Objectives: To examine the relationships within and between commonly used measures ofloneliness to determine the suitability of the measures in older adults. Further, todetermine items of key importance to the measurement of loneliness. Methods: Data wereobtained from 350 older adults via completion of an online survey. Four measures ofloneliness were completed. These were the UCLA Loneliness scale (Version 3), the de JongGierveld Loneliness Scale, the Social and Emotional Loneliness Scale for Adults (ShortVersion) and a direct measure of loneliness. Results: Analysis via a regularized partialcorrelation network and via clique percolation revealed that only the SELSA-Sencompassed loneliness relating to deficits in social, family and romantic relationships. Theremaining measures tapped mostly into social loneliness alone. The direct measure ofloneliness had the strongest connection to the UCLA item-4 and the de Jong Giervelditem-1 exhibited the strongest bridge centrality, being a member of the most clusters.Discussion: The results indicate that should researchers be interested in assessingloneliness resulting from specific relationships, then the SELSA-S would be the mostsuitable measure. Whereas the other measures are suitable for assessing loneliness moregenerally. The results further suggest that the de Jong Gierveld item-1 may be a moresuitable direct measure of loneliness than that currently employed as it taps into a greaternumber of relationships.

Currently in psychological science considerable effort is directed towards confirmatory practices. Much less attention has been devoted to how to do exploratory research. In this article, we support researchers in expanding their methodological toolbox by adding one more technique of exploratory research. The majority of this article is a hands-on tutorial that explains how exploration can be done using state-of-the-art statistical methods, ultimately leading to an in-depth demonstration of machine learning techniques. The practical part of this tutorial explores one of our own datasets, the Human Penguin Project (IJzerman, Lindenberg et al., 2018). The reader can follow the tutorial by recreating our analyses in their own RStudio, apply our annotated code to her own data or other secondary data, and repeat our steps. We show how to get familiar with datasets the researcher wants to use for machine learning, inspect it in many useful ways, and make predictions using machine learning algorithms. We close with describing the limitations related to causal inference and clarifying that finding robust patterns does not equate generating a comprehensive theory. Our tutorial requires basic knowledge of statistics and programming language R (R Core Team, 2016), but we provide resources for absolute beginners.

Objectives: To examine the relationship between number of friends and loneliness, depression, anxiety and stress in older adults. Methods: Data were obtained from 335 older adults via completion of an online survey. Measures included loneliness (UCLA Version 3), depression, stress and anxiety (DASS-21). Participants also reported their number of close friends. Results: Regression analysis revealed a negative curvilinear relationship between number of friends and each of the measures tested. Breakpoint analysis demonstrated a threshold for the effect of number of friends on each of the measures (loneliness = 4, depression = 2, anxiety = 3, stress = 2). Discussion: The results suggest that there is a limit to the benefit of increasing the number of friends in older adults for each of these measures. Elucidating these thresholds can enable loneliness and psychological well-being interventions to be more targeted.

Student satisfaction measures are important for policy. The National Student Survey (NSS) is a tool which often features in such policy decisions. Its most recent revision has been argued to capture eight dimensions (clusters) of student satisfaction. We used the public data from the NSS from 2019 and clustering methods to examine the structure in: a) the overall dataset and b) 78 course subjects. For the overall data, we found a four cluster, rather than eight cluster, solution. At course level, the most common cluster solution was two clusters, but with considerable variation, ranging from one to eight clusters. Our findings thus suggested that there is considerable variation in the structure of the NSS and that this variation can depend on analytical level (overall data vs. specific course subjects). We review the implications of this for the usage of the NSS as a metric for policy.