Assignment: Using Designs with Three or More Levels of an Independent Variable

Assignment: Using Designs with Three or More Levels of an Independent Variable

Assignment: Using Designs with Three or More Levels of an Independent Variable

Discussion: Discuss, elaborate and give example on the topic below. Please use the Reference/Module I provided below. Professor wont consider outside sources. Please be careful with spelling and Grammar.

Author: Jackson, S.L. (2017) Statistics plain and simple. (4th ed.). Boston, MA: Cengage Learning

Topic:

When there is a between-subjects design, use a one-way between-subjects ANOVA, which uses only one independent variable. 275 words.

Reference/Module

Module 13: Comparing More Than Two Groups

Using Designs with Three or More Levels of an Independent Variable

Comparing More than Two Kinds of Treatment in One Study

Comparing Two or More Kinds of Treatment with a Control Group

Comparing a Placebo Group to the Control and Experimental Groups

Analyzing the Multiple-Group Design

One-Way Between-Subjects ANOVA: What It Is and What It Does

Review of Key Terms

Module Exercises

Critical Thinking Check Answers

Module 14: One-Way Between-Subjects Analysis of Variance (ANOVA)

Calculations for the One-Way Between-Subjects ANOVA

Interpreting the One-Way Between-Subjects ANOVA

Graphing the Means and Effect Size

Assumptions of the One-Way Between-Subjects ANOVA

Tukey’s Post Hoc Test

Review of Key Terms

Module Exercises

Critical Thinking Check Answers

Chapter 7 Summary and Review

Chapter 7 Statistical Software Resources

In this chapter, we discuss the common types of statistical analyses used with designs involving more than two groups. The inferential statistics discussed in this chapter differ from those presented in the previous two chapters. In Chapter 5, single samples were being compared to populations (z test and t test), and in Chapter 6, two independent or correlated samples were being compared. In this chapter, the statistics are designed to test differences between more than two equivalent groups of subjects.

Several factors influence which statistic should be used to analyze the data collected. For example, the type of data collected and the number of groups being compared must be considered. Moreover, the statistic used to analyze the data will vary depending on whether the study involves a between-subjects design (designs in which different subjects are used in each group) or a correlated-groups design. (Remember, correlated-groups designs are of two types: within-subjects designs, in which the same subjects are used repeatedly in each group, and matched-subjects designs, in which different subjects are matched between conditions on variables that the researcher believes are relevant to the study.)

We will look at the typical inferential statistics used to analyze interval-ratio data for between-subjects designs. In Module 13 we discuss the advantages and rationale for studying more than two groups; in Module 14 we discuss the statistics appropriate for use with between-subjects designs involving more than two groups.

Assignment: Using Designs with Three or More Levels of an Independent Variable

MODULE 13

Comparing More Than Two Groups

Learning Objectives

•Explain what additional information can be gained by using designs with more than two levels of an independent variable.

•Explain and be able to use the Bonferroni adjustment.

•Identify what a one-way between-subjects ANOVA is and what it does.

•Describe what between-groups variance is.

•Describe what within-groups variance is.

•Understand conceptually how an F-ratio is derived.

The experiments described so far have involved manipulating one independent variable with only two levels—either a control group and an experimental group or two experimental groups. In this module, we discuss experimental designs involving one independent variable with more than two levels. Examining more levels of an independent variable allows us to address more complicated and interesting questions. Often, experiments begin as two-group designs and then develop into more complex designs as the questions asked become more elaborate and sophisticated.

Using Designs with three or More Levels of an Independent Variable

Researchers may decide to use a design with more than two levels of an independent variable for several reasons. First, it allows them to compare multiple treatments. Second, it allows them to compare multiple treatments to no treatment (the control group). Third, more complex designs allow researchers to compare a placebo group to control and experimental groups (Mitchell & Jolley, 2001).

Comparing More than Two Kinds of Treatment in One Study

To illustrate this advantage of more complex experimental designs, imagine that we want to compare the effects of various types of rehearsal on memory. We have participants study a list of 10 words using either rote rehearsal (repetition) or some form of elaborative rehearsal. In addition, we specify the type of elaborative rehearsal to be used in the different experimental groups. Group 1 (the control group) uses rote rehearsal, Group 2 uses an imagery mnemonic technique, and Group 3 uses a story mnemonic device. You may be wondering why we do not simply conduct three studies or comparisons. Why don’t we compare Group 1 to Group 2, Group 2 to Group 3, and Group 1 to Group 3 in three different experiments? There are several reasons why this is not recommended.

You may remember from Module 11 that a t test is used to compare performance between two groups. If we do three experiments, we need to use three t tests to determine any differences. The problem is that using multiple tests inflates the Type I error rate. Remember, a Type I error means that we reject the null hypothesis when we should have failed to reject it; that is, we claim that the independent variable has an effect when it does not. For most statistical tests, we use the .05 alpha level, meaning that we are willing to accept a 5% risk of making a Type I error. Although the chance of making a Type I error on one t test is .05, the overall chance of making a Type I error increases as more tests are conducted.