Question

Due: 07/25/2018
Bonus Assignment #1

Directions: For this bonus assignment, you will give a presentation on a statistical model of your choosing. To do this, choose a statistical model from the list below and come up with a hypothetical study related to your research interests that could be conducted using the model. Presentations should be 5-6 minutes and must include the following:
Background information on your topic, research question/hypothesis, and why you think your study should be conducted.
State the IV (including levels) and the DV. Be specific about what your variables are. Review “operational definitions” in your textbook. Also, mention the scale of measurement your variables are measured on.
Explain what the procedure would be for individuals participating in your study. BE SPECIFIC ABOUT EACH STEP IN THE PROCEDURE.
Come up with a sample data set for your hypothetical study (minimum of 30 data points total). Demonstrate how the data would be analyzed using the model in JMP*. You can obtain a free copy of JMP using your student id information here: (http://helpdesk.louisiana.edu/sasjmp).
Explain how to interpret the results of the test. Relate your analysis to the question posed by your hypothetical study.
Choose from the following statistical models:
One-way between-subjects ANOVA
One-way within-subjects ANOVA
Two-way between-subjects ANOVA
Regression
*Two-way ANOVA does not require a JMP demonstration.

Answer 1

model:- Two-way between-subjects ANOVA

A Two-Way ANOVA is useful when we desire to compare the effect of multiple levels of two factors and we have multiple observations at each level.

One-Way ANOVA compares three or more levels of one factor. But some experiments involve two factors each with multiple levels in which case it is appropriate to use Two-Way ANOVA.

STUDY:-

Occupational stress varies according to age and gender.

Variable:-

DEPENDENT VARIBLE- occupational stress

INDEPENDENT VARIABLE:- age and gender

Factors - age and gender.

Employees have been classified into three groups or levels:

• age less than 40,
• 40 to 55
• above 55

In addition employees have been labeled into gender classification (levels):

• male
• female

In this design, factor age has three levels and gender two. In all, there are 3 x 2 = 6 groups or cells. With this layout, we obtain scores on occupational stress from employee(s) belonging to the six cells.

PROCEDURE FOR DATA COLLECTION- The survey was organized into six sections: employment status, entire working career, current job, current job exposures, longest-held job.

Highly trained interviewers conduct approximately 30 follow-up interviews each month. The occupational survey was included as a module within these routine follow-up interviews. Full-scale administration was conducted March 2015 through March 2017. Study participants were notified about the new survey in advance through the newsletter. During the follow-up call, verbal consent to complete the occupational survey was obtained prior to administration. When a participant was unavailable during repeated call attempts, the survey was administered during the subsequent follow-up period.

ways to solve Two-Way ANOVA.

1. BASIC VERSION:- one observation in each cell - one occupational stress score from one employee in each of the six cells.

2. SECOND VERSION:- more than one observation per cell but the number of observations in each cell must be equal.

The advantage of the second point is it also helps us to test if there is any interaction between the two factors.This helps us to know if age and gender are independent of each other - they are independent if the effect of age on stress remains the same irrespective of whether we take gender into consideration.

Hypothesis Testing

In the basic version there are two null hypotheses to be tested.

• H₀₁: All the age groups have equal stress on the average
• H₀₂: Both the gender groups have equal stress on the average.

In the second version, a third hypothesis is also tested:

• H₀₃: The two factors are independent or that interaction effect is not present.

The computational aspect involves computing F-statistic for each hypothesis.

Assumption

normality, independence and equality of variance.

Advantages:

1. more efficient than its one-way counterpart- age and gender - this helps to reduce error variation thereby making this design more efficient\
2. Unlike One-Way ANOVA, it enables us to test the effect of two factors at the same time.
3. One can also test for independence of the factors provided there are more than one observation in each cell. The only restriction is that the number of observations in each cell has to be equal (there is no such restriction in case of one-way ANOVA).

In our example if we divided the employees only according to their age, then we would have ignored the effect of gender on stress which would then accumulate with the experimental error.

But we divided them not only according to age but also according to gender which would help in reducing the error - this is application of the principle of local control for reducing error variation and making the design more efficient.

CONCLUSION:-

The feasibility of conducting high-quality occupational data collection and I/O coding for a large heterogeneous population-based sample was demonstrated. We found that training of staff was important to reduce the effect of professional life on mental health.