Psychological Research Data Research Articles

Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares.

Multiple imputation (MI) is one of the most popular methods for handling missing data in psychological research. However, many imputation approaches are poorly equipped to handle a large number of variables, which are a common sight in studies that employ questionnaires to assess psychological constructs. In such a case, conventional imputation approaches often become unstable and require that the imputation model be simplified, for example, by removing variables or combining them into composite scores. In this article, we propose an alternative method that extends the fully conditional specification approach to MI with dimension reduction techniques such as partial least squares. To evaluate this approach, we conducted a series of simulation studies, in which we compared it with other approaches that were based on variable selection, composite scores, or dimension reduction through principal components analysis. Our findings indicate that this novel approach can provide accurate results even in challenging scenarios, where other approaches fail to do so. Finally, we also illustrate the use of this method in real data and discuss the implications of our findings for practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models

In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/ . Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.

Brief Research Report: Effects of Sampling Error and Categorization on Estimation of Measure of Sampling Adequacy

The second version of Kaiser’s Measure of Sampling Adequacy (MSA2) has been widely applied to assess the factorability of data in psychological research. The MSA2 is developed in the population and little is known about its behavior in finite samples. If estimated MSA2s are biased due to sampling errors, misleading inferences on the factorability of data are likely to occur. This study investigates the effect of sampling error on MSA2 estimation by systematically examining the accuracy and fluctuations of MSA2 estimates with simulated continuous and ordered categorical data. Features manipulated included the number of factors, number of variables per factor, factor loading, inter-factor correlations, sample size, number of response categories, skewness of variables, and types of correlation analyzed, Pearson and polychoric correlations. Results revealed that the MSA2s were underestimated due to the effect of sampling error. Severely biased MSA2 occurred when analyzing a large number of weakly correlated variables with insufficient participants. The underestimation of MSA2 became worse with categorized data. Polychoric correlations yielded slightly more accurate but relatively unstable MSA2 estimates compared to Pearson correlations. In practice, researchers need to bear in mind the downward bias of MSA2 estimates and interpret the value of sample MSA2s with caution.

An Ethics and Social-Justice Approach to Collecting and Using Demographic Data for Psychological Researchers.

The collection and use of demographic data in psychological sciences has the potential to aid in transforming inequities brought about by unjust social conditions toward equity. However, many current methods surrounding demographic data do not achieve this goal. Some methods function to reduce, but not eliminate, inequities, whereas others may perpetuate harmful stereotypes, invalidate minoritized identities, and exclude key groups from research participation or access to disseminated findings. In this article, we aim to (a) review key ethical and social-justice dilemmas inherent to working with demographic data in psychological research and (b) introduce a framework positioned in ethics and social justice to help psychologists and researchers in social-science fields make thoughtful decisions about the collection and use of demographic data. Although demographic data methods vary across subdisciplines and research topics, we assert that these core issues-and solutions-are relevant to all research within the psychological sciences, including basic and applied research. Our overarching aim is to support key stakeholders in psychology (e.g., researchers, funding agencies, journal editors, peer reviewers) in making ethical and socially-just decisions about the collection, analysis, reporting, interpretation, and dissemination of demographic data.

Using natural language processing and machine learning to replace human content coders.

Content analysis is a common and flexible technique to quantify and make sense of qualitative data in psychological research. However, the practical implementation of content analysis is extremely labor-intensive and subject to human coder errors. Applying natural language processing (NLP) techniques can help address these limitations. We explain and illustrate these techniques to psychological researchers. For this purpose, we first present a study exploring the creation of psychometrically meaningful predictions of human content codes. Using an existing database of human content codes, we build an NLP algorithm to validly predict those codes, at generally acceptable standards. We then conduct a Monte-Carlo simulation to model how four dataset characteristics (i.e., sample size, unlabeled proportion of cases, classification base rate, and human coder reliability) influence content classification performance. The simulation indicated that the influence of sample size and unlabeled proportion on model classification performance tended to be curvilinear. In addition, base rate and human coder reliability had a strong effect on classification performance. Finally, using these results, we offer practical recommendations to psychologists on the necessary dataset characteristics to achieve valid prediction of content codes to guide researchers on the use of NLP models to replace human coders in content analysis research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Exponentially Weighted Moving Average Procedure for Detecting Changes in Intensive Longitudinal Data in Psychological Research in Real-Time: A Tutorial Showcasing Potential Applications.

