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Running head: WEEK # 11 – SEX AND CLASS 1

Week # 11 – SEX and CLASS

Cristal Vázquez Dávila

Walden University

Week # 11 – SEX and CLASS

For this week application the Codebook for gss04student spps file.doc, was used to hand-picked the category of variable necessary for the analysis. The gss04student_corrected.sav data file was used to conduct a Chi-Square Test of Independence in SPSS of (SEX) and (CLASS) (Frankfort-Nachmias, Nachmias, & Deward, 2015; Green, S. B., & Salkind, N.J., 2014).

Analysis of Chi- Square Assumption

According to Green and Salkind (2014, p. 331) two assumptions of the data that must be met to ensure the results of the Chi-Square analysis can be generalized to the population. Being the first one that, the observations must be independent. This means that each cell count refers to different individuals or cases. No individuals or cases appear in more than one cell. Also there would be no link between people in each cell. The other assumption, is that the cell counts must be large enough to ensure the statistic’s distribution has the proper shape for this statistic in this case being a Chi- Square distribution. Therefore since all the expected cell counts are greater than 5 we can be relatively confident that this assumption was met (See table 1).

Analysis of Cell differ

In CLASS, we see the Adjusted Residuals for Lower Class of |-1.9| and |1.9| are not equal to or greater than 2. Therefore these cells are not different from expected. Among female or male of Lower Class, approximately equal numbers were male as were female. In Working Class we the Adjusted Residuals are also not equal to or greater than 2 |-1.5| and |1.5|. For that reason these cells are not different from expected. Among female or male of Working Class, approximately equal numbers were male as were female. In Middle Class, we see the Adjusted Residuals of |2.0| and |-2.0| are equal 2. This tells us that more male than expected and fewer female than expected in the Middle Class. More precisely, the Residuals indicate that 19.5 more male and -19.5 fewer female than expected are part of the Middle Class. (See table 1)

Table 1: SUBJECTIVE CLASS IDENTIFICATION (Cross tabulation)

SUBJECTIVE CLASS IDENTIFICATION * RESPONDENTS SEX Crosstabulation

RESPONDENTS SEX

Total

MALE

FEMALE

SUBJECTIVE CLASS IDENTIFICATION

LOWER CLASS

Count

39

64

103

Expected Count

48.5

54.5

103.0

% within SUBJECTIVE CLASS IDENTIFICATION

37.9%

62.1%

100.0%

% within RESPONDENTS SEX

5.5%

8.1%

6.9%

% of Total

2.6%

4.3%

6.9%

Residual

-9.5

9.5

Adjusted Residual

-1.9

1.9

WORKING CLASS

Count

297

365

662

Expected Count

311.5

350.5

662.0

% within SUBJECTIVE CLASS IDENTIFICATION

44.9%

55.1%

100.0%

% within RESPONDENTS SEX

42.2%

46.1%

44.3%

% of Total

19.9%

24.4%

44.3%

Residual

-14.5

14.5

Adjusted Residual

-1.5

1.5

MIDDLE CLASS

Count

340

341

681

Expected Count

320.5

360.5

681.0

% within SUBJECTIVE CLASS IDENTIFICATION

49.9%

50.1%

100.0%

% within RESPONDENTS SEX

48.3%

43.1%

45.5%

% of Total

22.7%

22.8%

45.5%

Residual

19.5

-19.5

Adjusted Residual

2.0

-2.0

UPPER CLASS

Count

28

22

50

Expected Count

23.5

26.5

50.0

% within SUBJECTIVE CLASS IDENTIFICATION

56.0%

44.0%

100.0%

% within RESPONDENTS SEX

4.0%

2.8%

3.3%

% of Total

1.9%

1.5%

3.3%

Residual

4.5

-4.5

Adjusted Residual

1.3

-1.3

Total

Count

704

792

1496

Expected Count

704.0

792.0

1496.0

% within SUBJECTIVE CLASS IDENTIFICATION

47.1%

52.9%

100.0%

% within RESPONDENTS SEX

100.0%

100.0%

100.0%

% of Total

47.1%

52.9%

100.0%

Analysis of results of the Chi-Square Test of Independence

The Chi-Square value was 8.628. The degrees of freedom are 3. The p value is equal to .035. Because the p value is less than a typical alpha level of .05, we can reject the null hypothesis that the variables are independent; the Chi-Square test is significant. CLASS (lower; working, middle, or, upper) was not independent of whether or not participants were male or female (SEX),  2(2, N = 1496) = 8.63, p = .035. (See table 2)

Table 2- Chi-Square Test

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

8.628a

3

.035

Likelihood Ratio

8.670

3

.034

Linear-by-Linear Association

8.533

1

.003

N of Valid Cases

1496

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 23.53.

Analysis of Effect size

Cramer’s V was = .076. This is considered a small effect. For Cramer’s V and Phi values near .10 are considered small, near .30 are considered medium, and near .50 are considered large. This is typically reported with the Chi-Square results. (See table Symmetric Measures in the Appendix)

The Relationship between SEX and CLASS

A Chi-Square test of Independence was conducted to explore the relationship between the variables SEX (male; female) and CLASS (lower, working, middle, and upper). Participants indicated if they were male or female (1 = Male, 2 = Female) and were categorized as one of eight categories (0 = NAP, 1 = Lower, 2 = Working, 3 = Middle, 4 = Upper, 5= No Class, 8= DK, 9= NA). The Chi-Square test of Independence was appropriate because both variables were categorical.

