Learning and Applying Tests of Significance – Statistics Assignment Help

Assignment Task

 

Assignment: Learning and Applying Tests of Significance
Part 1
In this portion of the assignment, you will review five mock studies. Follow the step-by-step instructions:
Mock Studies 1 & 2 require you to enter data from scratch. You need to create a data set for each of the three mock studies by yourself. (Refresh the data entry skill acquired in Week 1.)
Mock Studies 3 – 5 require you to use the GSS dataset. The variables are specified in each Mock Study.
Go through the five steps of hypothesis testing (below) for EVERY mock study.
All calculations should be coming from your SPSS. You will need to submit the SPSS output file (.spv) to get credit for this assignment.
The five steps of hypothesis testing when using SPSS are as follows:
State your research hypothesis (H1) and null hypothesis (H0).
Identify your significance level (alpha) at .05 or .01, based on the mock study.
Conduct your analysis using SPSS.
Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’ or ‘Asymptotic Sig.’ We will call this “p.”
Compare the two and apply the following rule:
If “p” is < or = alpha, then you reject the null.
Please explain what this decision means in regards to this mock study. (Example: Does counseling affect ADL? Do you recommend counseling services?)
Please make sure your answers are clearly distinguishable. Perhaps you could bold your font or use a different color.
This assignment is due no later than Sunday of Week 5 by 11:55 pm ET. Save this Word file in the following format: [your last name_SOCI332_A3]. Your spv (SPSS output) file should be labeled [your last name_SOCI332_A3Output].
T-Tests
Mock Study 1: t-Test for Independent Samples
Six months after an industrial accident, a researcher has been asked to compare the job satisfaction of employees who participated in counseling sessions with those who chose not to participate. The job satisfaction scores for both groups are reported in the table below.
Use the five steps of hypothesis testing to determine whether the job satisfaction scores of the group that participated in counseling session are statistically different from the scores of employees who chose not to participate in counseling sessions at .01 level of significance. (Clearly list each step).
As part of Step 5, indicate whether the researcher should recommend counseling as a method to improve job satisfaction following industrial accidents based on evaluation of the null hypothesis.
Data to be entered in SPSS (instructions below)

PARTICIPATED IN COUNSELING DID NOT PARTICIPATE IN COUNSELING
36 38
39 36
41 36
36 32
37 30
35 39
37 41
39 35
42 33

Step 1: Data managing
Open a blank SPSS data file: File New Data
In the blank SPSS data file, create your SPSS data set by entering the number of activities of daily living performed by those who participated/did not participate in the counseling sessions (reported on previous page). Please create two columns. Column one is the test variable, where you enter ALL the 18 scores in the table. Column 2 is the grouping variable, where you use “1” to indicate if a score is from someone who participated in the counseling sessions; and “0” to indicate if a score is from someone who chose not to participate in the counseling sessions. The data set will look like this in SPSS Data View window:
36 1
39 1
……….
38 0
36 0
……….
After data entry, go to Variable View window, change the name of the first variable (test variable) to “ADL” and the second variable (grouping variable) as “group.” Set decimals for both variables to zero.

Step 2: SPSS execution
Click: Analyze Compare MeansIndependent-Samples T Test use arrow to move ADL to “Test Variable” use arrow to move “group” to “Grouping Variable” when two (? ?) appear, click Define Groups. On the next pop up window, enter “1” for “Group 1” and “0” to “Group 2.”
Click OK.
Mock Study 2: t- Test for Dependent Samples/Paired Samples (15 points)
Researchers are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living before and after group therapy. The researchers randomly selected 10 depressed clients in a 6-week group therapy program.
Use the five steps of hypothesis testing to determine whether the observed differences in the numbers of activities of daily living obtained before and after therapy are statistically significant at .05 level of significance. (Clearly list each step).
As part of Step 5, indicate whether the researchers should recommend group therapy for all depressed people based on evaluation of the null hypothesis.
Data to be entered in SPSS (instructions below)
CLIENT BEFORE THERAPY AFTER THERAPY
A 11 17
B 7 12
C 10 12
D 13 21
E 11 12
F 12 15
G 9 16
H 8 17
I 13 17
J 12 8

Step 1: Managing data
Open a blank SPSS data file: FileNewData
In the blank SPSS data file, create your SPSS data set by entering the number of activities of daily living performed by the depressed clients (see above) in the Data View window. Enter the “before therapy” scores in the first column and the “after therapy” scores in the second column.
In the Variable View window, change the variable name for the first variable to “ADLPRE” and the second variable to “ADLPOST.” Set the decimals for both variables to zero.

