Effect size measures for longitudinal growth analyses: Extending a framework of multilevel model R-squareds to accommodate heteroscedasticity, autocorrelation, nonlinearity, and alternative centering strategies

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Disaggregating level-specific effects in cross-classified multilevel models

In psychology and other fields, data often have a cross-classified structure, whereby observations are nested within multiple types of non-hierarchical clusters (e.g., repeated measures cross-classified by persons and stimuli). This paper discusses ways that, in cross-classified multilevel models, slopes of lower-level predictors can implicitly reflect an ambiguous blend of multiple effects (for instance, a purely observation-level effect as well as a unique between-cluster effect for each type of cluster). The possibility of conflating multiple effects of lower-level predictors is well recognized for non-cross-classified multilevel models, but has not been fully discussed or clarified for cross-classified contexts. Consequently, in published cross-classified modeling applications, this possibility is almost always ignored, and researchers routinely specify models that conflate multiple effects. In this paper, we show why this common practice can be problematic, and show how to disaggregate level-specific effects in cross-classified models. We provide a novel suite of options that include fully cluster-mean-centered, partially cluster-mean-centered, and contextual effect models, each of which provides a unique interpretation of model parameters. We further clarify how to avoid both fixed and random conflation, the latter of which is widely misunderstood even in non-cross-classified models. We provide simulation results showing the possible deleterious impact of such conflation in cross-classified models, and walk through pedagogical examples to illustrate the disaggregation of level-specific effects. We conclude by considering additional model complexities that can arise with cross-classification, providing guidance for researchers in choosing among model specifications, and describing newly available software to aid researchers who wish to disaggregate effects in practice.

On the Common but Problematic Specification of Conflated Random Slopes in Multilevel Models

For multilevel models (MLMs) with fixed slopes, it has been widely recognized that a level-1 variable can have distinct between-cluster and within-cluster fixed effects, and that failing to disaggregate these effects yields a conflated, uninterpretable fixed effect. For MLMs with random slopes, however, we clarify that two different types of slope conflation can occur: that of the fixed component (termed fixed conflation) and that of the random component (termed random conflation). The latter is rarely recognized and not well understood. Here we explain that a model commonly used to disaggregate the fixed component-the contextual effect model with random slopes-troublingly still yields a conflated random component. Negative consequences of such random conflation have not been demonstrated. Here we show that they include erroneous interpretation and inferences about the substantively important extent of between-cluster differences in slopes, including either underestimating or overestimating such slope heterogeneity. Furthermore, we show that this random conflation can yield inappropriate standard errors for fixed effects. To aid researchers in practice, we delineate which types of random slope specifications yield an unconflated random component. We demonstrate the advantages of these unconflated models in terms of estimating and testing random slope variance (i.e., improved power, Type I error, and bias) and in terms of standard error estimation for fixed effects (i.e., more accurate standard errors), and make recommendations for which specifications to use for particular research purposes.

Bringing Order to Life: Temporal Order Effects during the Recall of Important Autobiographical Memories in Young and Old Adults

OBJECTIVE

The present study investigated whether younger and older adults differ in the extent to which they order their important autobiographical memories (AMs) in a chronological way during recall. Moreover, the number of autobiographical memories to be recalled was systematically varied in order to examine whether manipulating the size of the search sample affects the use of a chronological recall strategy.

METHODS

Data come from 118 younger adults (M = 23.12, SD = 3.33) and 124 older adults (M = 69.79, SD = 8.94) who recalled either seven or thirteen important AMs.

RESULTS

Using multilevel linear growth models, a temporal order effect for younger and older adults was found. For older adults, the temporal order effect was much more pronounced. The number of recalled events, however, did not affect the temporal order effect.

CONCLUSION

Our findings point to a higher need for a structured search or a stronger engagement in autobiographical reasoning processes in older age. Furthermore, asking for important AMs encourages participants to provide a comprehensive overview of their life independent of the number of important AMs to be recalled.

Characteristics of positive and negative autobiographical memories central to identity: emotionality, vividness, rehearsal, rumination, and reflection

Introduction

Some events are remembered as more central to a person's identity than others. However, it is not entirely clear what characterizes these autobiographical memories central to one's identity. In this study, we examined the effects of various characteristics on centrality to identity of positive and negative memories. Characteristics such as emotionality, vividness, and how frequently a memory is retrieved and shared with others as well as ruminative and reflective self-foci were studied.

Methods

The sample included 356 participants (18-92 years of age). First, participants responded to demographic questions and individual difference questionnaires. Next, they recalled memories in response to 12 emotional cue words. The cue words were balanced for emotional valence (i.e., six positive and six negative) and presented in a random order. After retrieving all memories, participants rated them regarding centrality, using the short seven-item Centrality of Event Scale and other memory characteristics, on a seven-point Likert scale. Multivariate multilevel regression was used for data analyzes, to consider multiple characteristics at the same time and account for data dependency within individual.

