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MAKE SURE TO SAVE YOUR SPSS/PSPP OUTPUT AS A PDF AND TURN IN WITH YOUR RESPONSES and be sure to properly label your responses]

For those that have trouble reading a cross tab SEE attachment at the end of this assignment (outputN.pdf)  You have to make sure you click on the assignment to see the attachment.

1) You will report the information listed below for both the GSS 2012 & GSS 2018 data sets: Examine the relationship between attitudes toward the level of national assistance for childcare (NATCHILD) and the sex of the respondent (SEX). Fill in the following information:

Make a prediction

What percent of Americans believe we spend too little for childcare? ________________

Do you think men and women vary on their perspectives on this issue?
YES         NO

Now perform the analysis and report the requested information  (Use a cross tab and remember the chi square is to determine if there is a significant difference in the percentages between group/categories that could not be credited to chance:                      

         2012/2018

Percentage of men (out of only men) stating the current level is too little ___________/_____________

Percentage of women  (out of only women) stating the current level is too little _________/_______________

Chi Square significance level __________/______________

Make sure you are reporting the level of significance (NOT the chi-square score) as stated in the PSPP output.

Is the relationship statistically significant YES NO (2012)/YES  NO (2018)

A) How would you interpret this result beyond just reference to the level of significance?  (what do the findings suggest?  Interpret).  Do not just restate the statistics without any interpretation, and include mention of the 2008 findings in your discussion. Be sure to expand on the findings reported and do not include anything about missing data)

2) You will report the information listed below for the GSS 2018 data sets:

You will report the information listed below for the GSS 2018 data sets: Examine the relationship between race (RACE) and whether a person supports the death penalty for murder (CAPPUN), whether one ever approves of police striking a citizen (POLHITOK), and the belief that Whites are hurt by affirmative action (DISCAFF). Fill in the following information:

Make a prediction

What percent of Americans support the death penalty? __________

Do you think Whites and Blacks vary on their perspectives on this issue (CAPPUN)? YES NO

Do you think Whites and Blacks vary on their perspectives on this issue (POLHITOK)? YES NO

Now perform the analysis and find:

A) CAPPUN BY RACE

Percentage of whites (out of only whites) that disapprove:________________________

Percentage of African Americans (out of only African Americans) that disapprove  ________________________

Percent of Others (out of only Others) that disapprove ________________________

Chi Square significance level ________________________ 

Is the relationship statistically significant?

B) POLHITOK BY RACE

Percentage of whites (out of only whites) that say no:________________________

Percentage of African Americans (out of only African Americans) that say no ________________________

Percent of Others (out of only Others) that say no ________________________

Chi Square significance level ________________________

Is the relationship statistically significant? 

C)  DISCAFF BY RACE

Make a prediction

What percent of Americans believe whites are hurt by affirmative action? ___________

Do you think Whites and Blacks vary on their perspectives on this issue? YES NO

Now perform the analysis and find:

Percentage of Whites (out of only whites) saying “Somewhat likely” ______________________

Percentage of Blacks (out of only African Americans) saying “Somewhat likely”  ______________________

Percentage of others (out of only Others) saying “Somewhat likely”  ______________________

Chi Square significance level _______________________

Report the level of significance (NOT the chi-square score) as stated in the PSPP output.

Is the relationship statistically significant YES NO

A) How would you interpret this result beyond just reference to the level of significance?  Do not just restate the statistics without any interpretation.  What do you observe in the findings- (beyond what is asked for above)-patterns? Be specific.

3) You will report the information listed below for the GSS 2018 data sets: Examine the relationship between general happiness (HAPPY) and marital status (MAR1). Fill in the following information:

Make a prediction

What percent of Americans are “very happy”? ________________

Who is the happiest? People who are (underline one):
Married Divorced Never married

Now perform the analysis and find:

Percentage of married individuals (out of only married people) who are “very happy” ________________________

Percentage of divorced individuals (out of only divorced people) who are “very happy” ________________________

Percentage of never married individuals (out of only never married people) who are “very happy” ________________________

Chi Square significance level ________________________

Report the level of significance (NOT the chi-square score) as stated in the SPSS/PSPP output.

