you to use the GSS 2016 dataset. The variables are specified in each Mock Study.

c. Go through the five steps of hypothesis testing (as covered in the lesson for Week 4) for EVERY mock study.

d. 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:

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

2. Identify your significance level (alpha) at .05 or .01, based on the mock study. In Mock Study One, you are required to use BOTH .05 and .01 to test your hypotheses. For the remaining mock studies, you only need to use ONE level of significance (either .05 or .01) as specified in the instructions.

3. Conduct your analysis using SPSS.

4. 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.”

5. Compare the two and apply the following rule:

a. If “p” is < or = alpha, then you reject the null.

b. Please explain what this decision means in regards to this mock study. (Ex: Will 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_A2]. Your spv (SPSS output) file should be labeled [your last name_SOCI332_A2Output].

t-Tests

Mock Study 1: t-Test for a Single Sample (20 points)

1. Researchers are interested in whether depressed people undergoing group therapy will perform a different number of activities of daily living after group therapy than the average for depressed people. The researchers randomly selected 12 depressed clients to undergo a 6-week group therapy program.

Use the five steps of hypothesis testing to determine whether the average number of activities of daily living (shown below in the table) obtained after therapy is significantly different from a mean number of activities of 17 that is typical for depressed people. (Clearly list each step).

Test the difference at both the .05 and .01 levels of significance.

As part of Step 5, indicate whether the behavioral scientists should recommend group therapy for all depressed people based on evaluation of the null hypothesis at both levels of significance (.05 and .01).

Data to be entered in SPSS (instructions below)

CLIENT AFTER THERAPY

A 18

B 14

C 11

D 25

E 24

F 17

G 14

H 10

I 23

J 11

K 22

L 19

Step 1: Data managing

1. Open a blank SPSS data file: File? New? Data

2. 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 (numbers listed under AFTER THERAPY – see above) in the Data View window.

3. In the Variable View window, change the variable name to “ADL.” Set the decimals to zero.

Step 2: SPSS execution

a. Click: Analyze ? Compare Means ? One-Sample T test ? use the arrow to move “ADL” to the Variable(s) window on the right.

b. Enter the population mean (17) in “Test Value”

c. Click OK.

Mock Study 2: t- Test for Dependent Means (15 points) APU SOCI332 Week 5 Assignment 2 Paper

1. 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 8 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 9 16

F 8 17

G 13 17

H 12 8

Step 1: Managing data

1. Open a blank SPSS data file: File?New?Data

2. 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.

3. 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

a. 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.

b. Click OK.

Mock Study 3: t-Test for Independent Samples (15 points, APU SOCI332 Week 5 Assignment 2 Paper)

1. 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

1. Open a blank SPSS data file: File? New? Data

2. 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

49 1

……….

39 0

36 0

……….

3. 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

a. Click: Analyze? Compare Means?Independent-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.”

b. Click OK.

ANOVA (15 points)

Mock study 4 (APU SOCI332 Week 5 Assignment 2 Paper)

1. 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 2016 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 2016 dataset to be used (instructions below):

RACE – race of respondent

1 = WHITE

2 = BLACK

3 = OTHER

TVHOURS – hours per day watching TV

Step 1: Data managing

1. Open a blank SPSS data file: File? Open Data? GSS2016.sav (from wherever you have it saved)

Step 2: SPSS execution

a. 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.

b. Click: Options ? Descriptive (to obtain descriptive statistics).

c. Click: Continue

d. Click: OK.

Additional question based on Mock Study 4

2. Describe the circumstances under which you should use ANOVA instead of t-Tests. Explain why t-Tests are inappropriate in these circumstances.

Chi-Square (20 points)

Mock study 5-1: Chi-Square Test for Goodness of Fit

1. Researchers are interested in whether US adults have different levels of confidence in the President (executive branch) of the federal government.

Following the five steps of hypothesis testing, conduct “goodness of fit” chi-square test to determine whether the observed frequencies are significantly different from the expected frequencies at the .01 level of significance. (Clearly list each step).

As part of Step 5, indicate whether the observed frequency is significantly different from the expected frequency when equal number of adults in each confidence category is assumed (100%/3=33%). What does this mean in regard to this mock study?

Variable from GSS 2016 dataset to be used (instructions below):

CONFED – confidence in executive branch of federal government

1 = A GREAT DEAL

2 = ONLY SOME

3 = HARDLY ANY

Step 1: Data managing

1. Open a blank SPSS data file: File? Open Data? GSS2016.sav (from wherever you have it saved)

Step 2: SPSS execution

a. Click: Analyze ? Non-Parametric Tests ? Legacy Dialogs ? Chi-Square ? use the arrow to move CONFED to “Test Variable list.”

