Matching Items (3)
Filtering by
- Genre: Diaries--Psychological aspects.
- Genre: Electronic government information
- Creators: Arizona State University
Description
Daily dairies and other intensive measurement methods are increasingly used to study the relationships between two time varying variables X and Y. These data are commonly analyzed using longitudinal multilevel or bivariate growth curve models that allow for random effects of intercept (and sometimes also slope) but which do not address the effects of weekly cycles in the data. Three Monte Carlo studies investigated the impact of omitting the weekly cycles in daily dairy data under the multilevel model framework. In cases where cycles existed in both the time-varying predictor series (X) and the time-varying outcome series (Y) but were ignored, the effects of the within- and between-person components of X on Y tended to be biased, as were their corresponding standard errors. The direction and magnitude of the bias depended on the phase difference between the cycles in the two series. In cases where cycles existed in only one series but were ignored, the standard errors of the regression coefficients for the within- and between-person components of X tended to be biased, and the direction and magnitude of bias depended on which series contained cyclical components.
ContributorsLiu, Yu (Author) / West, Stephen G. (Thesis advisor) / Enders, Craig K. (Committee member) / Reiser, Mark R. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Commitment to an activity is widely studied in leisure research. Serious Leisure Perspective (SLP) describes characteristics a committed activity participant possesses. The Psychological Continuum Model (PCM) describes the psychological process a person goes through to become committed to a leisure activity. Awareness, attraction, attachment and loyalty make of the four stages of PCM. Both perspectives have been used to describe committed leisure activity participants and commitment to organized recreational events. Research on leisure activity has yet to determine how the individual becomes loyal. Therefore, the purpose of this study is to determine the process in which recreation activity participates becomes loyal and to identify who can be labels as serious within the PCM Framework. Data was obtained from an online electronic survey distributed to participants of four U.S. marathon and half marathon events. A total of 579 responses were used in the final analysis. Path analysis determined the process in which a runner becomes committed. MANOVA is used to determine difference between leisure groups in the four stages of PCM. Results indicate that activity participants need to go through all four stages of PCM before becoming loyal. As knowledge increases, individuals are more motivated to participate. When the activity satisfies motives and becomes a reflection of their identity, feelings become stronger which results in loyalty. Socialization is instrumental to the progression through the PCM Framework. Additionally, attachment is the "bottleneck" in which all loyal activity participants my pass through. Differences exist between serious leisure groups in the attachment and loyalty stages. Those that are `less serious' are not as committed to the activity as their counterparts.
ContributorsMurphey, Elizabeth M (Author) / Lee, Woojin (Thesis advisor) / Hultsman, Wendy (Thesis advisor) / Larsen, Dale (Committee member) / Chisum, Jack (Committee member) / Arizona State University (Publisher)
Created2014
Description
The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit individual differences by allowing within-person slopes to vary across individuals. This study examined the statistical performance of the contextual model with a random slope for longitudinal within-person fluctuation data.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
A Monte Carlo simulation was used to generate data based on the contextual multilevel model, where sample size, effect size, and intraclass correlation (ICC) of the predictor variable were varied. The effects of simulation factors on parameter bias, parameter variability, and standard error accuracy were assessed. Parameter estimates were in general unbiased. Power to detect the slope variance and contextual effect was over 80% for most conditions, except some of the smaller sample size conditions. Type I error rates for the contextual effect were also high for some of the smaller sample size conditions. Conclusions and future directions are discussed.
ContributorsWurpts, Ingrid Carlson (Author) / Mackinnon, David P (Thesis advisor) / West, Stephen G. (Committee member) / Grimm, Kevin J. (Committee member) / Suk, Hye Won (Committee member) / Arizona State University (Publisher)
Created2016