Affect, behavior, and severity of psychopathological symptoms do not remain static throughout the life of an individual, but rather they change over time. Since the rise of the smartphone, longitudinal data can be obtained at higher frequencies than ever before, providing new opportunities for investigating these person-specific changes in real-time. Since 2019, researchers have started using the exponentially weighted moving average (EWMA) procedure, as a statistically sound method to reach this goal. Real-time, person-specific change detection could allow (a) researchers to adapt assessment intensity and strategy when a change occurs to obtain the most useful data at the most useful time and (b) clinicians to provide care to patients during periods in which this is most needed. The current paper provides a tutorial on how to use the EWMA procedure in psychology, as well as demonstrates its added value in a range of potential applications.

Management und Bereitstellung von Forschungsdaten in der Psychologie: Überarbeitung der DGPs-Empfehlungen

Providing to research data collected as part of publications and publicly funded research projects is now regarded as a central aspect of an open and transparent practice and is increasingly being called for by funding institutions and journals. To this end, researchers should strive to comply with the so-called FAIR principles (of data management), that is, research data should be findable, accessible, interoperable, and reusable. Systematic data management supports these goals and, at the same time, makes it possible to achieve them efficiently. With these revised recommendations on data management and data sharing, which also draw on feedback from a 2018 survey of its members, the German Psychological Society (Deutsche Gesellschaft fur Psychologie; DGPs) specifies important basic principles of data management in psychology. Initially, based on discipline-specific definitions of raw data, primary data, secondary data, and metadata, we provide recommendations on the degree of data processing necessary when publishing data. We then discuss data protection as well as aspects of copyright and data usage before defining the qualitative requirements for trustworthy research data repositories. This is followed by a detailed discussion of pragmatic aspects of data sharing, such as the differences between Type 1 and Type 2 data publications, restrictions on (embargo period), the definition of scientific use by secondary users of shared data, and recommendations on how to resolve potential disputes. Particularly noteworthy is the new recommendation of distinct access categories for data, each with different requirements in terms of data protection or research ethics. These range from completely open data without usage restrictions (access category 0) to data shared under a set of standardized conditions (e.g., reuse restricted to purposes; access category 1), individualized usage agreements (access category 2), and secure data under strictly controlled conditions (e.g., in a research data center; “access category 3). The practical implementation of this important innovation, however, will require data repositories to provide the necessary technical functionalities. In summary, the revised recommendations aim to present pragmatic guidelines for researchers to handle psychological research data in an open and transparent manner, while addressing structural challenges to data sharing solutions that are beneficial for all involved parties.

PsyCuraDat: Designing a User-Oriented Curation Standard for Behavioral Psychological Research Data.

Starting from the observation that data sharing in general and sharing of reusable behavioral data in particular is still scarce in psychology, we set out to develop a curation standard for behavioral psychological research data rendering data reuse more effective and efficient. Specifically, we propose a standard that is oriented toward the requirements of the psychological research process, thus considering the needs of researchers in their role as data providers and data users. To this end, we suggest that researchers should describe their data on three documentation levels reflecting researchers’ central decisions during the research process. In particular, these levels describe researchers’ decisions on the concrete research design that is most suitable to address the corresponding research question, its operationalization as well as a precise description of the subsequent data collection and analysis process. Accordingly, the first documentation level represents, for instance, researchers’ decision on the concrete hypotheses, inclusion/exclusion criteria and the number of measurement points as well as a conceptual presentation of all substantial variables included in the design. On the second level these substantial variables are presented within an extended codebook allowing for the linkage between the conceptual research design and the actually operationalized variables as presented within the data. Finally, the third level includes all materials, data preparation and analyses scripts as well as a detailed procedure graphic that allows the data user to link the information from all three documentation levels at a single glance. After a comprehensive presentation of the standard, we will offer some arguments for its integration into the psychological research process.