Is the relationship statistically significant YES NO

A) How would you interpret this result without just reference to the level of significance?  Do not just restate the statistics without any interpretation. What do you observe in the findings- (beyond what is asked for above)-patterns? be specific?

4).  In the absence of performing an analysis, make a prediction: What percent of Americans view their health as either “excellent”, “good”, “fair” and “poor”?  Then use the GSS2018 data, run a frequency on HEALTH, and also create a pie chart.  Describe your findings below and explain the extent to which your predictions conformed to the findings.  What do these findings suggest about health in America?  Exclude missing cases.                  

Predicted Percent                     

Excellent                      ___________                 

Good                           ___________                    

Fair                              ___________                    

Poor                            ___________


How close were your predictions?  If they differed, what do you think was the reason?
           

Valid Percent                     

Excellent                      ___________                 

Good                           ___________                    

Fair                              ___________                    

Poor                            ___________


Provide a PIE CHART and a BAR GRAPH:


What do these findings suggest about the state of health in America?  Policy implications given perceptions versus the reality of health in America? 

5)  Using the GSS 2018 data, examine the relationship between sex (SEX) and the belief that a woman will not get a job or promotion over a man (DISCAFFW).

Fill in the following information: 
Make a prediction


What percent of Americans believe women are less likely to get a job or promoted over a man:

                                                                                                                        ___________


Do you think males and females vary on their perspectives on this issue?                          YES    NO


Now perform the analysis and find:

Percentage of men (out of only men) saying “Very Likely”                             _____________

Percentage of women (out of only women) saying “Very Likely”                        ___________

Chi Square significance level                                                                                       __________________

Is the relationship statistically significant              YES     NO

A) How would you interpret this result without just reference to the level of significance?  Do not just restate the statistics without any interpretation.  What do you observe in the findings- (beyond what is asked for above)-patterns? Compare with the 2012 findings from the discussion. Be specific.

IMPORTANT: After you have submitted your assignment the answers will be made available below.  If you have any responses that are incorrect, resubmit your corrected responses along with explanations of what you did incorrectly no later than two days after the due dat

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CHAPTER 3: CHI SQUARE DISTRIBUTIONS

The next statistical procedure is the chi square distribution, which is used to determine the level
of statistical significance for relationships between variables at the nominal and ordinal level of
measurement. Specifically, The Pearson Chi-Square tests whether a particular pattern of
group frequencies is likely due to chance alone. Since the Pearson Chi-Square evaluates two
variables, a significant Chi-Square value tells two things: 1) the pattern of frequencies is
significantly different from a random pattern AND 2) that the values are significantly associated
with each other. We will also look at measures of association appropriate for nominal and
ordinal data: lambda and gamma.

ANALYZE

DESCRIPTIVE STATISTICS>CROSSTABS (click)

Graphic 3.1

A dialogue box will open with the list of variables and two areas to add the variables (row and
column).

Graphic 3.2

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We are going to look at Click on the first variable so that it is highlighted, then type ABANY.
This should bring you down to “Abortion if women wants for any reason.”—highlight it and
move it to row by clicking on the arrow.

Graphic 3.3

Then go back to the top, repeat but type SEX, it will go to Respondent’s Sex, highlight it and
move it to column

Graphic 3.4

Click on Statistics, and a new dialogue box will open. In the lower-right hand corner next to the
help button is something that looks like a dog-eared page. You can place your cursor it, right
click and drag on it and the dialogue box will expand revealing more choices. We are doing a
Chi-Square so make sure that the “Chisq” box is checked and we are going to include a measure
of association as well.

But wait! How do I know which measure of association to choose? The following discussion
was adapted from an exercise prepared by Ed Nelson at the Social Science Research and
Instructional Lab: There are many measures of association to choose from. We’re going to limit
our discussion to those measures that PSPP will compute. When choosing a measure of
association we’ll start by considering the level of measurement of the two variables (see chapter
2 for review of level of measurement).

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• If one or both of the variables is nominal, then choose one of these measures.
o Contingency Coefficient [automatically calculated by PSPP]
o Phi and Cramer’s V
o Lambda

• If both of the variables are ordinal, then choose from this list.
o Gamma
o Somer’s d
o Kendall’s tau-b
o Kendall’s tau-c

• Dichotomies should be treated as ordinal. Most variables can be recoded into dichotomies
(also know as dummy variables, where it is either coded a “1” or a “0”). For example,
marital status can be recoded into married (1) or not married (0). Race can be recoded as
white (1) or non-white (0). All dichotomies should be considered ordinal.