Step 2: SPSS execution
Click: Analyze Compare Means Paired-Samples t-Test use the arrow to move ADLPRE under “variable 1” inside Paired Variable(s) window and then use the arrow to move ADLPOST under “variable 2” inside Paired Variable(s) window.
Click OK.
ANOVA
Mock Study 3: One-Way ANOVA
An advertising firm has been hired to assess whether different demographics have different rates of TV watching to help determine their advertising strategy. Using the GSS data, determine whether hours of tv watched differs by race.
Use the five steps of hypothesis testing to determine whether the observed differences in the number of hours watching TV across three groups are statistically significant at .05 level of significance. (Clearly list each step).
As part of Step 5, indicate whether the advertising firm should target each racial group differently (if their habits differ) based on evaluation of the null hypothesis.
Variables from GSS dataset to be used (instructions below):
RACE – race of respondent1 = WHITE
2 = BLACK
3 = OTHER
TVHOURS – hours per day watching TV
Step 1: Data managing
Open a blank SPSS data file: File Open Data GSS2018.sav (from wherever you have it saved)
Step 2: SPSS execution
Click: Analyze Compare Means One-Way ANOVA use arrow to move TVHOURS to “Dependent Variable list” use arrow to move RACE to “Factor,” which instructs SPSS to conduct the analysis of variance on the number of activities performed by therapy type.
Click: Options Descriptive (to obtain descriptive statistics).
Click: Continue
Click: OK.
Additional question based on Mock Study 3
Describe the circumstances under which you should use ANOVA instead of t-Tests. Explain why t-Tests are inappropriate in these circumstances.
Chi-Square
Mock Study 4: Chi-Square Test for Independence
Researchers are interested in whether US adults have different levels of confidence in Congress (legislative branch of the federal government) and how strongly each person identifies with a specific political party. These data are presented below.
Following the five steps of hypothesis testing, conduct chi-square test for independence at the .05 level of significance. (Clearly list each step).
As part of Step 5, indicate whether the observed frequency is significantly different from the expected frequency, and what that means in regard to this mock study. In other words, does political party affiliation effect one’s confidence in Congress?
Variables from the GSS dataset to be used (instructions below):
CONLEGIS – confidence in congress (legislative branch of government)1 = A GREAT DEAL
2 = ONLY SOME
3 = HARDLY ANY
PARTYID – political party affiliation
0 = STRONG DEMOCRAT
1 = NOT STR DEMOCRAT
2 = IND NEAR DEMOCRAT
3 = INDEPENDENT
4 = IND NEAR REPUBLICAN
5 = NOT STR REPUBLICAN
6 = STRONG REPUBLICAN
7 = OTHER PARTY
Step 1: Data managing
Open a blank SPSS data file: File Open Data GSS2018.sav (from wherever you have it saved)
Step 2: SPSS execution
Click: Analyze Descriptive Statistics Crosstabs use arrow to move “PARTYID” to “Column(s)” use arrow to move “CONLEGIS” to “Row(s).” (Recall in crosstab, DV is always in the row and IV is always in the column.)
Click: Statistics check “Chi-Square.”
Click: Continue.
Click: Cells check “Expected.”
Click: Continue.
Click: OK.
Regression
Mock Study 5: Linear Regression
Researchers in the field of gerontology are researching the effects of age on mental health. They are using GSS data to gather some preliminary findings.
Following the five steps of hypothesis testing, conduct a linear regression analysis to determine whether age affects number of poor mental health days at the .05 level of significance. (Clearly list each step).
As part of Step 5, indicate whether there is a significant relationship between age and mental health at the .05 level and what does this mean in regard to this mock study. Should the researchers continue their study?
Variables from the GSS dataset to be used (instructions below):
AGE – age of respondent
MNTLHLTH – Days of poor mental health past 30 days
Step 1: Data managing
Open a blank SPSS data file: File Open Data GSS2018.sav (from wherever you have it saved)
Step 2: SPSS execution
Click: Analyze Regression Linear use arrow to move MNTLHLTH to “Dependent list” use arrow to move AGE to “Independent,” which instructs SPSS to conduct the linear regression on the relationship of age to poor mental health.
Click: OK.
Part 2
Now it is time for you to choose and perform the correct test of significance for your project variables (from the GSS, chosen in Week 1). As we discussed previously, the levels of measurement of our variables determine which test of significance works for the research project. Here are the guidelines: 
IV is categorical (nominal or ordinal) with two categories/values and DV is interval/ratio [DV values are independent]: Independent Samples T-test
IV is categorical (nominal or ordinal) with two categories/values and DV is interval/ratio [DV values are dependent]: Dependent Samples T-test
IV is categorical (nominal or ordinal) with more than two categories/values and DV is interval/ratio: ANOVA
IV and DV are BOTH categorical variables (nominal or ordinal): Chi-square
Special note for Chi-square: you should have less than 20% of the cells with an expected count of 5 or less. This information is reported automatically, right below the chi-square output table. If your chi-square test fails to meet this requirement, it is necessary to use “recoding” to combine some of the answer categories on the IV or DV to increase the expected counts.  
IV and DV are BOTH interval/ratio variables: Regression
Follow the five steps of hypothesis testing for your project variables:
State your research hypothesis (H1) and null hypothesis (H0).
Identify your significance level (alpha) at .05 or .01. (It is typical to use .05.)
Conduct your analysis using SPSS.
Look for the valid score for comparison. This score is usually under ‘Sig 2-tail’ or ‘Sig. 2’ or ‘Asymptotic Sig.’ We will call this “p.”
Compare the two and apply the following rule:
If “p” is < or = alpha, then you reject the null.
Please explain what this decision means in regards to your project variables.
(i.e., Does your IV influence your DV?)
Test of Significance on Project Variables