The Subject Approach in the Competence Model of Cultural Education

The article is devoted to the need for a broader application of the subject approach regarding the modern higher education. Based on the analysis of the psychological research data, the definitions of the concepts “persona­lity”, “subject”, “subjectivity” were provided. The use of the project tasks for the implementation of the subject approach is justified; the requirements for their development are formulated. The author offers examples of project and problem tasks for the disciplines of the Master’s program referring to the track Culture Studies.

Intelligent analysis of color preferences: search for associative rules vs. cluster analysis

The aim of the article is to present the experience of experimental implementation on the basis of modern software platforms and technologies of two different methods of data mining: (1) the method of associative rules, and (2) the method of clustering. The authors analyze potential and limitations of using both methods in socio-psychological research of color preferences. The material for the conducted experiment was the data of a socio-psychological study in the course of which the subjects (N = 50) were shown a color palette containing 27 different shades and were asked to select from it the colors which, in their opinion, best fit the interior of each of seven different room types: living room, entrance room, bedroom, bathroom, toilet, kitchen, and hallway. By means of the Apriori algorithm we obtained associative rules corresponding to the relationship between color preferences and room types. We discuss the potential of hierarchical clustering method application for obtaining conclusions, which cannot be achieved by calculating percentages. The choice of rules for combining clusters, which give the most objective and informative assessment of responses, is performed. It was proposed to calculate the distance between color choices of respondents using CIEDE2000 color difference metric. By means of concrete examples it is shown that both methods of intellectual analysis open wide possibilities for visualization of the revealed psychological mechanisms and regularities. Experiments were carried out, in the course of which it was established that the chosen methods allow to carry out an effective assessment of social and psychological research data. We can conclude that with a significant increase in the number of respondents and the range of possible variants of answers, the task of finding associations is effectively solved by parallel methods.

Fitting prediction rule ensembles to psychological research data: An introduction and tutorial.

Prediction rule ensembles (PREs) are a relatively new statistical learning method, which aim to strike a balance between predictive performance and interpretability. Starting from a decision tree ensemble, like a boosted tree ensemble or a random forest, PREs retain a small subset of tree nodes in the final predictive model. These nodes can be written as simple rules of the form if [condition] then [prediction]. As a result, PREs are often much less complex than full decision tree ensembles, while they have been found to provide similar predictive performance in many situations. The current article introduces the methodology and shows how PREs can be fitted using the R package pre through several real-data examples from psychological research. The examples also illustrate a number of features of package pre that may be particularly useful for applications in psychology: support for categorical, multivariate and count responses, application of (non)negativity constraints, inclusion of confirmatory rules and standardized variable importance measures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Возможности визуальных методов в качественных психологических исследованиях личности

The article is focused on advantages of using visual methods in qualitative psychological research of personality. The visual turn is not new for the social sciences. Still, this trend is relatively new for psychology. Currently, visual forms of communication have significantly expanded their influence in all areas of culture. This change has led to a rethinking of the strategy of using various visual data in psychological research. In modern qualitative research, visual data is represented by a variety of cultural instruments, from maps, diagrams and matrices, to photographs, videos, collages and drawings. A comprehensive analysis of this problem requires a multimodal approach, representing a combination of different types of communication (audio, visual, textual) and corresponding types of data (spoken words, music, sounds, written words, images, photographs, maps, videos, etc.). This approach lets us set new research tasks and explore new areas, e.g. emotional and bodily components of personal experience and personal changes. The expansion of the scope of visual methods should positively influence the researcher's ethical choices.

