Using Dynamic Panel Models to Study Age‐related Differences and Time‐related Changes in Personality

Along with an increasing interest in the plasticity and role of personality across the adult lifespan comes the need for a diverse set of innovative statistical approaches to study it. With this paper, we set out to illustrate some of the possibilities and challenges in modelling age‐related differences and time‐related changes in personality psychology by means of dynamic panel models. To this end, we first distinguish between the study of age‐related differences and time‐related changes and demonstrate how the treatment of age and time as either discrete or continuous variables implies important modelling choices. Second, we present a selection of four example cases that address the topic of age moderation in diverse matters and with different objectives. Based on our cross‐tabulation of age and time as discrete and continuous variables, the first two example cases represent fairly well‐established models (cases A and B), whereas the remaining cases are used to illustrate current developments in the field (cases C and D). We close the paper with some final remarks on current limitation and future research directions. © 2019 European Association of Personality Psychology     

A Systematic Review of Online Response Rates in Four Counseling Journals

This systematic review of articles published in 4 counseling journals found 207 articles that used online recruitment, of which 98 (47.3%) reported a response rate. These 98 studies collectively contained 22,466 individual participants. Results showed that nearly one third of all empirical articles used online recruitment, and the average response rate was 34.2% (SD = 22.6). Response rates are further described by journal, population, research methodology, and recruitment strategy. Researcher‐reported issues with online recruitment and data collection were also analyzed qualitatively, and recommendations for researchers are discussed.     

A Monte Carlo evaluation of masked visual analysis in response‐guided versus fixed‐criteria multiple‐baseline designs

We developed masked visual analysis (MVA) as a structured complement to traditional visual analysis. The purpose of the present investigation was to compare the effects of computer‐simulated MVA of a four‐case multiple‐baseline (MB) design in which the phase lengths are determined by an ongoing visual analysis (i.e., response‐guided) versus those in which the phase lengths are established a priori (i.e., fixed criteria). We observed an acceptably low probability (less than .05) of false detection of treatment effects. The probability of correctly detecting a true effect frequently exceeded .80 and was higher when: (a) the masked visual analyst extended phases based on an ongoing visual analysis, (b) the effects were larger, (c) the effects were more immediate and abrupt, and (d) the effects of random and extraneous error factors were simpler. Our findings indicate that MVA is a valuable combined methodological and data‐analysis tool for single‐case intervention researchers.     

Estimating Incremental Validity Under Missing Data

A common form of missing data is caused by selection on an observed variable (e.g., Z). If the selection variable was measured and is available, the data are regarded as missing at random (MAR). Selection biases correlation, reliability, and effect size estimates when these estimates are computed on listwise deleted (LD) data sets. On the other hand, maximum likelihood (ML) estimates are generally unbiased and outperform LD in most situations, at least when the data are MAR. The exception is when we estimate the partial correlation. In this situation, LD estimates are unbiased when the cause of missingness is partialled out. In other words, there is no advantage of ML estimates over LD estimates in this situation. We demonstrate that under a MAR condition, even ML estimates may become biased, depending on how partial correlations are computed. Finally, we conclude with recommendations about how future researchers might estimate partial correlations even when the cause of missingness is unknown and, perhaps, unknowable.     

Sensitive Topics,Missing Data,and Refusal in Social Network Studies: An Ethical Examination

We describe our ethics‐driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis‐sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would‐be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP ) values. We outline literature that was helpful in thinking through this challenge; we looked outside of CP to the decolonization literature on refusal. Lessons learned include listening for the possible meanings of refusals and considering the level of engagement and the labor required of participants when designing research studies.     

Explaining White Opposition to Black Political Leadership: The Role of Fear of Racial Favoritism

Despite the election of America's first Black president, most non‐Hispanic Whites continue to oppose Black political leadership. The conventional explanation for White opposition is sheer racial prejudice, yet the available empirical evidence for this theory is inconsistent. I test an alternative theory that Whites perceive Black political leaders as a threat to their group's interests. Using a new survey measure and nationally representative panel data covering the 2008, 2010, and 2012 U.S. elections, I find that a majority of Whites perceive Black elected officials as likely to favor Blacks over Whites. Moreover, fear of racial favoritism predicts support for Barack Obama in both cross‐sectional models and fixed‐effects models of within‐person change, controlling for negative racial stereotypes. I replicate these findings using a separate cross‐sectional survey fielded after the 2014 election that controls for racial resentment. Collectively, these results suggest that perceptions of conflicting group interests—and not just prejudice—drive White opposition to Black political leadership.