Explained Variance in Multilevel Models

Abstract Although emotions are reported in a large majority of dreams, little is known about the factors that account for night-to-night and person-to-person variations in people’s experience of dream affect. We investigated the relationship between waking trait and state variables and dream affect by testing multilevel models intended to predict the affective valence of people’s everyday dreams. Participants from the general population completed measures of personality and trauma history followed by a three-week daily journal in which they noted dream recall, valence of dreamed emotions and level of perceived stress for the day as well as prior to sleep onset. Within-subject effects accounted for most of the explained variance in the reported valence of dream affect. Trait anxiety was the only variable that significantly predicted dream emotional valence at the between-subjects level. In addition to highlighting the need for more fine-grained measures in this area of research, our results point to methodological limitations and biases associated with retrospective estimates of general dream affect and bring into focus state variables that may best explain observed within-subject variance in emotions experienced in everyday dreams.

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Explained Variance Measures for Multilevel Models

Organizational Research Methods ◽

10.1177/1094428114541701 ◽

2014 ◽

Vol 17 (4) ◽

pp. 433-451 ◽

Cited By ~ 72

Author(s):

David M. LaHuis ◽

Michael J. Hartman ◽

Shotaro Hakoyama ◽

Patrick C. Clark

Keyword(s):

Multilevel Models ◽

Explained Variance

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Explained variance measures for multilevel models.

The handbook of multilevel theory, measurement, and analysis. ◽

10.1037/0000115-016 ◽

2019 ◽

pp. 353-364 ◽

Cited By ~ 1

Author(s):

David M. LaHuis ◽

Caitlin E. Blackmore ◽

Kinsey B. Bryant-Lees

Keyword(s):

Multilevel Models ◽

Explained Variance

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The Relation Between Illegal Risk Behaviors and the Acquired Capability for Suicide

Crisis ◽

10.1027/0227-5910/a000274 ◽

2014 ◽

Vol 35 (6) ◽

pp. 368-377 ◽

Cited By ~ 13

Author(s):

Sean M. Mitchell ◽

Danielle R. Jahn ◽

Kelly C. Cukrowicz

Keyword(s):

College Students ◽

Risk Behaviors ◽

Risky Behaviors ◽

Interpersonal Theory ◽

Interpersonal Theory Of Suicide ◽

Acquired Capability ◽

Self Injury ◽

Partial Correlations ◽

History Of ◽

Explained Variance

Background: Suicide is the third leading cause of death among college students. The interpersonal theory of suicide may provide a way to conceptualize suicide risk in this population. Aims: We sought to examine relations between illegal behaviors that may act as risk factors for suicide and the acquired capability for suicide. Method: College students (N = 758) completed assessments of acquired capability and previous exposure to painful and provocative events, including illegal risk behaviors (IRBs). Linear regression, a nonparametric bootstrapping procedure, and two-tailed partial correlations were employed to test our hypotheses. Results: There was no significant relation between IRBs and acquired capability after controlling for legal painful and provocative experiences. A significant positive relation was identified between IRBs and fear/anxiety, contradicting the expected relation between increased painful and provocative experiences and lower fear/anxiety. Acquired capability explained variance in the relation between IRBs and history of suicide attempt or self-injury history. Conclusion: Further research is needed to examine links between IRBs and painful and provocative events, particularly to identify the point at which habituation begins to increase acquired capability, as our unexpected results may be due to a lack of habituation to risky behaviors or low variability of scores in the sample.

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Memory Processes, Aging, Cognitive Decline, and Neurodegenerative Diseases

European Psychologist ◽

10.1027/1016-9040.11.4.304 ◽

2006 ◽

Vol 11 (4) ◽

pp. 304-311 ◽

Cited By ~ 5

Author(s):

Lars-Göran Nilsson

Keyword(s):

Cognitive Impairment ◽

Cognitive Decline ◽

Neurodegenerative Disease ◽

Genetic Markers ◽

Brain Activity ◽

Memory Performance ◽

Modeling And Simulations ◽

Aging And Health ◽

Explained Variance ◽

Different Levels

This paper presents four domains of markers that have been found to predict later cognitive impairment and neurodegenerative disease. These four domains are (1) data patterns of memory performance, (2) cardiovascular factors, (3) genetic markers, and (4) brain activity. The critical features of each domain are illustrated with data from the longitudinal Betula Study on memory, aging, and health ( Nilsson et al., 1997 ; Nilsson et al., 2004 ). Up to now, early signs regarding these domains have been examined one by one and it has been found that they are associated with later cognitive impairment and neurodegenerative disease. However, it was also found that each marker accounts for only a very small part of the total variance, implying that single markers should not be used as predictors for cognitive decline or neurodegenerative disease. It is discussed whether modeling and simulations should be used as tools to combine markers at different levels to increase the amount of explained variance.

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Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽

10.1027/1614-2241/a000150 ◽

2018 ◽

Vol 14 (3) ◽

pp. 95-108 ◽

Cited By ~ 4

Author(s):

Steffen Nestler ◽

Katharina Geukes ◽

Mitja D. Back

Keyword(s):

Multilevel Model ◽

Multilevel Models ◽

Mixed Effects ◽

Repeated Measurements ◽

Scale Model ◽

Suggested Approach ◽

Psychological Study ◽

Level Data ◽

Ecological Momentary ◽

Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

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