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Essentials of Statistical Analysis

This course teaches the learner the essentials of statistical analysis by emulating the structure and presentation of a full-semester university statistical analysis course in an interactive on-line experience.

LEARNERS


About this Course


CITI Program's Essentials of Statistical Analysis course (renamed from Fundamentals of Biostatistics to more accurately reflect the course content) is an efficient, low-cost way to learn about or brush up on the basics of statistical analysis. The course consolidates a traditional 13-week, two class-a-week university course into 26 modules that can be completed conveniently online at the learner’s pace. Each interactive module consists of didactic materials, hands-on exercises, quizzes, and tests.

This course will be useful for undergraduate and graduate students interested in pursuing a career in social or biomedical sciences and research. In addition, the course provides students with the requisite foundational knowledge as they begin, or work through, a statistical analysis course at their own university.  The course will also benefit clinical research coordinators, basic research faculty, and other members of the research team who desire or need a foundational review of statistical analysis principles and methodologies.

CITI Program's Essentials of Statistical Analysis course has been authored by Seth J. Schwartz, Ph.D., Professor of Public Health Sciences at the University of Miami. Dr. Schwartz has taught both introductory and advanced biostatistics courses for many years at the University. The course has been peer reviewed by experts in the field and graduate student tested for usability.

Language Availability: English

Suggested Audiences: Undergraduate and Graduate Students, Research Faculty and Team Members, Clinical Research Coordinators

Organizational Subscription Price: Ask about course bundle pricing
Independent Learner Price: $200 per person (introductory price extended through 31 May 2018)


Course Content


Introduction New Content

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

Recommended Use: Required
ID (Language): 17609 (English)

Population and Sample New Content

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)

Central Tendency and Variability New Content

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)

Sensitivity and Specificity New Content

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)

Distribution and Probability New Content

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)

Probability and Odds New Content

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)

Normal Distribution and Z-Scores New Content

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 which can be used to compare the positions of a case’s scores across different variables.

Recommended Use: Required
ID (Language): 17615 (English)

Skewness and Kurtosis New Content

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)

Standard Error and Type I/II Errors New Content

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)

The Four Horsemen New Content

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)

Confidence Intervals and Degrees of Freedom New Content

This module introduces confidence intervals, or ranges that are likely to contain the true value (average, correlation, et cetera) 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)

Comparing Two Independent Means New Content

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)

Wilcoxon Rank-Sum Test New Content

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)

Paired Samples T-Test New Content

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)

Nonparametric Methods for Paired Sample Data New Content

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)

Analysis of Variance New Content

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

Recommended Use: Required
ID (Language): 17625 (English)

Following Up a Significant ANOVA New Content

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

Recommended Use: Required
ID (Language): 17626 (English)

Kruskal-Wallis Nonparametric ANOVA New Content

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

Recommended Use: Required
ID (Language): 17627 (English)

Proportions New Content

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)

Comparing Two Independent Proportions New Content

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

Recommended Use: Required
ID (Language): 17629 (English)

Contingency Tables and Chi-Square Tests New Content

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)

Other Contingency Table Analyses New Content

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)

Correlations New Content

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)

Comparing Correlation Coefficients New Content

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)

Simple Linear Regression New Content

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)

Multiple Regression New Content

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

Recommended Use: Required
ID (Language): 17635 (English)


FAQs


Who is the suggested audience?

The suggested audiences are undergraduate and graduate students who have an interest in pursuing a career in social or biomedical sciences research. In addition, clinical research coordinators, basic research faculty, and other members of the research team will benefit from this course. The course would be very useful for students who will soon begin a classroom course in statistics. It is likely that this on-line course will improve the student’s performance in such face to face classroom courses.

Who is eligible to purchase the new Essentials of Statistical Analysis course?

The new Essentials of Statistical Analysis course may be purchased and self-paid by any independent learner.  If an Organizational Administrator is interested in purchasing a course bundle for multiple learners, please contact Sales at sales@citiprogram.org.

Why should an independent learner consider taking this course?

This statistical analysis course is designed to simulate a 13 week, 2 classes per week, college level introductory statistics course. The course includes didactic materials, simulations, problems and help screens. This course is designed to:

  1. Provide the student with a head start before the student takes the college level class room version of the course.
  2. Provide a comprehensive review of the fundamentals of research statistics theory and practice.
  3. Provide a user-friendly resource for people needing a refresher experience in specific areas of statistical practice.

What qualifications do I need to take this course?

All you need is a working knowledge of algebra – specifically, working with variables. The course involves basic mathematical operations such as addition, subtraction, multiplication, division, raising a number to a power, and taking the square root of a number. Operations beyond these will be explained as part of the course.

How does the course work?

The course consists of 26 self-contained modules. Each module consists of a “teaching” portion (with examples), a knowledge check, an interactive exercise, and an end-of-module quiz. There are also mid-course and end-of-course exams that are similar to the quizzes. Users go through each module at their own pace, and no instructor or moderator is needed. However, users are welcome to send us feedback about the course so that we can continue to improve it.

How long will the course take me to complete?

Although completion time will vary from one user to the next – some people will need more time to work out the exercise and quiz problems than other people will – we estimate that each module will take about 45 minutes to complete. The modules are designed so that users can complete them in one sitting or in multiple sittings, and there is no time limit for any of the course activities.

Will this course give me the same information as a classroom-based course?

Yes. This course was adapted from the introductory statistical analysis course that Dr. Schwartz has taught for many years. This course actually includes more material than can feasibly be included in a 13-week, in-person course – including alternatives to use if the assumptions of parametric (normal distribution based) tests are not met.

Will the course work on mobile devices as well as on laptop and desktop computers?

This course is designed to work on as many devices as possible. For the best results, we recommend using a large screen device such as a desktop, laptop, or tablet. While mobile use is possible, it may not be desired if screen space is limited.