ВИКОРИСТАННЯ МОЖЛИВОСТЕЙ МОВИ ПРОГРАМУВАННЯ ТА СЕРЕДОВИЩА R У ПСИХОЛОГІЧНИХ ДОСЛІДЖЕННЯХ: Т’ЮТОРІАЛ З БАЗОВИХ МЕТОДІВ

The article belongs to a special modern genre of scholar publications, so-called tutorials – articles devoted to the application of the latest methods of design, modeling or analysis in an accessible format in order to disseminate best practices. The article acquaints Ukrainian psychologists with the basics of using the R programming language to the analysis of empirical research data. The article discusses the current state of world psychology in connection with the Crisis of Confidence, which arose due to the low reproducibility of empirical research. This problem is caused by poor quality of psychological measurement tools, insufficient attention to adequate sample planning, typical statistical hypothesis testing practices, and so-called “questionable research practices.” The tutorial demonstrates methods for determining the sample size depending on the expected magnitude of the effect size and desired statistical power, performing basic variable transformations and statistical analysis of psychological research data using language and environment R. The tutorial presents minimal system of R functions required to carry out: modern analysis of reliability of measurement scales, sample size calculation, point and interval estimation of effect size for four the most widespread in psychology designs for the analysis of two variables’ interdependence. These typical problems include finding the differences between the means and variances in two or more samples, correlations between continuous and categorical variables. Practical information on data preparation, import, basic transformations, and application of basic statistical methods in the cloud version of RStudio is provided.

Uncovering general, shared, and unique temporal patterns in ambulatory assessment data.

Intensive longitudinal data provide psychological researchers with the potential to better understand individual-level temporal processes. While the collection of such data has become increasingly common, there are a comparatively small number of methods well-suited for analyzing these data, and many methods assume homogeneity across individuals. A recent development rooted in structural equation and vector autoregressive modeling, Subgrouping Group Iterative Multiple Model Estimation (S-GIMME), provides one method for arriving at individual-level models composed of processes shared by the sample, a subset of the sample, and a given individual. As this algorithm was motivated and validated for use with neuroimaging data, its performance is less understood in the context of ambulatory assessment data. Here, we evaluate the performance of the S-GIMME algorithm across various conditions frequently encountered with daily diary (compared to neuroimaging) data; namely, a smaller number of variables, a lower number of time points, and smaller autoregressive effects. We demonstrate, for the first time, the importance of the autoregressive effects in recovering data-generating connections and directions, and the ability to use S-GIMME with lengths of data commonly seen in daily diary studies. We demonstrate the use of S-GIMME with an empirical example evaluating the general, shared, and unique temporal processes associated with a sample of individuals with borderline personality disorder (BPD). Finally, we underscore the need for methods such as S-GIMME moving forward given the increasing use of intensive longitudinal data in psychological research, and the potential for these data to provide novel insights into human behavior and mental health. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Responsible practices for data sharing.

Research transparency, reproducibility, and data sharing uphold core principles of science at a time when the integrity of scientific research is being questioned. This article discusses how research data in psychology can be made accessible for reproducibility and reanalysis by describing practical ways to overcome barriers to data sharing. We examine key issues surrounding the sharing of data such as who owns research data, how to protect the confidentiality of the research participant, how to give appropriate credit to the data creator, how to deal with metadata and codebooks, how to address provenance, and other specifics such as versioning and file formats. The protection of research subjects is a fundamental obligation, and we explain frameworks and procedures designed to protect against the harms that may result from disclosure of confidential information. We also advocate greater recognition for data creators and the authors of program code used in the management and analysis of data. We argue that research data and program code are important scientific contributions that should be cited in the same way as publications. (PsycINFO Database Record

ПСИХОСОЦІАЛЬНІ ОСОБЛИВОСТІ ЖІНОК, ЯКІ ВЧИНИЛИ НАСИЛЬНИЦЬКІ ЗЛОЧИНИ У СТАНІ АЛКОГОЛЬНОГО СП’ЯНІННЯ

This article is dedicated to the issue of female criminality; namely, individual characteristics and structure of violent crimes; and study of possible predictive factors of how such behavior is formed. Today we are able to examine other aspects of female behavior: violent criminal actions, which have earlier been more typical in males. Such changes are stipulated by the fact that women have been accepting more male roles, including consumption of psychoactive substances. The conducted research stipulates that a significant share of aggressive crimes among females are committed in the state of alcohol intoxication and are not always directed against their closest social circle. Crimes committed in the state of alcohol intoxication are characterized exclusively by the absence of any relationships with the victim of a crime; namely, socially dangerous actions committed in the course of joint consumption of alcoholic beverages. As a result of summarizing the psychological research data and materials of criminal proceedings (conclusions of forensic examinations), comparative analysis conducted in women in regards to their characteristics allowed to pinpoint their psychosocial characteristics, peculiarities in relationships with others, the most common type of response in conflict situations, and potential influence of mentioned factors on further criminal behavior. It was established that despite the absence of statistical difference in conditions of upbringing of women (deprivation of parental care, alcoholic parents, etc.), asocial forms of behavior in the past were mainly demonstrated by women who later on committed crimes in the state of alcohol intoxication. The research cites that low level of obtained education, as established, later on reflected such characteristics in women as low level of general knowledge, underdeveloped cognitive activity, which was manifested itself in low-level demands, primitiveness, practicality and narrowness of interests. In this research, we draw attention to characteristics of the victim for the purpose of a more profound analysis of how criminal behavior is formed in females. This analysis proved that one of the predictive factors of violent actions is influence of alcohol on a person. This, along with psychosocial characteristics of females, alters perception of conflicted relationships with others, hinders prediction of far-going consequences of one's own behavior and stipulates the manner of interaction with the environment. Through studying the crime structure and social interaction of females, we have presented an opinion on a crucial affect of alcohol on the behavior during a violation of law. This is especially obvious in how impulsive the choice of weapon is during the offense as well as in post-criminal behavior of offenders.

