This document provides information on using binary logistic regression and linear regression in SPSS. It explains that binary logistic regression is used to predict a dichotomous dependent variable, like yes/no outcomes. Examples are provided of using it to predict vaccination status and surgical infection complications. Linear regression is described as appropriate when the dependent variable is continuous. The document offers tips for conducting and interpreting the analyses in SPSS, like transforming categorical variables, checking for significance of models, and interpreting coefficient outputs. Examples are presented of using both techniques to analyze factors influencing surgical infection rates and Babesia infection in dogs.
Propensity Score Matching Using SAS Enterprise GuideIan Morton
The document discusses using propensity score matching to estimate the difference in an outcome between two groups while controlling for biases from differences in their characteristics. It describes a two-step process: 1) using logistic regression to calculate propensity scores representing the probability of being in the treatment group based on background characteristics, and 2) matching individuals in the treatment and control groups based on similar propensity scores. Examples are given of using this method to estimate differences in recidivism rates, medical outcomes, and dropout rates while accounting for differences in offender characteristics, health factors, and country of study.
This document provides information on using binary logistic regression and linear regression in SPSS. It explains that binary logistic regression is used to predict a dichotomous dependent variable, like yes/no outcomes. Examples are provided of using it to predict vaccination status and surgical infection complications. Linear regression is described as appropriate when the dependent variable is continuous. The document offers tips for conducting and interpreting the analyses in SPSS, like transforming categorical variables, checking for significance of models, and interpreting coefficient outputs. Examples are presented of using both techniques to analyze factors influencing surgical infection rates and Babesia infection in dogs.
Propensity Score Matching Using SAS Enterprise GuideIan Morton
The document discusses using propensity score matching to estimate the difference in an outcome between two groups while controlling for biases from differences in their characteristics. It describes a two-step process: 1) using logistic regression to calculate propensity scores representing the probability of being in the treatment group based on background characteristics, and 2) matching individuals in the treatment and control groups based on similar propensity scores. Examples are given of using this method to estimate differences in recidivism rates, medical outcomes, and dropout rates while accounting for differences in offender characteristics, health factors, and country of study.
20. TEPS理論架構之詮釋:
Aage B. Sørensen學習機會論
• Hallinan, M. T. (2004). “Sørensen’s learning opportunities model: Theoretical
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A. Rosenfeld (Eds), Inequality: Structures, Dynamics and Mechanisms: Essays in
Honor of Aage B. Sorensen. Research in Social Stratification and Mobility (Vol.
21, pp. 91–112). Oxford, England: Elsevier.
• Sørensen, A. B., & Hallinan, M. T. (1977). “A reconceptualization of school
effects.” Sociology of Education, 50(4), 273–289.
• Sørensen, A. B., & Hallinan, M. T. (1986). “Effects of ability grouping on growth
in academic achievement.” American Educational ResearchJ ournal, 23(4), 519–
542.
• Sørensen, A. B., & Morgan, S. L. (2000). “School effects: Theoretical and
methodological issues.” In M. T. Hallinan (Ed.), The Handbook of the Sociology
of Education (pp. 137–160). New York: Kluwer/Plenum.
22. Aage B. Sørensen學習機會論
• Aage B. Sørensen之學習機
會模型可以用一differential
equation來表示:
• 此模型中 y(t) 是在t 時間點
的學習成就,而dy(t)/dt 即
是學習成就變化率(rate of
change in achievement, or
rate of learning)。
dt
tdv
s
dt
tdy )()(