1. Quantitative Data Analysis or Introduction to Statistics Need to know: What you want to know -- Relationship? Prediction? Causation? Description? Everything?! Appropriate statistical procedures – Depends on what you want to know.

2. Common Statistical Procedures Relationship between 2 variables Relationship among more than 2 variables Prediction Description of pheno- mena or variables Statistical control Correlation Factorial analysis Regression Chi-square Descriptive statistics (frequency, mean, etc.) Standardization

3. Correlation  Mathematical ex- pression of the nature of a relation- ship between 2 variables  Expressed in terms of a coefficient  Relationship may be strong or weak, direct or inverse

4. ANOVA, MANOVA  MANOVA allows for the study of two or more rela- ted dependent variables  Effect of each IV without consideration of others is the main effect  Effects of two or more IVs combined is the interaction effect  Used to determine whe- ther obtained differences between means of two or more groups are due to chance  The larger the value of F, the more likely the result is statistically significant  Used when more than one independent variable is investigated

5. Regression  Regression analysis techniques allow the researcher to predict the behavior or some variables based on the knowledge of the behavior of others  Regression also refers to the tendency of scores to move toward the mean of the population in repeated tests

6. Chi-Square  May be used to deter- mine the probability that an observed relationship is statistically significant  On a single independent variable, tests “goodness of fit,” or “How well do sample values corre- spond to hypothesized population values?”  On two independent variables, tests “indepen- dence,” or “Are the values of one variable related to, or dependent on, the values of the other independent variable?”  Assumes random selec- tion of samples, indepen- dent observations, & sufficient sample sizes

7. Descriptive Statistics  Often used to organize or display data  Can be used to summarize important characteristics of a set of numerical data  Describes the perform- ance & variability of sample scores graphically

8. Standardization  Conversion of raw scores on different measures to a single scale for the purpose of comparison  Also describes a single distribution of z scores considered “normal,” where the mean is 0 & the standard deviation is 1  Two common formats are “ z scores” and “ T scores”  IQ scores are an exam- ple of standard scores normally distributed, with a mean of 100 and a standard deviation of 15 (Wechsler)