One assumption of the Chi-Square test is that the observations are independent (Green &

Salkind, 2014, p. 331). No participants belonged to more than one category. Therefore, this condition was met. A second assumption is that the statistic will have a distribution shaped like a

Chi-Square distribution. Because all expected cell counts were higher than five, this assumption was met as well.

The null hypothesis predicted that SEX and CLASS variables would be independent. The alternative hypothesis was that SEX and CLASS variable would not be independent. The Pearson Chi-Square test was significant,  2(2, N = 1496) =8.63, p = .035, Cramer’s V = .76. The null hypothesis was rejected and it was concluded that the variables were not independent, though the low value for Cramer’s V indicates a small effect size.

Figure 1 shows a clustered bar graph of the number of participants in each variable. In the Lower Class (N = 103) approximately equal numbers of Male (N = 39, 38%) and Female (N = 64, 62%). the Adjusted Residuals for Lower Class of |-1.9| and |1.9| are not equal to or greater than 2.

In Working Class (N= 662) 297 participants (43%) reported being Male and 365 (55%) reported that they were Female. The Adjusted Residuals are also not equal to or greater than 2 |-1.5| and |1.5|. However in Middle Class variable (N= 681) patterns reverses. In this variable Male (N= 340, 48%) and Females (N= 341, 43%), the Adjusted Residuals of |2.0| and |-2.0| are equal 2. This tells us that more male than expected and fewer female than expected in the Middle Class. More precisely, the Residuals indicate that 19.5 more male and -19.5 fewer female than expected are part of the Middle Class. When it comes to Upper Class participants (N= 50); male (N= 28, 56%) and female (N= 22, 44%). Upper class had an adjusted residuals of |-0.50| and |0.50| respectively. Because the absolute values of the adjusted residuals were lower than 2.00.

In summary, these results show a small, but significant relationship between SEX and CLASS. The relationship between Female and Male in Lower and Upper Class is observed. Also there is relatively equal results among Females and Males in Working and Middle Class variables.

References

Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). New York: Worth.

Green, S. B., & Salkind, N. J. (2014). Using SPSS for Windows and Macintosh: Analyzing and understanding data (7th ed.). Upper Saddle River, NJ: Pearson.

Appendix SPSS Syntax and Output

GET

FILE=’C:\Users\Cristal\Documents\walden\RSCH 8200I-4\WK11 Zip File\gss04student_corrected.sav’.

DATASET NAME DataSet1 WINDOW=FRONT.

CROSSTABS

/TABLES=CLASS BY SEX

/FORMAT=AVALUE TABLES

/STATISTICS=CHISQ PHI

/CELLS=COUNT EXPECTED ROW COLUMN TOTAL RESID ASRESID

/COUNT ROUND CASE

/BARCHART.

Crosstabs

[DataSet1] C:\Users\Cristal\Documents\walden\RSCH 8200I-4\WK11 Zip File\gss04student_corrected.sav

Case Processing Summary

Cases

Valid

Missing

Total

N

Percent

N

Percent

N

Percent

SUBJECTIVE CLASS IDENTIFICATION * RESPONDENTS SEX

1496

99.7%

4

0.3%

1500

100.0%

SUBJECTIVE CLASS IDENTIFICATION * RESPONDENTS SEX Crosstabulation

RESPONDENTS SEX

Total

MALE

FEMALE

SUBJECTIVE CLASS IDENTIFICATION

LOWER CLASS

Count

39

64

103

Expected Count

48.5

54.5

103.0

% within SUBJECTIVE CLASS IDENTIFICATION

37.9%

62.1%

100.0%

% within RESPONDENTS SEX

5.5%

8.1%

6.9%

% of Total

2.6%

4.3%

6.9%

Residual

-9.5

9.5

Adjusted Residual

-1.9

1.9

WORKING CLASS

Count

297

365

662

Expected Count

311.5

350.5

662.0

% within SUBJECTIVE CLASS IDENTIFICATION

44.9%

55.1%

100.0%

% within RESPONDENTS SEX

42.2%

46.1%

44.3%

% of Total

19.9%

24.4%

44.3%

Residual

-14.5

14.5

Adjusted Residual

-1.5

1.5

MIDDLE CLASS

Count

340

341

681

Expected Count

320.5

360.5

681.0

% within SUBJECTIVE CLASS IDENTIFICATION

49.9%

50.1%

100.0%

% within RESPONDENTS SEX

48.3%

43.1%

45.5%

% of Total

22.7%

22.8%

45.5%

Residual

19.5

-19.5

Adjusted Residual

2.0

-2.0

UPPER CLASS

Count

28

22

50

Expected Count

23.5

26.5

50.0

% within SUBJECTIVE CLASS IDENTIFICATION

56.0%

44.0%

100.0%

% within RESPONDENTS SEX

4.0%

2.8%

3.3%

% of Total

1.9%

1.5%

3.3%

Residual

4.5

-4.5

Adjusted Residual

1.3

-1.3

Total

Count

704

792

1496

Expected Count

704.0

792.0

1496.0

% within SUBJECTIVE CLASS IDENTIFICATION

47.1%

52.9%

100.0%

% within RESPONDENTS SEX

100.0%

100.0%

100.0%

% of Total

47.1%

52.9%

100.0%

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

8.628a

3

.035

Likelihood Ratio

8.670

3

.034

Linear-by-Linear Association

8.533

1

.003

N of Valid Cases

1496

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 23.53.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Phi

.076

.035

Cramer’s V

.076

.035

N of Valid Cases

1496

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