Results

The results showed that emotionality, vividness, and frequency of memory retrieval contributed to higher centrality of memories, and employing a reflective self-focus resulted in rating memories as more central. In specific cases, these characteristics were associated differently with centrality of positive and negative memories.

Discussion

Central memories can be perceived as markers in a person's life story. The findings of this study suggest that these marker events are also highly available in a person's memory system, by being actively emotional, visually rich, and frequently retrieved. Moreover, not only memory characteristics but also individual's features are important to fully understand the autobiographical memory centrality.

r2mlm: An R package calculating R-squared measures for multilevel models

Multilevel models are used ubiquitously in the social and behavioral sciences and effect sizes are critical for contextualizing results. A general framework of R-squared effect size measures for multilevel models has only recently been developed. Rights and Sterba (2019) distinguished each source of explained variance for each possible kind of outcome variance. Though researchers have long desired a comprehensive and coherent approach to computing R-squared measures for multilevel models, the use of this framework has a steep learning curve. The purpose of this tutorial is to introduce and demonstrate using a new R package - r2mlm - that automates the intensive computations involved in implementing the framework and provides accompanying graphics to visualize all multilevel R-squared measures together. We use accessible illustrations with open data and code to demonstrate how to use and interpret the R package output.

R-squared Measures for Multilevel Models with Three or More Levels

Applications of multilevel models (MLMs) with three or more levels have increased alongside expanding software capability and dataset availability. Though researchers often express interest in R-squared measures as effect sizes for MLMs, R-squareds previously proposed for MLMs with three or more levels cover a limited subset of choices for how to quantify explained variance in these models. Additionally, analytic relationships between total and level-specific versions of MLM R-squared measures have not been clarified, despite such relationships becoming increasingly important to understand when there are more levels. Furthermore, the impact of predictor centering strategy on R-squared computation and interpretation has not been explicated for MLMs with any number of levels. To fill these gaps, we extend the Rights and Sterba two-level MLM R-squared framework to three or more levels, providing a general set of measures that includes preexisting three-level measures as special cases and yields additional results not obtainable from existing measures. We mathematically and pedagogically relate total and level-specific R-squareds, and show how all total and level-specific R-squared measures in our framework can be computed under any centering strategy. Finally, we provide and empirically demonstrate software (available in the r2mlm R package) to compute measures and graphically depict results.

Examining individual differences in personality trait changes after negative life events

Personality traits can change throughout the entire life span, but people differ in their personality trait changes. To better understand individual differences in personality changes, we examined personal (personality functioning), environmental (environmental changes), and event-related moderators (e.g., perceived event characteristics) of personality trait changes. Therefore, we used a sample of 1069 participants who experienced a negative life event in the last 5 weeks and assessed their personality traits at five measurement occasions over 6 months. Employing preregistered multilevel lasso estimation, we did not find any significant effects. While exploratory analyses generally confirmed this conclusion, they also identified some effects that might being worth to be considered in future research (e.g., perceived impact and perceived social status changes were associated with changes in agreeableness after experiencing a relationship breakup). In total, our moderators explained less than 2% of variance in personality traits. Nonetheless, our study has several important implications for future research on individual differences in personality change. For example, future research should consider personal, environmental, and event-related moderators, use different analytical methods, and rely on highly powered samples to detect very small effects.

Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests

The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This tutorial demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate--the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes--the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen's f2 --that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.

Defining R-squared measures for mixed-effects location scale models

Ecological momentary assessment and other modern data collection technologies facilitate research on both within‐subject and between‐subject variability of health outcomes and behaviors. For such intensively measured longitudinal data, Hedeker et al extended the usual two‐level mixed‐effects model to a two‐level mixed‐effects location scale (MELS) model to accommodate covariates' influence as well as random subject effects on both mean (location) and variability (scale) of the outcome. However, there is a lack of existing standardized effect size measures for the MELS model. To fill this gap, our study extends Rights and Sterba's framework of R2 measures for multilevel models, which is based on model‐implied variances, to MELS models. Our proposed framework applies to two different specifications of the random location effects, namely, through covariate‐influenced random intercepts and through random intercepts combined with random slopes of observation‐level covariates. We also provide an R function, R2MELS, that outputs summary tables and visualization for values of our R2 measures. This framework is validated through a simulation study, and data from a health behaviors study and a depression study are used as examples to demonstrate this framework. These R2 measures can help researchers provide greater interpretation of their findings using MELS models.