• This procedure instructs SPSS that the chi-square for goodness of fit should be performed on the confidence in federal government variable. Note that “All categories equal” is the default selection in the “Expected Values” box, which means that SPSS will conduct the goodness of fit test using equal expected frequencies for each of the four styles, in other words, SPSS will assume that the proportions of students each style are equal.

b. Click OK.

Mock study 5-2: Chi-Square Test for Independence

2. Next, researchers categorized the same group from the previous study based on the level of confidence in the federal government and whether that person is of a certain (self-identified) social class. 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 social class affects one’s confidence in the President.

Variables from GSS 2016 dataset to be used (instructions below):

CONFED – confidence in executive branch of federal government

1 = A GREAT DEAL

2 = ONLY SOME

3 = HARDLY ANY

CLASS – subjective class identification

1 = LOWER CLASS

2 = WORKING CLASS

3 = MIDDLE CLASS

4 = UPPER CLASS

5 = NO CLASS

Step 1: Data managing

1. Continue to work on the data set already opened in Mock Study 5-1: goodness of fit Chi-square test.

Step 2: SPSS execution

a. Click: Analyze ? Descriptive Statistics ? Crosstabs ? use arrow to move “CLASS” to “Column(s)”? use arrow to move “CONFED” to “Row(s).” (Recall in crosstab, DV is always in the row and IV is always in the column.)

b. Click: Statistics ? check “Chi-Square.”

c. Click: Continue.

d. Click: Cells? check “Expected.”

e. Click: Continue.

f. Click: OK.

Regression (15 points)

Mock study 6-1: Linear Regression – APU SOCI332 Week 5 Assignment 2 Paper

1. 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 GSS 2016 dataset to be used (instructions below):

AGE – age of respondent

MNTLHLTH – Days of poor mental health past 30 days

Step 1: Data managing

2. Open a blank SPSS data file: File? Open Data? GSS2016.sav (from wherever you have it saved)

Step 2: SPSS execution

e. 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.

f. Click: OK.

This assignment is due no later than Sunday of Week 5 by 11:55 pm ET.

Save this file (as a Word file) in the following format: [your last name_SOCI332_A2]. You must also submit an SPSS output file (.spv) labeled [your last name_SOCI332_A2Output]. APU SOCI332 Week 5 Assignment 2 Paper

 

 

MORE INFO 

t- Test for Dependent and Independent Means

Introduction

You may have heard of a statistical test called the t- Test, but what is it? Well, it’s something that most people don’t even know they need to know. But don’t worry! You’re not alone! The t- Test is one of those things that most people either don’t know they need or think they already know. So let’s take a look at this simple statistical tool so that you can use it when making decisions about your data and how it affects your conclusions.

t- Test for Dependent Means

The t-test for one independent variable and two dependent variables is used to determine whether or not there is a significant difference in the means of the two groups being compared.

The null hypothesis states that there is no difference between the mean ranks of each group (H0). In other words, it says that if you were to randomly select an individual from one group and asked them how well they did on some task, then their rank would be exactly equal to those of individuals in another group. This assumption can be tested using a significance level such as 0.05 or p < 0.05 which means that if your result doesn’t match up with what we expect based on our previous knowledge about how things work statistically then this could mean something unusual happened during testing – maybe someone cheated!

Example

• t-Test for Dependent Means

• t-Test for Independent Means

t- Test for Independent Means

The t-test for independent means is used to test whether or not the means of two groups are equal. The null hypothesis states that the means are equal, and the alternative hypothesis states that they’re not.

In order to understand this type of test, you need some background information about data analysis in general. Data can be collected in different ways:

• Interval estimation involves estimating values between specific intervals (for example, from 0 to 1). This allows us to estimate how much confidence we have in our estimate based on how close it falls within an interval boundary; if one value falls outside an interval boundary line but still within another line’s larger border (i.e., 0-1), then there is some uncertainty about what exactly happened during those moments when your subject was taking those measurements during their day at work/school etcetera…

What does this mean?

In the t-test, the null hypothesis is that there is no difference between two groups. The alternative hypothesis states that there is a difference between the means of two groups.

The statistic used to test this null hypothesis is called an F-ratio and it takes one of three forms: p-value (where p = 0 or 1), Z value (where Z = number square root of twice SD), or critical value based on standard formula with degrees of freedom n – 1.

Takeaway:

The t-test is used to test the hypothesis that two independent or dependent means are equal. If this hypothesis is accepted, then you can reject the null hypothesis and conclude that your population means differ (or do not differ). If this hypothesis is rejected, then you can accept the null hypothesis and conclude that your population means are equal (or do not differ).

Conclusion

The t- Test is a statistical test that can be used to determine whether or not two samples have the same average. We have seen how to perform this test with one independent variable and two dependent variables. This example also provided an example of how to use the output from this statistical procedure in our research papers.