Multilevel Multivariate Meta-analysis with Application to Choice Overload.

We introduce multilevel multivariate meta-analysis methodology designed to account for the complexity of contemporary psychological research data. Our methodology directly models the observations from a set of studies in a manner that accounts for the variation and covariation induced by the facts that observations differ in their dependent measures and moderators and are nested within, for example, papers, studies, groups of subjects, and study conditions. Our methodology is motivated by data from papers and studies of the choice overload hypothesis. It more fully accounts for the complexity of choice overload data relative to two prior meta-analyses and thus provides richer insight. In particular, it shows that choice overload varies substantially as a function of the six dependent measures and four moderators examined in the domain and that there are potentially interesting and theoretically important interactions among them. It also shows that the various dependent measures have differing levels of variation and that levels up to and including the highest (i.e., the fifth, or paper, level) are necessary to capture the variation and covariation induced by the nesting structure. Our results have substantial implications for future studies of choice overload.

Can Practicing Mindfulness Improve Lawyer Decision-Making, Ethics, and Leadership?

To practice mindfulness is to pay attention in a curious, deliberate, kind, and non-judgmental way to life as it unfolds each moment. Mindfulness is currently very fashionable and has been so for sometime now in American business, education, media, medicine, popular culture, and sports. Many American business and law students, business and law professors, business and law schools, business people and lawyers, and business and legal organizations are considering how mindfulness can be helpful in business, law, and conflict resolution. Much of the popularity of mindfulness stems from (popular media coverage about) empirical, experimental research data in psychology and neuroscience about how practicing mindfulness improves emotional, mental, and physical health by boosting concentration, immune response, and positive affect, while reducing distress, emotional reactivity, and negative affect. This Article analyzes novel theoretical perspectives in financial economics, management science, and leadership studies to analyze how and why mindfulness can improve decision-making, ethics, and leadership. Practicing mindfulness is an experience that provides a temporal space to pause and then make reflective decisions, including sustaining ethical behavior and leadership. To date, there is little empirical or experimental research about how practicing mindfulness affects law students, lawyers, or law professors. There is though a growing body of empirical or experimental research about how practicing mindfulness affects people who are not in the legal profession. Based on this research, this Article makes three recommendations. First, law professors and lawyers team up with neuroscientists and psychologists to conduct multi-methods waitlist controlled research studies involving law students, lawyers, and law professors to determine if practicing mindfulness improves legal decision-making, ethics, and leadership. Second, law students, lawyers, and law professors try practicing mindfulness to see if they improve their legal decision-making, ethics, and leadership. Third, law schools, law firms, and bar associations try offering voluntary mindfulness training and supporting mindfulness practice to see if doing so improves legal decision-making, ethics, and leadership.

Are We Wasting a Good Crisis? The Availability of Psychological Research Data after the Storm

To study the availability of psychological research data, we requested data from 394 papers, published in all issues of four APA journals in 2012. We found that 38% of the researchers sent their data immediately or after reminders. These findings are in line with estimates of the willingness to share data in psychology from the recent or remote past. Although the recent crisis of confidence that shook psychology has highlighted the importance of open research practices, and technical developments have greatly facilitated data sharing, our findings make clear that psychology is nowhere close to being an open science.