Essentials of Statistical Analysis (EOSA): Complete (Parts 1, 2, and 3)

This module introduces the basics of statistical thinking. It establishes a foundation for the course.

Recommended Use: Required
ID (Language): 17609 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews why and how samples are drawn from populations and introduces the concepts of sampling, representativeness, and statistical inferences.

Recommended Use: Required
ID (Language): 17610 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces the tendency for scores to cluster around the “center” or “average,” and provides indices of the extent to which scores spread out from the center or average. Central tendency and variability are among the most fundamental concepts in statistics.

Recommended Use: Required
ID (Language): 17611 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces methods for determining the ability of diagnostic tests to correctly determine which cases do and do not meet specific criteria, as well as the ability of diagnostic tests to predict which cases will and will not meet criteria at some point in the future.

Recommended Use: Required
ID (Language): 17612 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces the concept of frequency distributions, or shapes that data points create when they are plotted. The module also introduces various kinds of probability that are used in statistical reasoning and analyses.

Recommended Use: Required
ID (Language): 17613 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module outlines the distinctions between probability and odds, and provides formulas for computing odds from probability and vice versa. The module also reviews statistics used for computing the associations between earlier events and later outcomes.

Recommended Use: Required
ID (Language): 17614 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews the properties of the normal distribution, which underlies the family of analyses known as parametric tests. The module also introduces standard scores, which index how far a score is from the center of the dataset and can be used to compare the positions of a case’s scores across different variables.

Recommended Use: Required
ID (Language): 17615 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews statistical values that index the extent to which a variable’s frequency distribution departs from what would be expected under the normal distribution. These statistical values can be used to determine whether parametric statistics are appropriate for use with a given variable or set of variables.

Recommended Use: Required
ID (Language): 17616 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module covers the standard error, an index of the amount of imprecision in a set of scores. The module also introduces two of the most common errors that can be committed when drawing conclusions from the results of statistical analyses.

Recommended Use: Required
ID (Language): 17617 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces four important factors that determine the results of statistical analyses. The module reviews the interrelationships among these factors and the process through which researchers can determine the number of cases needed when planning a study.

Recommended Use: Required
ID (Language): 17618 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces confidence intervals, or ranges that are likely to contain the true value (such as average and correlation) that one wishes to determine. The module also reviews degrees of freedom – the number of values in a dataset that are free to vary – and how the degrees of freedom for an analysis affect the results of that analysis.

Recommended Use: Required
ID (Language): 17619 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews independent-samples t-tests, the most common parametric analyses used to compare scores from two groups with no members in common.

Recommended Use: Required
ID (Language): 17620 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews the alternative to the independent-samples t-test when a parametric test cannot be used. Fundamentals of nonparametric tests are introduced.

Recommended Use: Required
ID (Language): 17622 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews parametric methods for comparing two sets of scores drawn from the same group of cases.

Recommended Use: Required
ID (Language): 17623 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module covers the alternative to the paired-samples t-test when a parametric test cannot be used.

Recommended Use: Required
ID (Language): 17624 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module covers parametric methods used to compare scores across three or more groups.

Recommended Use: Required
ID (Language): 17625 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces methods that can be used to determine, following a significant overall result, which groups are significantly different from the other groups on the variable being compared.

Recommended Use: Required
ID (Language): 17626 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews the alternative to the analyses of variance when parametric analyses cannot be used.

Recommended Use: Required
ID (Language): 17627 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces proportions, or percentages of cases that meet a specified set of criteria. The module also reviews fundamental concepts involved in analyzing and comparing proportions.

Recommended Use: Required
ID (Language): 17628 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews methods for comparing proportions across groups that do not share members in common.

Recommended Use: Required
ID (Language): 17629 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module introduces methods for analyzing the relationship between two categorical variables, and for identifying the specific categories that are most responsible for the relationship.

Recommended Use: Required
ID (Language): 17630 (English)

This module reviews methods for comparing categories within a single categorical variable, and for comparing two proportions from the same set of cases.

Recommended Use: Required
ID (Language): 17631 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews parametric and nonparametric methods for determining the strength of association between two sets of numerical scores.

Recommended Use: Required
ID (Language): 17632 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews methods for comparing associations across different groups of cases and within a single group of cases.

Recommended Use: Required
ID (Language): 17633 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module covers different types of predictive relationships and reviews methods for determining the relationship between a predictor and an outcome.

Recommended Use: Required
ID (Language): 17634 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami

This module reviews methods for examining the relationships of multiple predictors to a single outcome variable.

Recommended Use: Required
ID (Language): 17635 (English)
Author(s): Seth J. Schwartz, PhD - University of Miami