Identify whether or not the authors found support for their hypotheses. Consider sample size and Type I and Type II error.

Description

Hypothesis Testing: An Introduction to Various Applications

A variety of statistical tools can be used to investigate a hypothesis. These tools allow us to compare an average score against a standard (e.g., the z-test). Other tools allow us to compare the means of two groups. One is the independent samples t-test that is used to explore mutually exclusive groups (e.g., treatment and control, men and women, etc.).

Another set of statistics used for hypothesis testing includes analysis of variance (also known as ANOVA) and its various versions. We use ANOVA to explore the evolution of a variable over several time periods (i.e., longitudinal analysis) or more than two groups and how they differ on a variable of interest.

For example, we may hypothesize that people under 30 years of age are less committed to their jobs than those from 31 to 50 years of age, while those over 50 years are the most committed. ANOVA can be used to explore these types of hypotheses.

Last, we will discuss how we can use a tool called linear regression to make predictions about how one variable may influence another. In Week 6, we will focus solely on the correlation, which is an element of the regression analysis.

Be sure to review this week’s resources carefully. You are expected to apply the information from these resources when you prepare your assignments.

Reference:

Weiers, R. M. (2011). Introduction to business statistics (7th ed.). Boston, MA: Cengage Learning.

Assignment:

Locate three quantitative studies addressing a topic in your area of specialization. At minimum, two different statistical tests should be represented.

For example, you might search the literature for studies in transformational leadership and you may find two that used regression analysis and a third that used a t-test. For each study:

State the null and alternative hypotheses
Identify the statistical test used to determine statistical significance (e.g., t-test, analysis of variance, etc.).

Identify the test statistic, note it, and explain what it means (e.g., t=3.47).

Identify the significance level used in each study

Identify whether or not the authors found support for their hypotheses. Consider sample size and Type I and Type II error.

Explain the implications of each finding.

Identify whether or not the authors found support for their hypotheses. Consider sample size and Type I and Type II error.

Explain the implications of each finding.

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