For this exercise, since both variables are ordinal: both the SEX variable and categories available
for ABANY (Yes, NO) are dichotomies and as such is treated as ordinal.

Therefore, you can choose any of the four options listed. We are just choosing Gamma so make
sure to click on Gamma as well. Since the GSS 2008 treats these as nominal variables, choose
Lambda, and then click Continue then click OK.

Graphic 3.5

The following output (Graphic 3.6) will appear…

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Graphic 3.6

Looking at the various parts will help better understand what is portrayed in the output. First is a
listing of the syntax as shown in Graphic 3.7:

Graphic 3.7

Next is a summary of the valid and missing cases or observations. You normally would not
report the missing cases, so you go with the Valid Cases rather than the Total when reporting the
N (Number of Observations). For this question, the N equals 1298.

Graphic 3.8

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Graphic 3.9 displays a wealth of information but it means little of you do not understand it. To
know what it value stands for, first look at the top of table for a guide. The following appears at
the top: ABORTION IF WOMAN WANTS FOR ANY REASON*RESPONDENTS SEX
[count, row %, column %, total %. This last part is important because it indicates what each
value within a given square stands for.

Graphic 3.9

As an example, look at the row YES.

• Starting with the column for MALE, the first value, which is the count is 262. This is the
number of respondents that said YES and are MALE.

• The next value in the MALE column is the row percent. The value 47.64% is percent of
those that responded YES that are male, out of ALL those, in other words, EVERYONE
that responded YES. This value could be used to make comparisons across responses,
for example, what was the make-up of those that responded NO or YES.

• The third value in the MALE column is the column percent. The value, 44.56% is the
percent MALE that responded YES, out of ONLY MALE respondents. This is the value
one would use if they wanted to make comparisons across groups, in this case MALE
and FEMALE. IMPORTANT: Remember which variables you enter for each row and
column so that you know which is the categorical response (in this case: NO, YES) and
the GROUP (in this case: MALE, FEMALE).

• The last value: 20.18% is the percent MALE that responded YES out of the all
(TOTAL) respondents.

Regardless of whether or not there is a significant difference as indicated by the Pearson Chi-
Square (Graphic 10), the information in Graphic 3.9 can be used to provide valuable information.
Moving on to Graphic 10, the focus will be only on the top statistic: Chi Square. The following
measures of association (Lambda and Gamma) were also included.

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Graphic 3.10

The results of various tests are provided in the first of the three tables. If you did not click on
Gamma and Lambda, this would be the only table you would see. The Pearson Chi-Square
tests whether a particular pattern of group frequencies is likely due to chance alone. Recall from
the beginning of the chapter that a significant Pearson Chi-Square value tells us two things: 1)
the pattern of frequencies is significantly different from a random pattern AND 2) that the values
are significantly associated with each other. This association can also be tested with gamma,
lambda, and others as listed above.

Please not that most academic journals consider a significance level of .05 or lower to be
significant (95% of confidence intervals). Although, some journals also researchers to indicate
when test statistics indicate a significance level of .1 or lower. This can vary by journal and
discipline.

Back to our example: The Pearson Chi-Square value or score is 2.10. When statisticians
calculated these scores by hand it was necessary to look up critical values to see if this value was
significant. Statistical software, like PSPP, SPSS, SAS, and STATA and many others, calculates
the significance level for you so this is unnecessary. The significance level is listed under
Asymp. (Asymptotic) Sig. (2 tailed) and is .147. This indicates that it is not significant.
Similarly, neither Gamma or Lambda are significant (based on the Chi-Square) suggesting that
the pattern of frequencies are not significantly different that what could be produced by chance
nor, as indicated by all three test statistics, that these two variables are not significantly
associated with each other.

