26/SU Statistical Methods (PSY-716A-26SU1)
Course Description
This is a pre-requisite for PSY-717. Course covers basic concepts and measures in descriptive and inferential statistics, including the statistical tests, one and two sample t-tests, one-way ANOVA, bivariate correlation and regression analysis, familiarity with non-parametric alternatives to parametric tests and the chi–square test and related measures of association, power analysis, and effect size and confidence interval analysis.
Course Learning Objectives/Competencies
Students completing this course in Statistical Methods will be expected to:
1. Understand basic concepts and methods of univariate descriptive statistics, including levels of measurement, z-scores, measures of central tendency and dispersion, types of distributions, independent and dependent variables.
2. Understand basic concepts and methods of bivariate descriptive statistics, including scatterplots, linear relationships, regression models, residuals, and measures such as gamma, phi, slope, Y intercept, coefficient of determination, Pearson’s r, and eta.
3. Be familiar with univariate and bivariate graphing approaches, including bar charts, histograms, stem and leaf diagrams, pie charts, boxplots, scatterplots, regression lines, and bivariate data display with bar charts.
4. Understand basic concepts of statistical inference, including sampling distribution, sampling error, standard error, null and alternative hypothesis, one and two tailed tests, Type I and Type II error, rejection region, alpha level, level of significance, rejection of null hypothesis, central limit theorem, effect sizes and confidence intervals, general logic of inference, relationship of confidence interval to hypothesis test, meaning of Z, t, F and chi-square distributions.
5. Know the theory behind null hypothesis significance testing (NHST) and criticisms of and alternatives to this approach.
6. Be able to conduct the following statistical tests: chi-square test, one and two sample t-test (both independent and correlated group designs for two sample t-test), confidence intervals for proportions and means, power analysis, one way analysis of variance (one way ANOVA), bivariate regression and correlation analysis.
7. Know the meaning of statistical power and the relationship between power, effect size, sample size and Type I and II error.
This is a pre-requisite for PSY-717. Course covers basic concepts and measures in descriptive and inferential statistics, including the statistical tests, one and two sample t-tests, one-way ANOVA, bivariate correlation and regression analysis, familiarity with non-parametric alternatives to parametric tests and the chi–square test and related measures of association, power analysis, and effect size and confidence interval analysis.
Course Learning Objectives/Competencies
Students completing this course in Statistical Methods will be expected to:
1. Understand basic concepts and methods of univariate descriptive statistics, including levels of measurement, z-scores, measures of central tendency and dispersion, types of distributions, independent and dependent variables.
2. Understand basic concepts and methods of bivariate descriptive statistics, including scatterplots, linear relationships, regression models, residuals, and measures such as gamma, phi, slope, Y intercept, coefficient of determination, Pearson’s r, and eta.
3. Be familiar with univariate and bivariate graphing approaches, including bar charts, histograms, stem and leaf diagrams, pie charts, boxplots, scatterplots, regression lines, and bivariate data display with bar charts.
4. Understand basic concepts of statistical inference, including sampling distribution, sampling error, standard error, null and alternative hypothesis, one and two tailed tests, Type I and Type II error, rejection region, alpha level, level of significance, rejection of null hypothesis, central limit theorem, effect sizes and confidence intervals, general logic of inference, relationship of confidence interval to hypothesis test, meaning of Z, t, F and chi-square distributions.
5. Know the theory behind null hypothesis significance testing (NHST) and criticisms of and alternatives to this approach.
6. Be able to conduct the following statistical tests: chi-square test, one and two sample t-test (both independent and correlated group designs for two sample t-test), confidence intervals for proportions and means, power analysis, one way analysis of variance (one way ANOVA), bivariate regression and correlation analysis.
7. Know the meaning of statistical power and the relationship between power, effect size, sample size and Type I and II error.
- Faculty: Brittney Roberson