effect of stressor relevance is more pronounced on those hemodynamic parameters typically increased by the stressor, and (c) reveal differential effects of stressor relevance for subjective and cardiovascular stress responses. Taken together, it can be concluded that the process of the cognitive appraisal of stressor relevance outlines individual variability in cardiovascular responding to acute stress. (Contains 2 tables and 2 figures.)

Researchers often have one or more theories or expectations with respect to the outcome of their empirical research. When researchers talk about the expected relations between variables if a certain theory is correct, their statements are often in terms of one or more parameters expected to be larger or smaller than one or more other parameters. Stated otherwise, their statements are often formulated using inequality constraints. In this article, a Bayesian approach to evaluate analysis of variance or analysis of covariance models with inequality constraints on the (adjusted) means is presented. This evaluation contains two issues: estimation of the parameters given the restrictions using the Gibbs sampler and model selection using Bayes factors in the case of competing theories. The article concludes with two illustrations: a one-way analysis of covariance and an analysis of a three-way table of ordered means.