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– ## CorrelationandChi-Square.docx – classs_survey-11.sav **## CorrelationandChi-Square.docx** Part 1: Examining the relationship between two continuous variables using Pearson’s r Using the class_survey dataset and the variables Alcohol and Cigarettes, you are going to address the following resea…
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– ## CorrelationandChi-Square.docx
– classs_survey-11.sav
**## CorrelationandChi-Square.docx**
Part 1: Examining the relationship between two continuous variables using Pearson’s r
Using the class_survey dataset and the variables Alcohol and Cigarettes, you are going to address the following research questions:
You suspect that people who drink more alcohol are also more likely to smoke cigarettes due to disinhibiting effects of alcohol use. Thus, your research question is:
RQ1: Is there a relationship between alcohol consumption and smoking cigarettes?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for the research question.
In SPSS: First, create a scatterplot for alcohol use and cigarette use.
Compute Pearson’s r in SPSS for this relationship.
Describe the scatterplots (discuss the relationship between variables).
Then, report and discuss the bivariate correlation (i.e., significance, direction and strength) in the proper format (see examples below) and your decision regarding your hypotheses.
You should turn in: your hypotheses, scatterplots (from spss output), written description of scatterplots and report of Pearson's r, and decision regarding hypotheses.
Writing example for correlations:
Pearson’s product-moment correlation was conducted to determine the relationship between hours spent studying and GPA, hours spent playing video games and GPA and hours spent studying and hours spent playing video games. Hours spent playing video games and GPA were moderately negatively correlated, r(123) = – .32, p = .041 while hours spent studying and GPA were strongly positively correlated, r(123) = .61, p = .011. Finally, hours spent playing video games and hours spent studying were moderately negatively correlated, r(123) = -.52, p = .02.
Part 2: Examining two categorical variables using Chi-Square
You suspect that males are more likely to report having been arrested compared to females. Thus, using the class_survey dataset and the variables Gender and Arrest, you are going to address the following research question:
RQ1: Is having been arrested independent of gender?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for this research question.
Conduct a chi-square test in SPSS to determine if the data fail to reject or reject the null hypothesis. Make sure to request the appropriate counts and percentages from SPSS.
Write a paragraph describing and discussing the findings from the SPSS output. Remember to report the appropriate statistics (including any relevant percentages/counts) along with the effect size (note. follow APA format when reporting any statistics in text; make sure to properly interpret the effect size [Phi and Cramer’s V] with regard to the strength of the effect). Make sure to discuss the findings in terms of the hypotheses. Do NOT turn in your SPSS output for chi-square but make sure the proper statistics are reported in your write-up of findings.
You should turn in: null and research hypothesis, written description of what you observed from the crosstab and chi-square test (including discussion of observed frequencies/percentages), determine decision for hypotheses, and reported effect size with mention of magnitude of the relationship.
Examples for reporting chi-square results:
The percentage of students who reported ever being suspended from urban schools was 25% whereas the percentage from suburban schools was 9%. Based on a chi-square test, this difference was found to be significant, χ²(1, N = 167) = 22.01, p < 0.001. Despite the significant effect, the relationship between suspension and school location was weak (Φ = .16).
A Chi-square test of independence was calculated to determine if recidivism (a dichotomous outcome) was related to dispositional outcome (secure placement, probation, drug court). Of those receiving secure placement 38% recidivated compared to 12% receiving probation and 9% receiving drug court. These differences in recidivism across disposition were significant, χ²(2, N = 70) = 6.78, p = 0.03, V = .33.
Please only turn in your write up for parts 1 and 2 and the scatterplots output from part 1 – that is, I do not need to see your SPSS output for the other analyses but the proper statistics should be reported.
MAKE SURE YOUR ASSIGNMENT IS FREE FROM GRAMMATICAL ERRORS AND REPORTS STATISTICS IN THE PROPER FORMAT.
**classs_survey-11.sav**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/classs-survey-11-sav)
**classs_survey-11.sav**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/classs-survey-11-sav)
**## CorrelationandChi-Square.docx**
Part 1: Examining the relationship between two continuous variables using Pearson’s r
Using the class_survey dataset and the variables Alcohol and Cigarettes, you are going to address the following research questions:
You suspect that people who drink more alcohol are also more likely to smoke cigarettes due to disinhibiting effects of alcohol use. Thus, your research question is:
RQ1: Is there a relationship between alcohol consumption and smoking cigarettes?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for the research question.
In SPSS: First, create a scatterplot for alcohol use and cigarette use.
Compute Pearson’s r in SPSS for this relationship.
Describe the scatterplots (discuss the relationship between variables).
Then, report and discuss the bivariate correlation (i.e., significance, direction and strength) in the proper format (see examples below) and your decision regarding your hypotheses.
You should turn in: your hypotheses, scatterplots (from spss output), written description of scatterplots and report of Pearson's r, and decision regarding hypotheses.
Writing example for correlations:
Pearson’s product-moment correlation was conducted to determine the relationship between hours spent studying and GPA, hours spent playing video games and GPA and hours spent studying and hours spent playing video games. Hours spent playing video games and GPA were moderately negatively correlated, r(123) = – .32, p = .041 while hours spent studying and GPA were strongly positively correlated, r(123) = .61, p = .011. Finally, hours spent playing video games and hours spent studying were moderately negatively correlated, r(123) = -.52, p = .02.
Part 2: Examining two categorical variables using Chi-Square
You suspect that males are more likely to report having been arrested compared to females. Thus, using the class_survey dataset and the variables Gender and Arrest, you are going to address the following research question:
RQ1: Is having been arrested independent of gender?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for this research question.
