Effect Size, Statistical Power, and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis

Dziak, John J.; Lanza, Stephanie T.; Tan, Xianming

doi:10.1080/10705511.2014.919819

Cited by 333 publications

(245 citation statements)

References 57 publications

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“…Although the profiles differed with regard to levels of self-reported empowering and disempowering coach behaviors, the total number of coaches, particularly in the least empowering profile, was very low. Nevertheless, simulation studies have shown that when the separation between profiles are high (e.g., entropy >.75) sample sizes around 140 is needed to reach sufficient power (Dziak, Lanza, & Tan, 2014;Gudicha, Tekle, & Vermunt, 2016). Given the high entropy in our study it is likely that we have adequate power to find the best solution.…”

Section: Limitationsmentioning

confidence: 98%

Youth Sport Coaches’ Well-Being Across the Season: The Psychological Costs and Benefits of Giving Empowering and Disempowering Sports Coaching to Athletes

Solstad

Ivarsson

Haug

et al. 2018

International Sport Coaching Journal

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The purpose of this study was to investigate the associations between giving empowering and disempowering sports coaching to young athletes and coaches' well-being across the season. The sample comprised 169 Norwegian youth football (i.e., European soccer) coaches with a mean age of 41.99 (SD = 6.32). Moreover, we were interested in examining heterogeneous groups of coaches showing variability in their self-reporting of empowering and disempowering behaviors towards their athletes. Thus, a person-centered approach was used. The latent profile analysis revealed three distinct profiles and the association between these profiles and coaches' well-being was in line with the outlined hypotheses. Specifically, coaches who gave higher levels of empowering and lower levels of disempowering sports coaching to their athletes at the beginning of the season also reported higher levels of well-being at the end of the season. The results indicate that there exists an intrinsic value as to why coaches should give empowering sports coaching, as opposed to disempowering sports coaching, to their athletes; namely, these actions may be advantageous in terms of improving their own well-being. In practical terms, future coach education may take advantage of these findings by providing coaches another reason for coaching in an empowering manner.

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Section: Limitationsmentioning

confidence: 98%

Youth Sport Coaches’ Well-Being Across the Season: The Psychological Costs and Benefits of Giving Empowering and Disempowering Sports Coaching to Athletes

Solstad

Ivarsson

Haug

et al. 2018

International Sport Coaching Journal

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show abstract

“…By contrast, a statistically significant LMR-LRT value is indicative of better fit. With the BLRT, this statistic helps to evaluate whether a model improves significantly from the model with k -1 classes, where k is the number of classes for each analysis and there is an assessment as to whether a more parsimonious fit is available (Asparouhov & Muthén, 2012;Dziak, Lanza, & Tan, 2014). The entropy value (i.e.…”

Section: The Process Of Undertaking An Lcamentioning

confidence: 99%

Latent Class Analysis and Latent Profile Analysis

Williams¹,

Kibowski²

2015

Handbook of Methodological Approaches to Community-Based Research

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This is a draft of a chapter that has been accepted for publication by Oxford University Press in the forthcoming book Handbook of methodological approaches to community-based research:Qualitative, quantitative, and mixed methods, edited by L. A. Jason and D. S. Glenwick and due for publication in 2016.2 Latent class analysis (LCA) and latent profile analysis (LPA) are powerful techniques that enable researchers to glean insights into "hidden" psychological experiences to create typologies and profiles to provide better-informed community-based policies and practice. These analytic methods have been used in a variety of domains, such as: psychosis symptomatology in the general population (Kibowski & Williams, 2012;Shevlin, Murphy, Dorahy, & Adamson, 2007); substance abuse (Cleveland, Collins, Lanza, Greenberg, & Feinberg, 2010;James, McField, & Montgomery, 2013), peer victimization (Nylund, Bellmore, Nishina, & Graham, 2007), and anti-social/self-defeating behavior (Rosato & Baer, 2010). LCA and LPA are versatile methods of dealing with data of interest to community-based researchers in a deep and psychologically grounded way. This chapter will address the nuances of how and when to use LCA and LPA. Case studies of LCA and LPA will also be presented to illustrate the applicability of these techniques. Introduction to Latent Class AnalysisThe main aim of LCA is to split data that are apparently homogeneous overall into subclasses of two or more different homogeneous groups or classes. Study participant responses to a questionnaire, structured interview, or behavioral checklist would be used as the basis for making probabilistic assessments of the likelihood of each participant being assigned to one of these classes. A participant's likelihood of belonging to any of the other latent classes would also be calculated, and then decisions would be made as to the ultimate class membership that each respondent would assume. The beneficial role that LCA can have is that, once class membership has been assigned to each participant in relation to the pattern of responses or behaviors, this class membership can be used to inform policies and practice-based interventions aimed at targeting a specific latent class that has emerged from the analysis. An example of the 3 potential for this method can be seen in a study of the transportation-related attitudes and experiences of workers (Williams, Murphy, & Hill, 2008). In this study, latent class analysis was deployed to examine the role of multimodality (i.e. using more than one mode of transportation) versus single transport mode use on commuters' psychological well-being.Other community-level analyses have utilized LCA to investigate how to encourage sections of the population to engage more in community-based arts activities (Biggins, Cottee, & Williams, 2012). LCA is also helpful for testing population-wide phenomena and epidemiological trends, such as the potential existence of psychosis symptom experiences being measured along a continuum throughout the general populati...

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“…Generacijska svijest o specifičnim povijesnim događajima i njihovo iskustvo umnogome se temelji na tome što prenose mediji te na zajedničkim rutinama i upotrebama (informativnih) medija (Westlund i Weibull, 2013: 148). Treba uzeti u obzir i činjenicu da je uzorak na kojem je provedena analiza latentnih klasa razmjerno malen za tu vrstu analize (Diziak, Lanza i Tan, 2014). Zbog toga identificirane klase nisu nužno i najbolja aproksimacija stvarnih medijskih repertoara hrvatskih publika.…”

Section: Zaključakunclassified

Rhythms of Media Generations in Croatia: Research on the Repertoires of Media Generations from the Sociological Perspective

Čuvalo

Peruško

2018

revsoc

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Effect Size, Statistical Power, and Sample Size Requirements for the Bootstrap Likelihood Ratio Test in Latent Class Analysis

Cited by 333 publications

References 57 publications

Youth Sport Coaches’ Well-Being Across the Season: The Psychological Costs and Benefits of Giving Empowering and Disempowering Sports Coaching to Athletes

Youth Sport Coaches’ Well-Being Across the Season: The Psychological Costs and Benefits of Giving Empowering and Disempowering Sports Coaching to Athletes

Latent Class Analysis and Latent Profile Analysis

Rhythms of Media Generations in Croatia: Research on the Repertoires of Media Generations from the Sociological Perspective

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