scholarly journals Who Benefits Most From Head Start? Using Latent Class Moderation to Examine Differential Treatment Effects

2014 ◽  
pp. n/a-n/a ◽  
Author(s):  
Brittany Rhoades Cooper ◽  
Stephanie T. Lanza
Keyword(s):  
Head Start ◽  
Treatment Effects ◽  
Latent Class ◽  
Differential Treatment

The Comparative Efficacy of Christian and Secular Rational-Emotive Therapy with Christian Clients

1994 ◽  
Vol 22 (2) ◽  
pp. 130-140 ◽  
Author(s):  
W. Brad Johnson ◽  
Ronald Devries ◽  
Charles R Ridley ◽  
Donald Pettorini ◽  
Deland R. Peterson
Keyword(s):  
Therapeutic Efficacy ◽  
Treatment Effects ◽  
Differential Treatment ◽  
Psychotherapy Outcome ◽  
Comparative Efficacy ◽  
Rational Emotive Therapy ◽  
Negative Thinking ◽  
General Pathology ◽  
Accommodation Theory ◽  
Comparative Psychotherapy

The present study utilized a comparative psychotherapy outcome design to evaluate the relative therapeutic efficacy of Christian and secular RET with depressed Christian clients. Results showed both treatments significantly reduced depression, automatic negative thinking, irrational thinking, and general pathology. These gains were maintained after three months. There were no significant differential treatment effects. Results are discussed in terms of the implications for accommodation theory.

scholarly journals Preschool Intervention Can Promote Sustained Growth in the Executive-Function Skills of Children Exhibiting Early Deficits

2017 ◽  
Vol 28 (12) ◽  
pp. 1719-1730 ◽  
Author(s):  
Tyler R. Sasser ◽  
Karen L. Bierman ◽  
Brenda Heinrichs ◽  
Robert L. Nix
Keyword(s):  
Executive Function ◽  
Head Start ◽  
Latent Class ◽  
Intervention Group ◽  
Third Grade ◽  
Control Group ◽  
Social Emotional ◽  
Usual Practice ◽  
Preschool Intervention ◽  
Latent Class Growth

This study examined the effects of the Head Start Research-Based, Developmentally Informed (REDI) preschool intervention on growth in children’s executive-function (EF) skills from preschool through third grade. Across 25 Head Start centers, each of 44 classrooms was randomly assigned either to an intervention group, which received enhanced social-emotional and language-literacy components, or to a “usual-practice” control group. Four-year-old children ( N = 356; 25% African American, 17% Latino, 58% European American; 54% girls) were followed for 5 years, and EF skills were assessed annually. Latent-class growth analysis identified high, moderate, and low developmental EF trajectories. For children with low EF trajectories, the intervention improved EF scores in third grade significantly more ( d = 0.58) than in the control group. Children who received the intervention also demonstrated better academic outcomes in third grade than children who did not. Poverty often delays EF development; enriching the Head Start program with an evidence-based curriculum and teaching strategies can reduce early deficits and thereby facilitate school success.

scholarly journals Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

2020 ◽  
pp. 107699862095198
Author(s):  
Youmi Suk ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Likelihood Estimation ◽  
Study Data ◽  
Latent Class Models ◽  
Finite Mixture ◽  
Latent Classes ◽  
Heterogeneous Treatment Effects

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

The Use of One or Three Semantic Associative Primes in Treating Anomia in Aphasia

2016 ◽  
Vol 25 (4S) ◽  
Author(s):  
Naomi Hashimoto
Keyword(s):  
Treatment Effects ◽  
Differential Treatment ◽  
The Other ◽  
Effect Sizes ◽  
Single Subject ◽  
Positive Correlation ◽  
Treatment Data ◽  
Prime Condition ◽  
Naming Deficits ◽  
Treatment Design

Purpose The aims of the study were to investigate the use of semantic associative relationships as primes in treating naming deficits, or anomia, in aphasia and to determine if differential treatment effects would be found if 1 or 3 primes were presented. Method Ten individuals with varying degrees of anomia participated in the study. A single-subject, A-B treatment design with a cross-over component was used. A 1-PRIME condition (use of 1 prime) was compared with a 3-PRIMES condition (use of 3 primes) for each participant. Results Visual analyses of treatment data revealed improvements in 8 of 10 participants. Meaningful effect sizes were obtained in at least 1 of the conditions for 6 of the 10 participants. Slightly more participants demonstrated meaningful effect sizes in the 3-PRIMES condition than in the 1-PRIME condition. Correlation analyses revealed a positive correlation between the number of teaching episodes and 3-PRIMES probe performance. Conclusions The results support a protocol that uses semantic associative primes to increase naming accuracy in aphasia. The 3-PRIMES condition was slightly more beneficial than the 1-PRIME condition in terms of improving naming abilities in these participants, but there was no overwhelming advantage in using one or the other condition.

scholarly journals Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables

2016 ◽  
Vol 35 (26) ◽  
pp. 4837-4855 ◽  
Author(s):  
Wei-Yin Loh ◽  
Haoda Fu ◽  
Michael Man ◽  
Victoria Champion ◽  
Menggang Yu
Keyword(s):  
Treatment Effects ◽  
Differential Treatment
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