Introduction to Multilevel Analysis

Leeuw, Jan de; Meijer, Erik

doi:10.1007/978-0-387-73186-5_1

Cited by 81 publications

(75 citation statements)

References 80 publications

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“…For these models, group-mean centering of lower-level predictors is recommended (Aguinis, Gottfredson, & Culpepper, 2013;Raudenbush & Bryk, 2002) because it allows the most unambiguous interpretation of crosslevel interactions. Some authors argue that the group means of lowerlevel predictors should also be entered as covariates at the higher level (de Leeuw & Meijer, 2008;Kreft et al, 1995). The additional inclusion of group means is most relevant when researchers are interested in assessing contextual effects (Lüdtke et al, 2008).…”

Section: Notesmentioning

confidence: 98%

Agency‐Communion and Interest in Prosocial Behavior: Social Motives for Assimilation and Contrast Explain Sociocultural Inconsistencies

Gebauer

Sedikides

Lüdtke

et al. 2013

Journal of Personality

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Identifying the "prosocial personality" is a classic project in personality psychology. However, personality traits have been elusive predictors of prosocial behavior, with personality-prosociality relations varying widely across sociocultural contexts. We propose the social motives perspective to account for such sociocultural inconsistencies. According to this perspective, a focal quality of agency (e.g., competence, independence, openness) is the motive to swim against the social tide-agentic social contrast. Conversely, a focal quality of communion (e.g., warmth, interdependence, agreeableness) is the motive to swim with the social tide-communal social assimilation. We report two cross-sectional studies. Study 1 (N = 131,562) defined social context at the country level (11 European countries), whereas Study 2 (N = 56,395) defined it at the country level (11 European countries) and the city level (296 cities within these countries). Communion predicted interest in prosocial behavior comparatively strongly in sociocultural contexts where such interest was common and comparatively weakly where such interest was uncommon. Agency predicted interest in prosocial behavior comparatively strongly in sociocultural contexts where such interest was uncommon and comparatively weakly where such interest was common. The results supported the social motives perspective. Also, the findings help to reestablish the importance of personality for understanding prosociality.

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Section: Notesmentioning

confidence: 98%

Agency‐Communion and Interest in Prosocial Behavior: Social Motives for Assimilation and Contrast Explain Sociocultural Inconsistencies

Gebauer

Sedikides

Lüdtke

et al. 2013

Journal of Personality

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“…There were 2 moments for the WL group and 5 for the CBT and iCBT groups. MLM is appropriated to deal with measures at varying time points (De Leeuw and Meijer, 2008). The treatment effects were evaluated at Level 2 (coded, 0 ¼ WL, 1 ¼ CBT, 2 ¼ iCBT).…”

Section: Statistical Analysis and Sample Sizementioning

confidence: 99%

Internet versus face-to-face group cognitive-behavioral therapy for fibromyalgia: A randomized control trial

Vallejo

Ortega

Rivera

et al. 2015

Journal of Psychiatric Research

165

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“…Given the estimated variance componentsτ and the estimated smoothing parametersθ andλ, it is rather simple to obtain estimates (predictions) of the random effect realizations. Using standard linear mixed-model theory (see Wang 1998a;Zhang et al 1998;de Leeuw and Meijer 2008;Verbeke and Molenberghs 2000), the best linear unbiased predictor (BLUP) of b has the form…”

Section: Penalized Generalized Least Squaresmentioning

confidence: 99%

Efficient estimation of variance components in nonparametric mixed-effects models with large samples

Helwig

2015

Stat Comput

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Linear mixed-effects (LME) regression models are a popular approach for analyzing correlated data. Nonparametric extensions of the LME regression model have been proposed, but the heavy computational cost makes these extensions impractical for analyzing large samples. In particular, simultaneous estimation of the variance components and smoothing parameters poses a computational challenge when working with large samples. To overcome this computational burden, we propose a two-stage estimation procedure for fitting nonparametric mixed-effects regression models. Our results reveal that, compared to currently popular approaches, our two-stage approach produces more accurate estimates that can be computed in a fraction of the time.

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Introduction to Multilevel Analysis

Cited by 81 publications

References 80 publications

Agency‐Communion and Interest in Prosocial Behavior: Social Motives for Assimilation and Contrast Explain Sociocultural Inconsistencies

Agency‐Communion and Interest in Prosocial Behavior: Social Motives for Assimilation and Contrast Explain Sociocultural Inconsistencies

Internet versus face-to-face group cognitive-behavioral therapy for fibromyalgia: A randomized control trial

Efficient estimation of variance components in nonparametric mixed-effects models with large samples

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