scholarly journals Differential effects of formative feedback on math achievement and their predictors: Application of regression mixture analysis

2021 ◽  
Vol 24 (4) ◽  
pp. 269-290
Author(s):  
Minae Park ◽  
Wonsook Sohn
Keyword(s):  
Math Achievement ◽  
Formative Feedback ◽  
Mixture Analysis ◽  
Differential Effects ◽  
Regression Mixture

scholarly journals Assessing differential effects: Applying regression mixture models to identify variations in the influence of family resources on academic achievement.

2009 ◽  
Vol 45 (5) ◽  
pp. 1298-1313 ◽  
Author(s):  
M. Lee Van Horn ◽  
Thomas Jaki ◽  
Katherine Masyn ◽  
Sharon Landesman Ramey ◽  
Jessalyn A. Smith ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Mixture Models ◽  
Family Resources ◽  
Differential Effects ◽  
Regression Mixture Models ◽  
Regression Mixture

Parenting behaviors and child psychopathy: A regression mixture analysis

2020 ◽  
Author(s):  
Jiaxin Deng ◽  
Meng-Cheng Wang ◽  
Yiyun Shou ◽  
Hongyu Lai ◽  
Hong Zeng ◽  
...  
Keyword(s):  
Parenting Behaviors ◽  
Mixture Analysis ◽  
Child Psychopathy ◽  
Regression Mixture

scholarly journals Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

2014 ◽  
Vol 75 (4) ◽  
pp. 677-714 ◽  
Author(s):  
M. Lee Van Horn ◽  
Thomas Jaki ◽  
Katherine Masyn ◽  
George Howe ◽  
Daniel J. Feaster ◽  
...  
Keyword(s):  
Mixture Models ◽  
Differential Effects ◽  
Regression Mixture Models ◽  
Regression Mixture

Assessing Differential Effects of Somatic Amplification to Positive Affect in Midlife and Late Adulthood—A Regression Mixture Approach

2021 ◽  
pp. 009141502110665
Author(s):  
Minjung Kim ◽  
Menglin Xu ◽  
Junyeong Yang ◽  
Susan Talley ◽  
Jen D. Wong
Keyword(s):  
Positive Affect ◽  
Mixture Model ◽  
Latent Class ◽  
Model Building ◽  
The United States ◽  
Aging Research ◽  
Statistical Tool ◽  
Differential Effects ◽  
Somatic Amplification ◽  
Regression Mixture

This study aims to provide an empirical demonstration of a novel method, regression mixture model, by examining differential effects of somatic amplification to positive affect and identifying the predictors that contribute to the differential effects. Data derived from the second wave of Midlife in the United States. The analytic sample consisted of 1,766 adults aged from 33 to 84 years. Regression mixture models were fitted using Mplus 7.4, and a two-step model-building approach was adopted. Three latent groups were identified consisting of a maladaptive (32.1%), a vulnerable (62.5%), and a resilient (5.4%) group. Six covariates (i.e., age, education level, positive relations with others, purpose in life, depressive symptoms, and physical health) significantly predicted the latent class membership in the regression mixture model. The study demonstrated the regression mixture model to be a flexible and efficient statistical tool in assessing individual differences in response to adversity and identifying resilience factors, which contributes to aging research.

Self-Regulatory Climate: A Social Resource for Student Regulation and Achievement

2015 ◽  
Vol 117 (2) ◽  
pp. 1-28 ◽  
Author(s):  
Curt M. Adams ◽  
Patrick B. Forsyth ◽  
Ellen Dollarhide ◽  
Ryan Miskell ◽  
Jordan Ware
Keyword(s):  
Math Achievement ◽  
Self Regulation ◽  
Psychological Needs ◽  
School Level ◽  
Differential Effects ◽  
Self Regulated Learning ◽  
Regulatory Climate ◽  
Ex Post ◽  
Regulated Learning ◽  
The Relationship

Background/Context Schools have differential effects on student learning and development, but research has not generated much explanatory evidence of the social-psychological pathway to better achievement outcomes. Explanatory evidence of how normative conditions enable students to thrive is particularly relevant in the urban context where attention disproportionately centers on the pathology of these environments rather than social attributes that contribute to student growth. Research Purpose Our purpose in this study was to determine if a self-regulatory climate works through student self-regulation to influence academic achievement. We hypothesized that (1) self-regulatory climate explains school-level differences in self-regulated learning, and (2) self-regulated learning mediates the relationship between self-regulatory climate and math achievement. Research Design We used ex post facto survey data from students and teachers in 80 elementary and secondary schools from a large, southwestern urban school district. A multilevel modeling building process in HLM 7.0 was used to test our hypotheses. Results Both hypotheses were supported. Self-regulatory climate explained significant school-level variance in self-regulated learning. Additionally, student self-regulated learning mediated the relationship between self-regulatory climate and math achievement. Conclusions Our results suggest that schools, like teachers, have differential effects on the motivational resources of students, with self-regulatory climate being an essential social condition for self-regulation and achievement. We believe self-regulatory climate has value for educators seeking to provide equitable learning opportunities for all students and for researchers seeking to account for achievement differences attributed to schools. In both cases, self-regulatory climate advances a construct and measure that conceptualizes and operationalizes school-level support for psychological needs.

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