465 Adv Stats


Lecture Outline (Tentative for Semester)

Lecture Outline 465

I.  Basic Probability

    A) Laws of Probability

    1) Additive Law

    2) Multiplicative Law

    B) Conditional Probability

    C) Continuous vs. Discrete Data (also brief on nominal data)

    D) Working with Discrete Data

          1) Permutations

          2) Combinations

    E) Binomial & Binomial Distribution

           -Exact Probability applying the Binomial

    F) Hypothesis Testing applying the Binomial

    G) Sign Test applied using the Binomial   

II.  Review of Chi-Square

    A) Goodness of Fit (one-dimensional table)

    B) Test of Independence (2 by X dimensional table)

    C) Partitioning Method

    D) Reading Summary Data into SPSS, SAS, R

    E) Assumptions Chi-Square

    F) Fisher's Exact Test

    G) Odds Ratio

    H) Stratification for Known Groups

III.  Review of Correlation

    A) Purpose

    B) Assumptions

    C) Pearson Correlation

IV. Review of the t-test

    A) Mechanics (what the test assess)

    B) Independent Samples t-test

    C) Assumptions

    D) Applied Examples

V.  Brief Overview of Effect Size Meaning based on 'r'

VI. Review of One-way ANOVA

    A) Mechanics

    B) One-way ANOVA

    C) Assumptions

    D) Family-wise Error Concersn

    E) Follow-up Tests

         1) Multiple Comparison Procedures





         2) Planned Comparisons

             -a priori t-tests

             -orthogonal contrasts

     F) Eta^2 (% variance accounted for), Eta (Effect)

     G) Structural Formula

VII. Factorial ANOVA

     A) Structural Formula for two-way ANOVA

     B) Assumptions

     C) Main effects and follow-up tests (similar to One-way)

     D) Interaction Effects & Moderators

        -Simple Effects testing in SAS

        -Scheffe test as follow-up to sig simple effects with 3+ means

     E) Eta^2 for all effects, effect sizes

     E) Condition Effect

     F) Sum of Squares Type III and Type I

        -Type III is unweighted means analysis (standard)

        -Type I is weighted means analysis, used for Analysis of Covariance

     G) Three-way ANOVA

Above are materials covered on the Midterm


Below are materials covered on the Final exam

VIII. Repeated Measures

     A) Partitioning variance to isolate subject effects

     B) Structural formula

     C) One-way within design

          -post-hoc tests, Bonferroni adj alpha level

     D) Assumptions

     E) Between-within designs

          -compared to Gain or Differences scores

          -ANCOVA option

          -chat about simple main effects testing

          -chat about moderators

     D) 2x2 all within design

IX. Multiple Regression

     A) OLS ideology and model

     B) Direct or forced entry approach

         -Interp of OLS regression components (model, slope, intercept)

         -semi-partial regression coeffs vs. BETAs

         -f^2 statistic for power, cutoffs

     C) Assumptions

     D) Chat on hierarchical approach

     E) Statistical Approaches (Backward, Forward, Stepwise)

     F) Suppression

     G) Mediation, direct, indirect effects

X. Regression and Categorical Data

     A) Dummy coding

     B) Layering in effects

     C) ANOVA demonstration in Regression

XI. Trend Analysis

     A) Linear, Quadratic, Cubic, Quartic trends

     B) When to use

     C) Rules for trend tests (simplest first)

     D) Orthogonal Polynomials in for trends ANOVA

     E) Powered vectors in regression

XII. Nonparametric Tests

     A) Pros/cons

     B) Kappa Test

     C) Runs Test

     D) Sign Test

XIII. Odds and Ends

     A) Cronbach's alpa - what it does, viability, interpretation nuances

     B) Binomial Effect Size Display (BESD) - application

     C) Your career options in with Quant Methods


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