OBJECTIVE

    The objective of this course to introduce some important and procedures of statistics and relate them to data analysis through a statistical package SPSS/PC.The course includes concepts related to testing of hypotheses, non-parametric statistics, and correlations of various type, including partial and multiple correlation. Students will learn to calculate and interpret simple regression and carry out One-way and Two-way Analysis of Variance (ANOVA).
    Students are expected to learn to select the statistics most suitable for the type of data they have; carry out statistical analyses using SPSS/PC, and interpret the results in terms of their meaning, significance and power.

 
CONTENTS

1.  Revision of Statistical Concepts.
 
      Basic concepts: Level of measurement, Population and Samples,
      Data presentation: Frequency distribution, Charts and Graphs, Cumulative Frequency Distributions.
      [Harris: chapter 1,4 and 5 and Hinkle, Wiersma and Jurs: chapter 1,3, and 5].

     Computer Exercise
     1.  Introduction to SPSS 7.0 for Windows.
              Starting SPSS
              Preparing data in SPSS
              Use of Data Editor.
              Data Files
 
     2.  Data Handling
               Data list, Value Labels, Variable Labels, Begin Data, End Data, List.
               Recode, Compute, Select if , Sort.
 
2.  Testing of Hypothesis and Statistical power
     Basic concepts in hypothesising: Null Hypothesis, Degress of Freedom, Level of Significant, Critical region, one and
     two-tailed tests, errors of type I and type II, statistical power. [Hinkle, Wiersma and Jurs, 293-311]
     Steps in testing statistical hypothesis.
     Statistical power and effect size [Hinkle, Wiersma and Jurs, pp. 293-311]
     Confidence Intervals [Aron & Aron, pp. 201-209].
     Some parametric tests: Normal test. [Howell, 89-101]
 
     Computer Exercise

              Data Screening
              Accuracy of data file
              Missing data
              Outliers-Detecting Outliers
              Normality Skewness and Kurtosis

3.   Hypothesis Tests : Mean (t-test for two sample)
 
      Simpling distribution of the mean : Basic concepts. [ Howell, pp 171-175]
      Significance of the difference between two mean for independent samples (equal variance and unequal variance).
      Significant of the difference between two means for correlated samples.[Hinkle, Wiersma and Jurs, pp. 239-261, b and c ]

      Computer Exercise
            Data analysis: t-test.
 
4.  Analysis of Variance I ( two Weeks)
 
      Rationale and basic logic of the analysis of variance: [Main Text : Vaughan, pp 352-377)
      Assumptions underlying ANOVA.
      Conceptual foundation-transforming the t-test into ANOVA, the F ratio
      Hypothesis testing with the analysis of variance
      Analysis of Variance procedures: One-way of ANOVA
      Post-hoc: Scheffe comparisons, Tukey test and Newman-Keuls test,
      Power test: Eta. [Main Text: Vaughan, pp 377-378]
      [Main Text: Hinkle, Wiersma and Jurs, pp346-358, b-f)

5.  Analysis of Variance II (Two weeks)
 
     Concepts of Two-way ANOVA, Factorial design and assumptions of the Two-way ANOVA. [Main Text : Harris, pp
     407-423]
    Analysis of variance: Two-way of ANOVA
    The interpretation of interaction effects
    Post-hoc tests.
    [Main Text: Hinkle, Wiersma, and Jurs, pp 399-437, b-d].

    Computer exercise

          Computer analysis: One-way and Two-way of ANOVA

6.  Correlation
 
      Meaning of correlation, Scatterplot. Covariance [Howell, pp 230-239]
      Types of correlation: Bravis-Pearson’s Product-moment correlation and Spearman’s Rank-order correlation, Biserial,
      Point-biserial, Tetrachoric, Phi.
      Factors influencing the coefficient of correlation.
      [Main Text : Hinkle, Wiersma, and Jurs, pp 104-117,524-544, b and c]

     Computer exercise

           Correlation, matrix correlation, and all types of correlation mentioned above.

 7.  Regression (Two weeks)
 
      Simple liner regression of Y on X and the regression of X on Y.
      Prediction: Procedures.
      Power test: R (R: square)
      [Main Text: Hinkle, Wiersma and Jurs, pp 127-144 and Howell, pp 241-254]

     Computer exercise

            Screening data for regression procedures and regression procedures.

8.  Non-Parametric statistics: Chi Square
     Assumption of the Chi- Square tests
     Chi Square test for Goodness of Fit
     Chi- Square test for independence
     Measures of Association in contingency tables.
     A test for correlated proportions in a two by two tables.
     [main Text : Harris, pp. 447-470, Hinkle, Wiersma and Jurs, pp 550-566]

    Computer exercise
 
          Data analysis: NPAR & CROSTABS.

9. Revisions

 
Main Textbook
 

Aron, A. & Aron, E.N. (1997). Statistics for psychology. Upper Saddle River, NJ: Prentice Hall.
Harris, M. B. (1998). Basic statistics for behavioural science research. Boston : Allyn and Bacon.
Howell.D.C. (1997). Statistics methods for psychology. Belmont, CA: Duxbury Press.
Hinkle D.E , Wiersma, W. & Jurs, S.G. (1994). Applied statistics for the behavioral sciences. Boston: Houghton Mifflin
    Company.
Vaughan, E.D. (1998). Statistics: Tools for understanding data in the behavioural sciences. Upper Saddle River, NJ: Prentice
     Hall.

Computer References

Kinnear P.R. & Gray C.D. (1997). SPSS for windows made simple. Hove: Psychological Press.
Norusis M.J. (1996) SPSS. Chicago: SPSS inc.