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Study Guide for Test 1 You should be able to: Explain the difference between descriptive and inferential statistics. Explain the difference between a sample and a population. Explain why researchers often use samples. Distinguish between a statistic and a parameter. Explain what a data set and a data file are. Determine the unit of analysis for a data set. Distinguish between a variable and a constant. Know how to construct social scientifically sensible variable values. Explain what a dichotomous variable is. Determine the level of measurement of any variable (that is, whether the variable is nominal, ordinal or interval/ratio). Determine whether a variable is continuous or discrete. Explain what aggregate and ecological data are and offer examples of each. Transform raw data into frequency and percentage distributions. Know what outliers and missing data are and be able to recognize them. Create and explain cumulative percentage distributions. Create and interpret presentation-quality frequency, percentage and cumulative percentage tables. Create presentation-quality pie charts and bar graphs. Explain how the level of measurement of a variable influences our use of cumulative distributions and choice of graph. Calculate and interpret modes, medians and means. Explain when each type of average is appropriate. Define and calculate standard deviations and variances for both samples and populations. Recognize when standard deviations and variances are appropriate or inappropriate. Measure skewness in variables. Calculate and interpret standard scores.
Study Guide for Test 2 You should be able to: Calculate percentages from frequencies in a bivariate table. Compare percentages in a bivariate table to determine strength of relationship. Distinguish between a positive and negative relationship. State null hypothesis and research hypothesis for test of significance for bivariate table. Calculate chi-square score for bivariate table. Use chi-square score to determine statistical significance and decide whether or not to reject the null hypothesis. Given measures of association for a bivariate table:
pick an appropriate measure, tell strength, direction (if ordinal), and significance level (p<…) Say what a PRE measure is. State null hypothesis for test of significance for difference between two means (t-test). State research hypothesis for t-test. Calculate t-statistic for t-test. Use t-statistic to determine statistical significance and decide whether or not to reject the null hypothesis (for two- tailed test) Write conclusions for results from comparing percentages, chi-square test, or t-test. Say when to use chi-square test and when to use t-test. Say when we run the risk of Type I error and when the risk of Type II error. Identify the dependent variable and the independent variable. Explain the difference between substantive and statistical significance. Explain what the sampling distribution of the mean is. Calculate confidence intervals around means and differences between means.
Study Guide for Test 3 You should be able to: State null hypothesis for ANOVA. Calculate F-ratio for ANOVA for appropriate data. Use F-ratio to determine statistical significance and decide whether or not to reject the null hypothesis. Write substantive conclusions of ANOVA test. Say what a (y-intercept) and b (slope) in bivariate regression equation mean. Draw a scatterplot from data. Interpret correlation coefficient. Calculate and interpret the significance of a correlation coefficient. Interpret partial tables in multivariate tabular analysis: identify independent, dependent, and control variables; identify type of control variable; identify what elaboration process occurred; draw conclusions. Given a situation to analyze, say what test of statistical significance you would use (chi-square test, t-test, ANOVA, test for significance of correlation and regression coefficients). Say when we run the risk of Type I error and when the risk of Type II error. |