Applied Research Methods

Review the 2 attached case study’s and answer questions. 

The Case of Buying Computers

The purchasing manager of a company had been tasked with buying new computers for all of
the employees. Needing to purchase about 500 computers, the manager sent out requests for
proposals to three companies: Swell, Doorway and MBI. Each of these companies responded and the
manager quickly saw that he could get the best deal from Doorway. Given that, he completed a
purchase order and presented it to the Vice-President of Finance for her approval. Despite the good
deal he had managed to work out, he was shocked to hear that the Vice-President wanted to go with
MBI. “After all”, the VP said, “MBI is the industry leader and we’ve always had fewer problems with
their equipment than we’ve had with the computers we’ve bought from Swell and Doorway. Because
of that, this company is going to guy MBI.”.

The purchasing manager, really not believing what he had just heard, went to the director of
technical support to look at the maintenance requests for the past five years. He was able to put
together a chart showing the actual occurrence of maintenance requests for each of the brands of
computer. These figures are shown in the chart below. The manager knew that the VP had final
authority on the request but wanted to know, for his own peace of mind, if there were really any
differences between the amount of maintenance each brand required.

Total
Maintenance

Requests

Swell 25

Doorway 33

MBI 19

1. What is the hypothesis that the manager is investigating?

2. What is the independent variable? What are the levels of the independent variable?

3. What is the dependent variable?

4. Which statistical test would he use to test his hypothesis?

5. For each of the sets of output below, what can you tell about the dependent variable? What
decision would the manager make?

Case A:

Observed Number Expected Number Residual

Doorway 33 25.7 7.3

Swell 25 25.7 -.7

MBI 19 25.7 -6.7

Total 77

Brand

Chi-Square 3.844

Degrees of Freedom 2

p value .146

Case B:

Observed Number Expected Number Residual

Doorway 43 25.7 17.3

Swell 21 25.7 -4.7

MBI 13 25.7 -12.7

Total 77

Brand

Chi-Square 18.805

Degrees of Freedom 2

p value .000

Case C:

Observed Number Expected Number Residual

Doorway 15 25.7 -10.7

Swell 21 25.7 -4.7

MBI 41 25.7 15.3

Total 77
Brand

Chi-Square 14.442

Degrees of Freedom 2

p value .001

The Case of Student Admissions

Admissions representatives at a large university are concerned with reports they’re hearing
about the validity and reliability of the standardized test that they use for helping determine admission to
their freshman class. Given that, they want to investigate alternate means of determining who will be
admitted to their university.

After careful consideration and a lot of research, the admissions counselors determine that one
of the best predictors of undergraduate grade point average (GPA) is a student’s high school GPA.
Knowing that, they search through their databases and find information about the high school GPA and
the first year GPA for all students admitted in the past five years. After formatting the data and running
it through a statistical program, they set out to try to determine if the predictive ability of the high school
GPA is accurate.

1. What is the hypothesis that the admissions counselors are investigating?

2. Is there an independent variable and/or a dependent variable? If not, what types of variables
are there?

3. What types of data do each of the variables represent?

4. Which statistical test would they use to test their hypothesis?

5. How should the admissions counselors interpret each of these cases? Are there any special
precautions or assumptions they should consider?

6. If the admissions counselors were to graphically plot each of these relationships, what would be
the general direction of each line?

Case A:

High School Freshman
GPA GPA

Pearson High School GPA 1.000 .801
Correlation Freshman GPA .801 1.000

Number High School GPA 1241 1241
Freshman GPA 1241 1241

Case B:

High School Freshman
GPA GPA

Pearson High School GPA 1.000 .555
Correlation Freshman GPA .555 1.000

Number High School GPA 1241 1241
Freshman GPA 1241 1241

Case C:

High School Freshman
GPA GPA

Pearson High School GPA 1.000 -.980
Correlation Freshman GPA -.980 1.000

Number High School GPA 1241 1241
Freshman GPA 1241 1241