Explained Variance Measures for Multilevel Models

LaHuis, David M.; Hartman, Michael J.; Hakoyama, Shotaro; Clark, Patrick C.

doi:10.1177/1094428114541701

Cited by 219 publications

(157 citation statements)

References 28 publications

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“…Overall, according to the pseudo R 2 statistic (LaHuis et al, 2014), age, control variables, job characteristics, and interactions between age and job characteristics explained 30% of the variance in job attraction (see Table 3). …”

Section: Resultsmentioning

confidence: 99%

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

et al. 2017

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Based on an integration of job design and lifespan developmental theories, Truxillo et al. (2012) proposed that job characteristics interact with employee age in predicting important work outcomes. Using an experimental policy-capturing design, we investigated age-differential effects of four core job characteristics (i.e., job autonomy, task variety, task significance, and feedback from the job) on job attraction (i.e., individuals' rating of job attractiveness). Eighty-two employees between 19 and 65 years (Mage = 41, SD = 14) indicated their job attraction for each of 40 hypothetical job descriptions in which the four job characteristics were systematically manipulated (in total, participants provided 3,280 ratings). Results of multilevel analyses showed that the positive effects of task variety, task significance, and feedback from the job were stronger for younger compared to older employees, whereas we did not find significant age-differential effects of job autonomy on job attraction. These findings are only partially consistent with propositions of Truxillo et al.'s (2012) lifespan perspective on job design.

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Section: Resultsmentioning

confidence: 99%

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

et al. 2017

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“…We modeled heterogeneity in the average levels of the response variables and the effects of social distance as random effects, using linear mixed models algorithms provided by the package lme4 (Bates et al, 2014) for the software R (R Core Team, 2014). To obtain standardized effect sizes, we used a function provided by LaHuis et al (2014) which calculates the approximate explained variance at Level 1.…”

Section: Resultsmentioning

confidence: 99%

Expectations and Decisions in the Volunteer’s Dilemma: Effects of Social Distance and Social Projection

Krueger

Ullrich

Chen³

2016

Front. Psychol.

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In a Volunteer’s Dilemma (VoD) one individual needs to bear a cost so that a public good can be provided. Expectations regarding what others will do play a critical role because they would ideally be negatively correlated with own decisions; yet, a social-projection heuristic generates positive correlations. In a series of 2-person-dilemma studies with over 1,000 participants, we find that expectations are indeed correlated with own choice, and that people tend to volunteer more than game-theoretic benchmarks and their own expectations would allow. We also find strong evidence for a social-distance heuristic, according to which a person’s own probability to volunteer and the expectation that others will volunteer decrease as others become socially more remote. Experimentally induced expectations make opposite behavior more likely, but respondents underweight these expectations. As a result, there is a small but systematic effect of over-volunteering among psychologically close individuals.

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“…These previous articles featured generalized linear mixed-effects models (GLMMs) as the most versatile engine for estimating R 2 and ICC (specifically R 2 GLMM and ICC GLMM ). Our descriptions were limited to random-intercept GLMMs, but Johnson [4] has recently extended the methods to random-slope GLMMs, widening the applicability of these statistics (see also, [5, 6]).…”

Section: Introductionmentioning

confidence: 99%

The coefficient of determinationR²and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

Nakagawa

Johnson

Schielzeth

2016

Preprint

262

311

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The coefficient of determination R2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R2 that we called R2GLMM for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients ICC using Poisson and binomial GLMMs. In this article, we expand our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen’s inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen’s inequality has important implications for biologically meaningful interpretation of GLMMs, while the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.

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Explained Variance Measures for Multilevel Models

Cited by 219 publications

References 28 publications

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

Expectations and Decisions in the Volunteer’s Dilemma: Effects of Social Distance and Social Projection

The coefficient of determinationR²and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

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Explained Variance Measures for Multilevel Models

Cited by 219 publications

References 28 publications

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

Age-Differential Effects of Job Characteristics on Job Attraction: A Policy-Capturing Study

Expectations and Decisions in the Volunteer’s Dilemma: Effects of Social Distance and Social Projection

The coefficient of determinationR2and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

Contact Info

Product

Resources

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The coefficient of determinationR²and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded