Chi Square
Part 1:
Green & Salkind: Lesson 40, Exercises 1–4
The following helpful tips are numbered to correspond with the exercise number to which they refer (a dash indicates that no tips are needed):
 Use the method reviewed in the presentation to weight the cases for this data set. (no points—done in data file)
 Do a, b, and c. (2 pts for output and 2 pts each for a–c)
  (2 pts)
 All homework “Results sections” must follow the example given in the Course Content document “Writing Results of Statistical Tests in Current APA Format” (Note: you do not have to refer to a figure). (2 pts)
Green & Salkind: Lesson 41, Exercises 1–3
The following helpful tips are numbered to correspond with the exercise number to which they refer (a dash indicates that no tips are needed):
Lilly collects data on a sample of 130 high school students to evaluate whether the proportion on female high school students who thake advanced mathe courses in high school varies demending upons whether they have been raised primiarily by their father or by both their mother and their father. The SPSS data File contains two variables: math (0 = no advanced math and 1 = some advanced math) and parent ( 1 = primararily father and 2 = father and mother)
NOTE: This exercise does not use the weighted cases method. Use the data file “as is.”
 Do a, b, c, d, and e. For letter “e,” this question is asking specifically about effect size. (2 pts for output and 2 pts each for a–e)
Conduct a crosstabs analysis to examing whether the proportion of female high school students who take advanced mathe courses is different for different levels of the parent variable. From the output, identify the following:
Case Processing Summary 


Cases 

Valid 
Missing 
Total 

N 
Percent 
N 
Percent 
N 
Percent 

Parents of female high school student * Math classes 
130 
100.0% 
0 
0.0% 
130 
100.0% 
Parents of female high school student * Math classes Crosstabulation 


Math classes 
Total 

No advanced math 
Some advanced math 

Parents of female high school student 
Primarily father 
Count 
21 
9 
30 
Expected Count 
26.1 
3.9 
30.0 

% within Parents of female high school student 
70.0% 
30.0% 
100.0% 

Father and mother 
Count 
92 
8 
100 

Expected Count 
86.9 
13.1 
100.0 

% within Parents of female high school student 
92.0% 
8.0% 
100.0% 

Total 
Count 
113 
17 
130 

Expected Count 
113.0 
17.0 
130.0 

% within Parents of female high school student 
86.9% 
13.1% 
100.0% 
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Exact Sig. (2sided) 
Exact Sig. (1sided) 
Pearson ChiSquare 
9.826^{a} 
1 
.002 


Continuity Correction^{b} 
7.986 
1 
.005 


Likelihood Ratio 
8.434 
1 
.004 


Fisher's Exact Test 



.004 
.004 
LinearbyLinear Association 
9.751 
1 
.002 


N of Valid Cases 
130 




a. 1 cells (25.0%) have expected count less than 5. The minimum expected count is 3.92. 

b. Computed only for a 2x2 table 
Symmetric Measures 


Value 
Approx. Sig. 

Nominal by Nominal 
Phi 
.275 
.002 
Cramer's V 
.275 
.002 

N of Valid Cases 
130 

 Percentage of female students who took no advanced math classes? 13.1%
 Percent of female students who took no advanced math classes when female students were raised by their fathers? 70.0%
 Percent of female students raised by their father only?
 χ^{2 }value χ^{2 }(1, N = 130) = 9.83
 Strength of relationship between taking advanced math classes and level aof parenting
  (2 pts) Create a clustered bar graph to show differences in the number of female students taking some advanced mathe classes for the different categories of parenting.
 All homework “Results sections” must follow the example given in the Course Content document “Writing Results of Statistical Tests in Current APA Format” (Note: you do not have to refer to a figure). (2 pts)
Part 2:
 An industrial/organizational (I/O) psychologist is helping a company determine the type of work stations preferred by its employees. The business owner believes that people who work in different departments may prefer different work station layouts. In order to examine this claim, the I/O psychologist sets up 3 simulated work stations: private office (PO), semiprivate office (SPO), and open floor plan (OFP). She recruits employees from 3 different departments: Information Technology, Human Resources, and Marketing. The participants spend 30 minutes in each simulated work station performing general prearranged tasks. At the end of the 1.5 hours, the participants turn in a form on which they mark which work station they prefer. The results are listed in the table on the following page. Perform a chi square test of independence (using an SPSS twoway contingency table analysis) to determine whether the proportions of work station preferences differ across departments. Use the weighted cases method.
The steps will be the same as the ones you have been practicing in Part 1 of the assignment—the only difference is that you are now responsible for creating the data file as well. Remember to name and define your variables under the “Variable View,” then return to the “Data View” to enter the data. (2 pts)

