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You searched for subject:(Propensity score methods). Showing records 1 – 8 of 8 total matches.

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1. Vukovich, Maria M. Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation.

Degree: PhD, Quantitative Research Methods, 2016, U of Denver

  This mixed methods dissertation explored potential differences in distress levels between newly arrived refugees from Bhutan/Nepal, Burma, Iraq, Somalia and the Democratic Republic of… (more)

Subjects/Keywords: Mental Health Screening; Mixed Methods; Newly Arrived Refugees; Propensity Score Analysis; Multicultural Psychology

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Vukovich, M. M. (2016). Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1196

Chicago Manual of Style (16th Edition):

Vukovich, Maria M. “Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation.” 2016. Doctoral Dissertation, U of Denver. Accessed August 18, 2019. https://digitalcommons.du.edu/etd/1196.

MLA Handbook (7th Edition):

Vukovich, Maria M. “Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation.” 2016. Web. 18 Aug 2019.

Vancouver:

Vukovich MM. Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation. [Internet] [Doctoral dissertation]. U of Denver; 2016. [cited 2019 Aug 18]. Available from: https://digitalcommons.du.edu/etd/1196.

Council of Science Editors:

Vukovich MM. Exploring Ethnocultural Differences in Distress of Newly Arrived Refugees During Early Resettlement: A Mixed Methods Dissertation. [Doctoral Dissertation]. U of Denver; 2016. Available from: https://digitalcommons.du.edu/etd/1196

2. Polson, Ksenia. Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model.

Degree: PhD, Quantitative Research Methods, 2017, U of Denver

  This evaluation utilized propensity score matching methods and a longitudinal hierarchical linear growth model to determine the effect of residential energy efficiency upgrade(s) on… (more)

Subjects/Keywords: Energy Efficiency; Program Evaluation; Propensity Score Matching; Research Methods; Statistics and Probability

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APA (6th Edition):

Polson, K. (2017). Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model. (Doctoral Dissertation). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/1275

Chicago Manual of Style (16th Edition):

Polson, Ksenia. “Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model.” 2017. Doctoral Dissertation, U of Denver. Accessed August 18, 2019. https://digitalcommons.du.edu/etd/1275.

MLA Handbook (7th Edition):

Polson, Ksenia. “Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model.” 2017. Web. 18 Aug 2019.

Vancouver:

Polson K. Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model. [Internet] [Doctoral dissertation]. U of Denver; 2017. [cited 2019 Aug 18]. Available from: https://digitalcommons.du.edu/etd/1275.

Council of Science Editors:

Polson K. Analyzing Electricity Use of Low Income Weatherization Program Participants Using Propensity Score Analysis and a Hierarchical Linear Growth Model. [Doctoral Dissertation]. U of Denver; 2017. Available from: https://digitalcommons.du.edu/etd/1275


University of Minnesota

3. Zhao, Hong. Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis.

Degree: PhD, Biostatistics, 2016, University of Minnesota

 In scientific research, multiple studies on the same intervention occur for many reasons, such as using different study populations or designs. Research synthesis attempts to… (more)

Subjects/Keywords: Bayesian methods; Diagnostic tests; Inconsistency; Network meta-analysis; Propensity score; Randomized and Non-randomized Studies

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APA (6th Edition):

Zhao, H. (2016). Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/182239

Chicago Manual of Style (16th Edition):

Zhao, Hong. “Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis.” 2016. Doctoral Dissertation, University of Minnesota. Accessed August 18, 2019. http://hdl.handle.net/11299/182239.

MLA Handbook (7th Edition):

Zhao, Hong. “Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis.” 2016. Web. 18 Aug 2019.

Vancouver:

Zhao H. Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis. [Internet] [Doctoral dissertation]. University of Minnesota; 2016. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/11299/182239.

Council of Science Editors:

Zhao H. Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis. [Doctoral Dissertation]. University of Minnesota; 2016. Available from: http://hdl.handle.net/11299/182239


Michigan State University

4. Lo, Yun-Jia. How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions.

Degree: 2012, Michigan State University

Thesis Ph. D. Michigan State University. Measurement and Quantitative Methods 2012.

In educational research, a randomized controlled trial is the best design to eliminate potential… (more)

Subjects/Keywords: Regression analysis; Analysis of variance; Statistics; Educational tests & measurements; Propensity score methods; Suppressor variables; Covariates

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APA (6th Edition):

Lo, Y. (2012). How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions. (Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:1606

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lo, Yun-Jia. “How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions.” 2012. Thesis, Michigan State University. Accessed August 18, 2019. http://etd.lib.msu.edu/islandora/object/etd:1606.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lo, Yun-Jia. “How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions.” 2012. Web. 18 Aug 2019.

Vancouver:

Lo Y. How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions. [Internet] [Thesis]. Michigan State University; 2012. [cited 2019 Aug 18]. Available from: http://etd.lib.msu.edu/islandora/object/etd:1606.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lo Y. How a suppressor variable affects the estimation of causal effect : examples of classical and reciprocal suppressions. [Thesis]. Michigan State University; 2012. Available from: http://etd.lib.msu.edu/islandora/object/etd:1606

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Texas A&M University

5. Travis, Jennifer Lynn. A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits.

Degree: PhD, Curriculum and Instruction, 2014, Texas A&M University

 Self-regulated learners continually monitor and adjust the learning process through a recursive loop of forethought, learning enactment, and reflection. The literature review for this study… (more)

Subjects/Keywords: self-regulated learning; study habits; developmental mathematics; community college; higher education; mixed methods; mathematics; mathematics achievement; study journals; propensity score matching

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APA (6th Edition):

Travis, J. L. (2014). A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/152547

Chicago Manual of Style (16th Edition):

Travis, Jennifer Lynn. “A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits.” 2014. Doctoral Dissertation, Texas A&M University. Accessed August 18, 2019. http://hdl.handle.net/1969.1/152547.

