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Using the High School Longitudinal Study 2009 dataset, the research question is to test for any possible difference between students’ hours spent watching television and school’s geographic region. The group statistics showed that the students from the Northeast region spend 3169 hours watching television or movies on typical school day while students from Midwest spend 5536 hours watching television or movies on a typical school day. The mean for the two variables are 2.39 for Northeast and 2.32 for Midwest. The standard deviation are 1.489 for Northeast and 1.458 for Midwest while the standard error mean are 0.26 for Northeast and 0.20 for Midwest.

Group Statistics

 

T1 School geographic region

N

Mean

Std. Deviation

Std. Error Mean

Hours spent watching television or movies on typical schoolday

Northeast

3169

2.39

1.489

.026

Midwest

5536

2.32

1.458

.020

 

 

 

 

 

Independent Samples Test

 

 

Levene’s Test for Equality of Variances

t-test for Equality of Means

 

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

 

Lower

Upper

Hours spent watching television or movies on typical schoolday

Equal variances assumed

3.051

.081

2.141

8703

.032

.070

.033

.006

.134

Equal variances not assumed

 

 

2.128

6482.427

.033

.070

.033

.006

.135

                       

 

The null hypothesis for this question is that there is no statistical significance between the two variables given the Leven’s Test for Equality of Variance that revealed a significance of 0.81 which is higher than the conventional 0.50 threshold. Therefore this null is rejected. 

 

The research design that would align with this question is that in which the p value is the probability that the samples are from the same population with regard to the dependent variable (outcome). Usually, the hypothesis we are testing is that the samples (groups) differ on the outcome. The p value is directly related to the null hypothesis. The p value determines whether or not we reject the null hypothesis. We use it to estimate whether or not we think the null hypothesis is true. The p value provides an estimate of how often we would get the obtained result by chance, if in fact the null hypothesis were true.

In the comparison of the means, m computations suggest that we can’t know whether the null hypothesis is true, but the sample that provided a mean value of 0.70 provides much stronger evidence in favor of rejecting the null hypothesis.

The dependent variable used in this analysis was the number of hours spent in watching television which in measured numerically while the independent variable was he region of respondents which was either Northeast or Midwest.

The null hypothesis is essentially the “devil’s advocate” position. That is, it assumes that whatever you are trying to prove did not happen (hint: it usually states that something equals zero).  In this scenario analysed here it means there are no relationships between the geographic locations and the number of hours they spend watching television or movies during school days. The statistical analysis using the SPSS proved otherwise and so the null hypothesis was rejected. According to Frankfort-Nachmias and Leon-Guerrero (2015) a descriptive research design would be best for this type of study.

 

 

 

References

 

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2015). Social statistics for a diverse society (7th ed.). Thousand Oaks, CA: Sage Publications.

 

Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.

 

Laureate Education (Producer). (2016f). Meaningfulness vs. statistical significance [Video file]. Baltimore, MD: Author.

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