Private Office 
SemiPrivate Office 
Open Floor Plan 
TOTAL 
Information Technology 
9 
6 
4 
19 
Human Resources 
6 
10 
3 
19 
Marketing 
7 
3 
9 
19 
TOTAL 
22 
19 
16 
57 
 Create a clustered bar graph depicting your results. (2 pts)
 Write an APAstyle Results section describing the outcome. All homework “Results sections” must follow the example given in the Course Content document “Writing Results of Statistical Tests in Current APA Format” (Note: you do not have to refer to a figure). (2 pts)
Part 3: Cumulative Homework
 A researcher wants to find out if the number of absences from a chemistry class are predictive of final exam scores at a local university. The data from the past term are in the table below. Are number of absences predictive of final exam scores? Choose the correct test to analyze this question, set up the SPSS file, and run the analysis. Follow the directions on the following page.
Number of Absences 
Final Exam Scores 
1 1 2 3 4 5 5 5 6 6 6 7 7 
98 95 89 89 80 85 80 75 76 69 70 62 60 
 Paste appropriate SPSS output. (2 pts)
 Paste appropriate SPSS graph. (2 pts)
 Write an APAstyle Results section describing the outcome. All homework “Results sections” must follow the example given in the Course Content document “Writing Results of Statistical Tests in Current APA Format” (Note: you do not have to refer to a figure). (2 pts)
Submit this assignment by 11:59 p.m. (ET) on Monday of Module/Week 7.
Part 4: Phase Study
It is time to present your findings. During this phase, you will write a short Results section in current APA style that presents the results of your statistical test as well as interprets these results in light of the research question. The Results section must be 1–2 paragraphs must include:
 The results of your analysis, including the value of the appropriate test statistic, the significance level, and any other pertinent information (sample size, etc.).
 Several sentences that interpret these results, including the following information:
 Were the results significant or not?
 Do these results lead you to accept or reject the null hypothesis?
 What are the strengths and weaknesses of the statistical test that was used?
 Are there any characteristics of the sample or the data collection method that should be taken into consideration when interpreting these results that you would mention briefly to the reader?
Remember that the Results section is not a Discussion section. Therefore, it is NOT the place to make any wideranging statements about doctrine in general, how surprised (or not surprised) you are by the results, whether they correspond with other research, etc. You will have a chance in the last phase of the lab to share your thoughts and insights, but remember for this phase that Results sections focus on data. Use the sections in your textbooks as guides concerning content and style. You can also use the Publication Manual of the American Psychological Association as a guide (if you have one), or visit this website for more guidance: http://web.psych.washington.edu/writingcenter/writingguides/pdf/stats.pdf
Descriptive Statistics 


Mean 
Std. Deviation 
N 
per_god 
4.2857 
1.06904 
14 
jc_god 
4.1429 
1.16732 
14 
jc_rose 
4.1429 
1.16732 
14 
plp_good 
3.0714 
.82874 
14 
jc_sacrifice 
4.1429 
1.09945 
14 
gdev_cir 
2.6429 
1.00821 
14 
gdwrd_true 
4.5714 
.51355 
14 
fthjc_hvn 
4.4286 
.75593 
14 
gfgs_ghs 
4.5000 
.75955 
14 
total_und 
35.9286 
35.08334 
14 
Correlations 


per_god 
jc_god 
jc_rose 
plp_good 
jc_sacrifice 
gdev_cir 
gdwrd_true 
fthjc_hvn 
gfgs_ghs 
total_und 

per_god 
Pearson Correlation 
1 
.951^{**} 
.951^{**} 
.025 
.944^{**} 
.326 
.240 
.027 
.189 
.216 
Sig. (2tailed) 

.000 
.000 
.933 
.000 
.255 
.408 
.926 
.517 
.458 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

jc_god 
Pearson Correlation 
.951^{**} 
1 
1.000^{**} 
.068 
.942^{**} 
.215 
.367 
.274 
.434 
.276 
Sig. (2tailed) 
.000 

.000 
.817 
.000 
.461 
.197 
.343 
.121 
.339 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

jc_rose 
Pearson Correlation 
.951^{**} 
1.000^{**} 
1 
.068 
.942^{**} 
.215 
.367 
.274 
.434 
.276 
Sig. (2tailed) 
.000 
.000 

.817 
.000 
.461 
.197 
.343 
.121 
.339 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

plp_good 
Pearson Correlation 
.025 
.068 
.068 
1 
.072 
.033 
.103 
.070 
.061 
.117 
Sig. (2tailed) 
.933 
.817 
.817 

.806 
.911 
.725 
.812 
.836 
.691 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

jc_sacrifice 
Pearson Correlation 
.944^{**} 
.942^{**} 
.942^{**} 
.072 
1 
.367 
.253 
.198 
.276 
.341 
Sig. (2tailed) 
.000 
.000 
.000 
.806 

.197 
.383 
.497 
.339 
.232 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

gdev_cir 
Pearson Correlation 
.326 
.215 
.215 
.033 
.367 
1 
.127 
.115 
.251 
.021 
Sig. (2tailed) 
.255 
.461 
.461 
.911 
.197 

.664 
.695 
.386 
.943 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

gdwrd_true 
Pearson Correlation 
.240 
.367 
.367 
.103 
.253 
.127 
1 
.510 
.789^{**} 
.164 
Sig. (2tailed) 
.408 
.197 
.197 
.725 
.383 
.664 

.063 
.001 
.575 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

fthjc_hvn 
Pearson Correlation 
.027 
.274 
.274 
.070 
.198 
.115 
.510 
1 
.804^{**} 
.317 
Sig. (2tailed) 
.926 
.343 
.343 
.812 
.497 
.695 
.063 

.001 
.269 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

gfgs_ghs 
Pearson Correlation 
.189 
.434 
.434 
.061 
.276 
.251 
.789^{**} 
.804^{**} 
1 
.206 
Sig. (2tailed) 
.517 
.121 
.121 
.836 
.339 
.386 
.001 
.001 

.479 

N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

total_und 
Pearson Correlation 
.216 
.276 
.276 
.117 
.341 
.021 
.164 
.317 
.206 
1 
Sig. (2tailed) 
.458 
.339 
.339 
.691 
.232 
.943 
.575 
.269 
.479 


N 
14 
14 
14 
14 
14 
14 
14 
14 
14 
14 

**. Correlation is significant at the 0.01 level (2tailed). 
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