MLA Handbook (7th Edition):

Travis, Jennifer Lynn. “A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits.” 2014. Web. 18 Aug 2019.

Vancouver:

Travis JL. A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits. [Internet] [Doctoral dissertation]. Texas A&M University; 2014. [cited 2019 Aug 18]. Available from: http://hdl.handle.net/1969.1/152547.

Council of Science Editors:

Travis JL. A Self-regulated Learning Intervention for Developmental Mathematics Students at a Community College: Effects of Study Journals on Achievement and Study Habits. [Doctoral Dissertation]. Texas A&M University; 2014. Available from: http://hdl.handle.net/1969.1/152547


University of Pennsylvania

6. Tognatta, Namrata. Technical and Vocational Education and Training in India - A Study of Choice and Returns.

Degree: 2014, University of Pennsylvania

 India has made remarkable progress and achieved near universal enrollment in primary school education. However, the quality of learning and progress beyond primary education are… (more)

Subjects/Keywords: India; Instrumental variables; Propensity score matching; Skill development; Survey methods; TVET; Educational Assessment, Evaluation, and Research; Education Policy; Other Education

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Tognatta, N. (2014). Technical and Vocational Education and Training in India - A Study of Choice and Returns. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/1472

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tognatta, Namrata. “Technical and Vocational Education and Training in India - A Study of Choice and Returns.” 2014. Thesis, University of Pennsylvania. Accessed August 18, 2019. https://repository.upenn.edu/edissertations/1472.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tognatta, Namrata. “Technical and Vocational Education and Training in India - A Study of Choice and Returns.” 2014. Web. 18 Aug 2019.

Vancouver:

Tognatta N. Technical and Vocational Education and Training in India - A Study of Choice and Returns. [Internet] [Thesis]. University of Pennsylvania; 2014. [cited 2019 Aug 18]. Available from: https://repository.upenn.edu/edissertations/1472.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tognatta N. Technical and Vocational Education and Training in India - A Study of Choice and Returns. [Thesis]. University of Pennsylvania; 2014. Available from: https://repository.upenn.edu/edissertations/1472

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Tampere University

7. Roto, Ville. Entropy Balancing Autor's (2003) Data on the US Labor Markets .

Degree: 2015, Tampere University

 This thesis studies the impact of employment protection legislature on employment outsourcing via temporary help services. Given the recent trend of companies shifting away from… (more)

Subjects/Keywords: labor economics; employment protection legislature; employment outsourcing; temporary help services; implied contract; unjust dismissal; empirical evaluation; difference-in-differences; propensity score; entropy balancing; randomized controlled trial; data pre-processing methods

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APA (6th Edition):

Roto, V. (2015). Entropy Balancing Autor's (2003) Data on the US Labor Markets . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/97017

Chicago Manual of Style (16th Edition):

Roto, Ville. “Entropy Balancing Autor's (2003) Data on the US Labor Markets .” 2015. Masters Thesis, Tampere University. Accessed August 18, 2019. https://trepo.tuni.fi/handle/10024/97017.

MLA Handbook (7th Edition):

Roto, Ville. “Entropy Balancing Autor's (2003) Data on the US Labor Markets .” 2015. Web. 18 Aug 2019.

Vancouver:

Roto V. Entropy Balancing Autor's (2003) Data on the US Labor Markets . [Internet] [Masters thesis]. Tampere University; 2015. [cited 2019 Aug 18]. Available from: https://trepo.tuni.fi/handle/10024/97017.

Council of Science Editors:

Roto V. Entropy Balancing Autor's (2003) Data on the US Labor Markets . [Masters Thesis]. Tampere University; 2015. Available from: https://trepo.tuni.fi/handle/10024/97017


University of Florida

8. Gurel, Sungur. The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study.

Degree: MA, Research and Evaluation Methodology - Human Development and Organizational Studies in Education, 2012, University of Florida

 We investigated the performance of our different propensity score (PS) methods to reduce selectionbias in estimates of the average treatment effect (ATE) in observational studies:… (more)

Subjects/Keywords: Estimation bias; Estimation methods; Observational research; Observational studies; Population estimates; Proportions; Sample size; Sampling bias; Selection bias; Standard error; ate  – full  – inverse  – matching  – observational  – optimal  – probability  – propensity  – score  – stratification  – studies  – treatment  – truncation  – weighting

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gurel, S. (2012). The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044426

Chicago Manual of Style (16th Edition):

Gurel, Sungur. “The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study.” 2012. Masters Thesis, University of Florida. Accessed August 18, 2019. http://ufdc.ufl.edu/UFE0044426.

MLA Handbook (7th Edition):

Gurel, Sungur. “The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study.” 2012. Web. 18 Aug 2019.

Vancouver:

Gurel S. The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study. [Internet] [Masters thesis]. University of Florida; 2012. [cited 2019 Aug 18]. Available from: http://ufdc.ufl.edu/UFE0044426.

Council of Science Editors:

Gurel S. The Performance of Propensity Score Methods to Estimate the Average Treatment Effect in Observational Studies with Selection Bias a Monte Carlo Simulation Study. [Masters Thesis]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044426

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