Conduct a chi-square test in SPSS to determine if the data fail to reject or reject the null hypothesis. Make sure to request the appropriate counts and percentages from SPSS.
Write a paragraph describing and discussing the findings from the SPSS output. Remember to report the appropriate statistics (including any relevant percentages/counts) along with the effect size (note. follow APA format when reporting any statistics in text; make sure to properly interpret the effect size [Phi and Cramer’s V] with regard to the strength of the effect). Make sure to discuss the findings in terms of the hypotheses. Do NOT turn in your SPSS output for chi-square but make sure the proper statistics are reported in your write-up of findings.
You should turn in: null and research hypothesis, written description of what you observed from the crosstab and chi-square test (including discussion of observed frequencies/percentages), determine decision for hypotheses, and reported effect size with mention of magnitude of the relationship.
Examples for reporting chi-square results:
The percentage of students who reported ever being suspended from urban schools was 25% whereas the percentage from suburban schools was 9%. Based on a chi-square test, this difference was found to be significant, χ²(1, N = 167) = 22.01, p < 0.001. Despite the significant effect, the relationship between suspension and school location was weak (Φ = .16).
A Chi-square test of independence was calculated to determine if recidivism (a dichotomous outcome) was related to dispositional outcome (secure placement, probation, drug court). Of those receiving secure placement 38% recidivated compared to 12% receiving probation and 9% receiving drug court. These differences in recidivism across disposition were significant, χ²(2, N = 70) = 6.78, p = 0.03, V = .33.
Please only turn in your write up for parts 1 and 2 and the scatterplots output from part 1 – that is, I do not need to see your SPSS output for the other analyses but the proper statistics should be reported.
MAKE SURE YOUR ASSIGNMENT IS FREE FROM GRAMMATICAL ERRORS AND REPORTS STATISTICS IN THE PROPER FORMAT.
**classs_survey-11.sav**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/classs-survey-11-sav)
**## CorrelationandChi-Square.docx**
Part 1: Examining the relationship between two continuous variables using Pearson’s r
Using the class_survey dataset and the variables Alcohol and Cigarettes, you are going to address the following research questions:
You suspect that people who drink more alcohol are also more likely to smoke cigarettes due to disinhibiting effects of alcohol use. Thus, your research question is:
RQ1: Is there a relationship between alcohol consumption and smoking cigarettes?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for the research question.
In SPSS: First, create a scatterplot for alcohol use and cigarette use.
Compute Pearson’s r in SPSS for this relationship.
Describe the scatterplots (discuss the relationship between variables).
Then, report and discuss the bivariate correlation (i.e., significance, direction and strength) in the proper format (see examples below) and your decision regarding your hypotheses.
You should turn in: your hypotheses, scatterplots (from spss output), written description of scatterplots and report of Pearson's r, and decision regarding hypotheses.
Writing example for correlations:
Pearson’s product-moment correlation was conducted to determine the relationship between hours spent studying and GPA, hours spent playing video games and GPA and hours spent studying and hours spent playing video games. Hours spent playing video games and GPA were moderately negatively correlated, r(123) = – .32, p = .041 while hours spent studying and GPA were strongly positively correlated, r(123) = .61, p = .011. Finally, hours spent playing video games and hours spent studying were moderately negatively correlated, r(123) = -.52, p = .02.
Part 2: Examining two categorical variables using Chi-Square
You suspect that males are more likely to report having been arrested compared to females. Thus, using the class_survey dataset and the variables Gender and Arrest, you are going to address the following research question:
RQ1: Is having been arrested independent of gender?
Here is what you are to do for the assignment:
Compose a null and research hypothesis for this research question.
Conduct a chi-square test in SPSS to determine if the data fail to reject or reject the null hypothesis. Make sure to request the appropriate counts and percentages from SPSS.
Write a paragraph describing and discussing the findings from the SPSS output. Remember to report the appropriate statistics (including any relevant percentages/counts) along with the effect size (note. follow APA format when reporting any statistics in text; make sure to properly interpret the effect size [Phi and Cramer’s V] with regard to the strength of the effect). Make sure to discuss the findings in terms of the hypotheses. Do NOT turn in your SPSS output for chi-square but make sure the proper statistics are reported in your write-up of findings.
You should turn in: null and research hypothesis, written description of what you observed from the crosstab and chi-square test (including discussion of observed frequencies/percentages), determine decision for hypotheses, and reported effect size with mention of magnitude of the relationship.
Examples for reporting chi-square results:
The percentage of students who reported ever being suspended from urban schools was 25% whereas the percentage from suburban schools was 9%. Based on a chi-square test, this difference was found to be significant, χ²(1, N = 167) = 22.01, p < 0.001. Despite the significant effect, the relationship between suspension and school location was weak (Φ = .16).
A Chi-square test of independence was calculated to determine if recidivism (a dichotomous outcome) was related to dispositional outcome (secure placement, probation, drug court). Of those receiving secure placement 38% recidivated compared to 12% receiving probation and 9% receiving drug court. These differences in recidivism across disposition were significant, χ²(2, N = 70) = 6.78, p = 0.03, V = .33.
Please only turn in your write up for parts 1 and 2 and the scatterplots output from part 1 – that is, I do not need to see your SPSS output for the other analyses but the proper statistics should be reported.
MAKE SURE YOUR ASSIGNMENT IS FREE FROM GRAMMATICAL ERRORS AND REPORTS STATISTICS IN THE PROPER FORMAT.
**classs_survey-11.sav**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/classs-survey-11-sav)
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