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Doctor of Philosophy in Rehabilitation Sciences (PhD-RS) Advanced Research Methods in Rehabilitation (ARM-812): Course Content

Advanced research methods in rehabilitation refer to sophisticated and specialized techniques used to study and improve the effectiveness of rehabilitation programs.

Course Objectives

Course Learning Objectives (CLOs):

  1. To gain proficiency in advanced statistical methods such as multivariate analysis, logistic regression,survival analysis, and mixed-effects models relevant to rehabilitation research.
  2. To learn to apply biostatistical techniques to analyze and interpret complex data sets from rehabilitation studies, ensuring accurate and meaningful results.
  3. To develop predictive models for rehabilitation outcomes and validate these models using appropriate statistical methods, enhancing the ability to forecast patient progress and treatment efficacy.
  4. To critically assess and interpret the statistical methods and results presented in rehabilitation research literature, ensuring a deep understanding of their implications and limitations.
  5. To acquire advanced skills in using statistical software (such as r, sas, or spss) for conducting sophisticated biostatistical analyses, preparing students for real-world data analysis tasks in rehabilitation research.

Course Outline

Course Contents: Foundations of Biostatistics

  1. Measure of Central Tendency
    • Mean, median, and mode
    • Application and interpretation in health research
  2. Measure of Dispersion
    • Range, variance, standard deviation, and interquartile range
    • Importance in understanding data variability
  3. Quantitative Data Presentation
    • Graphical methods: histograms, box plots, scatter plots
    • Tabular methods: frequency tables, cross-tabulations
    • Effective presentation of data in research
  4. Hypothesis Testing
    • Null and alternative hypotheses
    • Type I and Type II errors
    • P-values and significance levels
  5. Sample Size Calculation
    • Importance of sample size in research
    • Methods for calculating sample size
    • Power analysis and its application
  6. Parametric Tests
    • Assumptions and applications
    • Common parametric tests: t-tests, ANOVA
    • Interpretation of results
  7. Non-Parametric Tests
    • When to use non-parametric tests
    • Common non-parametric tests: Chi-square, Mann-Whitney U, Kruskal-Wallis
    • Interpretation of results

MODULE 2: Advanced Analysis of Variance (ANOVA) Techniques

Course Contents:

  1.  One-Way ANOVA
  2. Two-Way ANOVA
  3. Mixed-Design ANOVA
  4. Multivariate Analysis of Variance (MANOVA)
  5. Analysis of Covariance (ANCOVA)
  6. Mixed-Design ANOVA (Repeated for emphasis)
  7. Multivariate Analysis of Covariance (MANCOVA)

MODULE 3: Advanced Regression Techniques and Reliability Testing.

Course Contents:

  •    Simple Linear Regression
  • Multiple Linear Regression
  • Logistic Regression
  •   Ordinal Regression
  •   Reliability Testing (Cronbach's Alpha)

 

Module 4: Factor Analysis

Course Contents:

  • Introduction to Factor Analysis
  • Exploratory Factor Analysis (EFA)
  • Confirmatory Factor Analysis (CFA)
  • Assumptions and Diagnostics in Factor Analysis
  • Advanced Topics in Factor Analysis
  • Practical Applications and Case Studies