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Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects./
作者:	Wurpts, Ingrid Carlson.
面頁冊數:	1 online resource (137 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
標題:	Cognitive psychology. -
電子資源:	click for full text (PQDT)
ISBN:	9781369004168

Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects.
Wurpts, Ingrid Carlson.

Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects. - 1 online resource (137 pages)

Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2016.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369004168Subjects--Topical Terms:

556029
Cognitive psychology.
Index Terms--Genre/Form:

554714
Electronic books.

Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects.
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245 1 0 $a Performance of Contextual Multilevel Models for Comparing Between-Person and Within-Person Effects.
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500 $a Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
500 $a Adviser: David P. MacKinnon.
502 $a Thesis (Ph.D.)--Arizona State University, 2016.
504 $a Includes bibliographical references
520 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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