Central tendency and spread 2

Data visualization to determine measures of central tendency and spread

7/10

Generate measures of central tendency and spread

A. PCI_15 (Per Capita Income: 2015) Which measure of central tendency is the preferred measure and why?

Statistics

Per Capita Income, 2015

N

Valid

51

Missing

0

Mean

46929.3529

Std. Error of Mean

1096.95234

Median

45002.0000

Mode

35444.00a

Std. Deviation

7833.80661

Variance

61368526.073

Range

36052.00

Minimum

35444.00

Maximum

71496.00

Sum

2393397.00

Percentiles

25

40998.0000

50

45002.0000

75

51146.0000

a. Multiple modes exist. The smallest value is shown

B. CRS63 (Prisoners under Sentence of Death: 2008) Which measure of central tendency is the preferred measure and why?

Statistics

Prisoners Under Sentence of Death: 2008

N

Valid

37

Missing

14

Mean

85.297

Std. Error of Mean

22.4623

Median

35.000

Mode

2.0

Std. Deviation

136.6327

Variance

18668.492

Range

669.0

Minimum

.0

Maximum

669.0

Sum

3156.0

Percentiles

25

8.000

50

35.000

75

96.000

C. HrtDRT17 (Heart Disease Mortality Rate: 2017) Which measure of central tendency is the preferred measure and why?

Statistics

Heart Disease Mortality Rate, 2017

N

Valid

50

Missing

1

Mean

165.5100

Std. Error of Mean

4.02777

Median

157.7500

Mode

119.10a

Std. Deviation

28.48063

Variance

811.146

Range

118.10

Minimum

119.10

Maximum

237.20

Sum

8275.50

Percentiles

25

145.2250

50

157.7500

75

183.9500

a. Multiple modes exist. The smallest value is shown

Using the GSS2018 dataset, what is the proper graph (scatterplot, histogram, bar chart)-make sure to provide it, and report what you think is the correct measure of central tendency. Why?

A. REALRINC (R’s Income in constant $)

Statistics

R’s income in constant $

N

Valid

1363

Missing

985

Mean

24994.19

Std. Error of Mean

782.298

Median

17025.00

Mode

20430

Std. Deviation

28881.511

Variance

834141678.466

Skewness

2.968

Std. Error of Skewness

.066

Kurtosis

10.128

Std. Error of Kurtosis

.132

Range

150824

Minimum

227

Maximum

151051

Sum

34067080

The correct measure of tendency is mean because the data is evenly distributed. Visualization can be done using a bar graph. Because the data is so spread out, use the median.

B. PARTNERS5 (How many sex partner’s R has in last 5 year)

Statistics

How many sex partner’s R had in last 5 years

N

Valid

1388

Missing

960

Mean

1.73

Std. Error of Mean

.050

Median

1.00

Mode

1

Std. Deviation

1.856

Variance

3.443

Skewness

2.053

Std. Error of Skewness

.066

Kurtosis

4.385

Std. Error of Kurtosis

.131

Range

9

Minimum

0

Maximum

9

Sum

2406

The correct measure of tendency is mode because the data is not evenly distributed. Visualization can be done using a histogram. No, given this is ratio level data, use either mean or median. The skew and spread suggest using the median.

C. RELITEN (Strength of Affiliation)

Statistics

Strength of affiliation

N

Valid

2314

Missing

34

Mean

2.17

Std. Error of Mean

.024

Median

2.00

Mode

2

Std. Deviation

1.140

Variance

1.300

Range

3

Minimum

1

Maximum

4

Sum

5011

The correct measure of central tendency in this case mean. It can be used to show data distribution inclined to strength of affiliation. Data visualization can be used using a bar graph because it shows clear data representation. Use the median and a bar graph because this is ordinal level data. Given there is an even number of categories, the mode might make more sense as it shows which level of affiliation is most frequently selected.

3. Using the GSS2018 dataset, what is the proper graph (scatterplot, histogram, bar chart-make sure to provide it), for:

A. HAPPY (General Happiness) by SEX (Respondent’s Sex)

The proper graph to use in this case is an histogram because it gives a more precise data visualization. No, ordinal level data so use the bar graph.

B. ANCESTRS (Believe in Supernatural Power of Deceased Ancestors) by SEX (Respondent’s Sex)

The proper graph to use in this case is an histogram because it gives a more precise data visualization. No, use bar graph because this is ordinal level data.