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課程名稱 |
行為科學多變數統計分析
Multivariate Statistical Methods in Behavioral Research |
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開課學期 |
109-1 |
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授課對象 |
生物資源暨農學院 生物產業傳播暨發展學研究所 |
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授課教師 |
周志秉 |
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課號 |
BICD7172 |
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課程識別碼 |
630EM9150 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
第5,6,7,8,9,10,11,12,13 週
星期一2,3,4(9:10~12:10)星期四2,3,4(9:10~12:10) |
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上課地點 |
生傳402室生傳520室 |
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備註 |
本課程以英語授課。密集課程。
總人數上限：20人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
This course is designed for graduate students to learn multivariate statistical techniques frequently utilized in social sciences, particularly in health behavioral research. Specific techniques covered in this course are multiple linear regression, logistic repression, path model, multilevel regression for both cross-sectional and longitudinal design, power calculation, exploratory factor analysis and some latent variable modeling. |
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課程目標 |
Upon completion of this course, students will be prepared to:
1. Apply the most appropriate multivariate statistical approach analyze data obtained from public health research.
2. Obtain hand-on experience conducting analyses using SAS program.
3. Develop efficient SAS programming skills.
4. Adopt the correct SAS procedures for the selected statistical approach.
5. Identify correctly the types and metrics of variable to be included in the statistical models.
6. Conduct independently statistical analyses.
7. Perform significance tests to evaluate research hypotheses.
8. Adequately present and interpret the statistical analytical results. |
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課程要求 |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
Allison PD (2000). Multiple Imputation for Missing Data: A Cautionary Tale. Sociological Methods and Research, 28: 301-309.
Allison PD (2001). Missing Data. Thousand Oaks, CA: Sage Publications.
Cody R. (2001). Longitudinal Data and SAS: A Programmer’s Guide. Cary, NC: SAS Institute Inc. Chapters 1-4, 6-8.
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4, 272-299.
Hatcher L (2013). A Step-by Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, 2nd Edition. Cary, NC: SAS Institute Inc.
Lanza ST, Collins LM, Lemmon DR, Schafer JL (2007). PROC LCA: A SAS Procedure for Latent Class Analysis. Structural Equation Modeling, 14, 671–694.
Lanza, S. T., & Rhoades, B. L. (2013). Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14(2), 157-168.
Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Schabenberger O (2006). SAS for Mixed Models (2nd ed.). Cary, NC: SAS Institute Inc.
Loehlin JC (2004). Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. Lawrence Erlbaum Assoc Inc. Chapter 5.
Singer J. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 24, 323-355. |
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評量方式
(僅供參考) |
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