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APA
Subject:
Health, Medicine, Nursing
Type:
Lab Report
Language:
English (U.S.)
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Topic:

Chi-Square Test

Lab Report Instructions:

Using the provided dataset from SLP 1, calculate the appropriate descriptive statistics for the following variables, comparing diabetes with no diabetes status: gender, race, salary, education, height, weight, BMI, allergies, family history diabetes, family history allergies. For chi-square tests, report the chi-square value and the p-value (if p-value < 0.05, then the test is significant). For t-tests, report the t-test value and the p-value. Include a 2- to 3-page description of the descriptive statistics, including tables of the summarized data. This is similar to a "Results" section in a published manuscript or journal article. Use the following online calculators to obtain the results for this analysis.
Chi-Square for Categorical Data: http://www(dot)vassarstats(dot)net/
Choose "Frequency Data" from the far left, then "Chi-Square, Cramer's V, and Lambda" from the middle of the page.
Enter in the number of people in each category (e.g., number of women who have diabetes, number of men with diabetes, etc.).
Example of a table below:
Diabetes No Diabetes
Female 86 214
Male 36 264
Choose a 2 x 2 table and where A1 = 86, A2 = 36; B1 = 214; B2 = 264.
Report the percent of people in each category and the chi-square and p-value. A possible sentence to interpret the results could be:
There are significantly more women (64%) who have diabetes than men (36%).
T-Tests for Continuous Data: http://www(dot)vassarstats(dot)net/
Choose "T-Tests & Procedure" from the far left, then "Two-Sample t-Test", then click "Independent Samples" under Setup.
Copy and Paste the values for those with diabetes into Sample A and those without diabetes into Sample B, then click Calculate.
For instance, copy and paste all of the ages of those with diabetes into Sample A and all of the ages of those without diabetes into Sample B. From the Data Summary window, report the Mean of those with Diabetes (Sample A) and those without Diabetes (Sample B); also report the "t" from the Results box, as well as the two-tailed p-value. A "p" that is <0.05 suggests that the result is statistically significant. One way to report such a finding would be to use the following language:
The average age of those with diabetes is __ years and for those without diabetes, the average is __ years. Those with diabetes were significantly older/younger (p<0.05).
Assignment should be at least 2 pages (500 words) in length.

Lab Report Sample Content Preview:

BIOSTATISTICS
Student's Name
Institution
Course
Instructor's Name
Date
Chi-Square Test
The chi-square statistic is a non-parametric statistic that is commonly used to test the association between categorical data. It is mostly used as a test of independence and compares the differences between observed values and expected values in a contingency table. A chi-square test allows testing hypotheses when the variables are measured at the nominal level (Turhan, 2020). It assumes that the data is from a simple random sample, the sample size is sufficiently large so that the expected cell counts can be adequate and that the observations are independent (Hess & Hess, 2017). The null hypothesis of this test states that no association exists between the variables in the population. On the other hand, the alternative hypothesis is that an association exists between the variables.
This project aims to perform chi-square tests to establish whether there is an association between diabetes status (have diabetes, no diabetes) and the following categorical variables: gender, race, education, allergies, family history diabetes, and family history allergies.
Diabetes status and Gender
H0: Diabetes is not significantly associated with gender
H1: Diabetes is significantly associated with gender
The table below shows a cross-tabulation of diabetes status and gender and the chi-square results.

gender

 

 

Chi-Square Statistic

P-Value

Diabetes Status

female

male

Grand Total



No

53.9%

46.1%

100%


0.76

Yes

51.4%

48.6%

100%

0.09


Grand Total

53.0%

47%

100%



Table SEQ Table \* ARABIC 1: A cross-tabulation of diabetes status and gender and the chi-square results.
Despite females with diabetes (51.4%) being more than men with diabetes (48.6%), there was no significant association between diabetes and gender, χ2(1, N = 300) = 0.09, p = .76.
Diabetes Status and Race
H0: Diabetes is not associated with race
H1: Diabetes is associated with race
The table below shows a cross-tabulation of diabetes status and race and the chi-square results.

Race


Diabetes Status

African American

Asian American

Hispanic

Native American& Other

White

Grand Total

No

12%

7%

17%

9%

56%

100%

Yes

12%

7%

21%

8%

52%

100%

Grand Total

12%

7%

18%

5%

54.7%

100%

Chi-Square Statistic

1.13

P-Value

0.89

Table 2: A cross-tabulation of diabetes status and race and the chi-square results.
Whites with diabetes (52%) were more than other races. However, there was no significant association between diabetes and race, χ2 (4, N = 300) = 1.13, p = .89.
Diabetes Status and Education
H0: Diabetes is not associated with education
H1: Diabetes is associated with education
The table below shows a cross-ta...
Updated on
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