Quantitative Analysis SPSS Report Harvard Case Solution & Analysis

Introduction of Report

This is an SPSS report and all the analysis is based on a quantitative analysis which would be only performed in SPSS. Different hypothesis tests would be conducted on the target dataset BSA (British Social Attitudes). The data is one of the datasets that measures the attitudinal movements of the respondents and it complements the information gathered through the large scale government surveys to determine the facts and the behavior patterns related to different political issues. We have generated three different hypotheses related to three different topics and then we have applied the required tests based on specific research questions, null and alternative hypothesis and analyzed those results in detail for each hypothesis.

Hypothesis Test # 1 (Pearson Correlation Test)

In our first hypothesis test, we determine the statistical association between the respondent’s obesity, measured by their BSA (WtFactor) with how often they travel by cars for a distance of less than 2 miles in each week (shrtjrn). The research question that we would be answering in this hypothesis test is as follows:

Research Question

Is there any statistical association between obesity of the respondents to their frequency of using cars for short distances?

The alternative and null hypothesis for this test is as follows:

Ho: There is no statistical association between obesity of the respondents to their frequency of using cars for short distances.

H1:There is statistical association between obesity of the respondents to their frequency of using cars for short distances.

Identification of Dependable and Independent Variable

The dependent variable in this case is the obesity of the respondents or the weight of the respondents and the independent variable is the frequency of using cars for short distances. These have been chosen to check for association as a higher frequency of using cars might have a positive and significant correlation with the weight or obesity of the respondents. Because as they travel more by cars, they walk less and gain more weight.

Identification of Outliers

We have checked the dependent variable for the outliers, however, since the dependent variable is measured at a nominal scale, therefore, the WtFactor variable might not have any outliers as the weight is measured in BSA. For instance, the results are shown below:

 

Extreme Values
Case NumberValue
Final BSA weightHighest123914.9095
220903.8098
327483.6821
424423.6373
57853.3902
Lowest12272.2655
21950.2732
32119.2832
42216.2872
51196.2920

 

 

As we can see above, histogram for the obesity variable is not normally distributed and it is curved leftwards. The extreme values table shows there are specific extreme values in the dataset dependent variable of weight of respondents, however, since this is a nominal variable in this case, therefore, we ignore these values and continue with our analysis.

Descriptive Statistics

We have now generated descriptive statistics for the weight and frequency of car use variable as shown below:

 

Statistics
Final BSA weightHow many journeys of less than 2 miles do you make by car in a typical week: Version B
NValid4328889
Missing03439
Mean1.00000024.50
Mode.914697
Std. Deviation.491398138.848
Variance.2411509.194

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