Statistics Discussion Response

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Textbooks

1. OpenStax, Introductory Statistics. OpenStax. 19 September 2013. http://cnx.org/content/col11562/latest/

2. Tyrrell, S. 2009. SPSS: Stats practically short and simple (1st edition). bookboon.com. Ebooks and textbooks from Bookboon.com https://oerstatistics.wordpress.com/2016/03/05/spss-books/

Original Question:

Week 6 Forum

During Week 5, we used measures of association (Lambda, Gamma, Pearson’s r) to determine if a relationship is weak, medium, or strong. This week, we’ll study tests of significance. Like measures of association, levels of measurement of your variables determine which test of significance works for your research project. Here is the guideline: 

1. Before-and-after design and the variable is at I/R level: Dependent Sample T-test

2. DV and IV are BOTH categorical variables (nominal/ordinal): Chi-square

3. DV and IV are both continuous variables: regression

4. DV is a continuous variable (I/R level):

1. IV is categorical (dichotomous nominal/ordinal): Independent Sample T-test

2. IV is categorical with more than 2 categories: ANOVA

Why do we need to run tests of significance?

1. They allow us to see if our relationship is “statistically significant.” To be more specific, these test tell us if a relationship observed in a sample, like your research project based on GSS 2012 data set, is generalizable to the population from which this sample was drawn.

2. Test result reported under “p” in the SPSS output tells us the chances that a relationship observed in the sample is not real, but rather due to factors like a sampling error. We compare this “chance” with level of significance, commonly set as .05 or .01. If this chance is smaller than level of significance, we can reject the null hypothesis, and keep the research hypothesis.

This week in the forum discussion:

1. you will decide which test of significance you will use for your project. Use the guideline above to make your choice;

2. you will be using a process for hypothesis testing which outlines five steps researchers can follow to complete this process:

1. Write your research hypothesis (H1) and your null hypothesis (H0).

2. Identify and record your level of significance: either .05 or .01.

3. Complete the test using SPSS.

4. Identify the number under Sig. (2-tail).  This will be represented by “p.” compare the numbers in steps 2 (level of significance) and 4 and apply the following rule:

1. If p < or = level of significance, than you reject the null hypothesis

5. Determine what to do with your null and explain this to your reader.  Be sure to go beyond the phrase “reject or fail to reject the null” and explain what that means to your research .

Reply to the following response with 200 words minimum. (please make response as if having a conversation, respond directly to some of the statements in below post.)

Research Question:

Does the amount of time spent on the internet affect how often people socialize with relatives?

Variables:

1. wwwhr2, or WWW hours per week (Independent). Variable was recoded from wwwhr. Original variable was interval, but recoded variable is ordinal.

1. socrel, or spend evening with relatives (DV). Variable is ordinal.

Select the test of significance:

Like measures of association, levels of measurement determine which test of significance works best. Since both variables are ordinal, I selected the Chi-Square test of significance after reviewing the guideline in our reading.

Research hypothesis (H1) and corresponding null hypothesis (H0):

H 1: Spending time with family decreases with increased Internet usage.

H 0: Spending time with family does not decrease with increased Internet usage.

Level of significance:

The level of significance after running the Chi Square test is .002. Since this value is less than .05 or .01, I can reject the null and say that there is a significant relationship between these variables.

In other words, my H 1 (ppending time with family decreases with increased Internet usage) is accepted and that there is a significant relationship between spending time with family and internet usage.

Spend evening with relatives * wwwhr revised Crosstabulation
	wwwhr revised	Total
	.00	1.00	2.00	3.00	4.00	5.00
Spend evening with relatives	ALMOST DAILY	Count	8	34	25	29	3	25	124
		Expected Count	10.8	26.2	27.7	24.8	5.8	28.6	124.0
	SEV TIMES A WEEK	Count	19	59	61	49	9	51	248
		Expected Count	21.7	52.4	55.3	49.7	11.7	57.2	248.0
	SEV TIMES A MNTH	Count	16	45	57	47	10	45	220
		Expected Count	19.2	46.5	49.1	44.1	10.4	50.8	220.0
	ONCE A MONTH	Count	8	30	29	31	5	34	137
		Expected Count	12.0	28.9	30.5	27.5	6.5	31.6	137.0
	SEV TIMES A YEAR	Count	19	21	40	36	17	54	187
		Expected Count	16.3	39.5	41.7	37.5	8.8	43.2	187.0
	ONCE A YEAR	Count	10	16	7	5	3	19	60
		Expected Count	5.2	12.7	13.4	12.0	2.8	13.9	60.0
	NEVER	Count	9	10	8	7	1	7	42
		Expected Count	3.7	8.9	9.4	8.4	2.0	9.7	42.0
Total	Count	89	215	227	204	48	235	1018
	Expected Count	89.0	215.0	227.0	204.0	48.0	235.0	1018.0

Chi-Square Tests
	Value	df	Asymptotic Significance (2-sided)
Pearson Chi-Square	57.411a	30	0.002
Likelihood Ratio	56.331	30	0.003
Linear-by-Linear Association	1.017	1	0.313
N of Valid Cases	1018
a. 3 cells (7.1%) have expected count less than 5. The minimum expected count is 1.98.

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