Social Work Research: Chi Square Research Paper

Molly, an administrator with a regional organization that advocates for alternatives to long-term prison sentences for nonviolent offenders, asked a team of researchers to conduct an outcome evaluation of a new vocational rehabilitation program for recently paroled prison inmates. The primary goal of the program is to promote full-time employment among its participants.  Social Work Research: Chi Square Research Paper

To evaluate the program, the evaluators decided to use a quasi-experimental research design. The program enrolled 30 individuals to participate in the new program. Additionally, there was a waiting list of 30 other participants who planned to enroll after the first group completed the program. After the first group of 30 participants completed the vocational program (the “intervention” group), the researchers compared those participants’ levels of employment with the 30 on the waiting list (the “comparison” group).

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In order to collect data on employment levels, the probation officers for each of the 60 people in the sample (those in both the intervention and comparison groups) completed a short survey on the status of each client in the sample. The survey contained demographic questions that included an item that inquired about the employment level of the client. This was measured through variables identified as none, part-time, or full-time. A hard copy of the survey was mailed to each probation officer and a stamped, self-addressed envelope was provided for return of the survey to the researchers.

After the surveys were returned, the researchers entered the data into an SPSS program for statistical analysis. Because both the independent variable (participation in the vocational rehabilitation program) and dependent variable (employment outcome) used nominal/categorical measurement, the bivariate statistic selected to compare the outcome of the two groups was the Pearson chi-square.

After all of the information was entered into the SPSS program, the following output charts were generated:

TABLE 1. CASE PROCESSING SUMMARY

Social Work Research: Chi Square Research Paper

TABLE 2. PROGRAM PARTICIPATION *EMPLOYMENT CROSS TABULATION

TABLE 3. CHI-SQUARE TESTS

Social Work Research: Chi Square Research Paper

5. Social Work Research: Chi Square Research Paper

5. ^a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 6.88.

The first table, titled Case Processing Summary, provided the sample size (N = 59). Information for one of the 60 participants was not available, while the information was collected for all of the other 59 participants.

The second table, Program Participation Employment Cross Tabulation, provided the frequency table, which showed that among participants in the intervention group, 18 or 60% were found to be employed full time, while 7 or 23% were found to be employed part time, and 5 or 17% were unemployed. The corresponding numbers for the comparison group (parolees who had not yet enrolled in the program but were on the waiting list for admission) showed that only 6 or 21% were employed full-time, while 7 or 24% were employed part time, and 16 or 55% were unemployed.

The third table, which provided the outcome of the Pearson chi-square test, found that the difference between the intervention and comparison groups were highly significant, with a p value of .003, which is significantly beyond the usual alpha-level of .05 that most researchers use to establish significance.

These results indicate that the vocational rehabilitation intervention program may be effective at promoting full-time employment among recently paroled inmates. However, there are multiple limitations to this study, including that 1) no random assignment was used, and 2) it is possible that differences between the groups were due to preexisting differences among the participants (such as selection bias).

Potential future studies could include a matched comparison group or, if possible, a control group. In addition, future studies should assess not only whether or not a recently paroled individual obtains employment but also the degree to which he or she is able to maintain employment, earn a living wage, and satisfy other conditions of probation.

(Plummer 63-65)

Plummer, Sara-Beth, Sara Makris, Sally Brocksen. Social Work Case Studies: Concentration Year. Laureate Publishing, 10/21/13. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

ADDITIONAL INFORMATION 

Chi Square Research Paper

Introduction

The purpose of this research paper is to provide an overview of chi square analysis and its application in data collection.

Abstract

The abstract is a concise statement of the research question, purpose of the study, methods and results. It should be written in present tense, active voice. The abstract should not exceed 100 words long.

The following are some examples:

• “The purpose of this study was to find out if students who attended classes that were taught by professor A were better prepared than those who attended classes taught by professor B.” (Abstract)

• “The main objective was to determine if there was any difference between two groups.” (Abstract)

Introduction

The introduction should include a brief summary of the research question, study design and methods. It should be no more than 1-2 paragraphs in length. The introduction should also state why this topic is significant to you or your audience.

Literature Review

In this section, you will be introduced to the concept of chi square and its uses in research. You will learn about how chi square works and examples of its application in various fields.

Chi Square was first developed by Gosset (1905). It is a non-parametric test that measures association between two variables by comparing the difference between their expected frequencies and observed frequencies at each cell entry of a contingency table. The null hypothesis states that there is no relationship between the variables at all; while the alternative hypothesis states that there is some relationship between them. Chi-square values can range from 0 (no association) to 1 (complete association), depending on whether one or both sides are equal to zero when compared with those used for establishing significance levels for other statistical tests such as Student t-test or Pearson Correlation Coefficient

Methods

To calculate a chi square test, you need to know the following:

• The hypothesis and null hypothesis. These are statements about your data that you want to test. In this case, we have an overall group size (the sample) and two independent variables (gender and age). The null hypothesis is that there is no difference between genders or ages within each group; i.e., that both groups have equal proportions of males and females with respect to those variables at each point in time (this should be true regardless of what else might be happening).

• The probability distribution functions for each combination of rows/columns/categories you expect to see if your data lies within its sampling frame—that is, if it’s random sampling from some population’s distribution–then we’ll call these p(r).

Results

The following results were obtained in this project:

• Chi-square test: The chi square test was used to determine whether or not there were any significant differences between the number of times a given variable appeared and its expected frequency. This is done by comparing your observed frequency with its expected value. If you can show that your observed frequencies do not match their expected values, then you may conclude that there are some unexpected events going on in your data set (i.e., it could be due to chance).

• Goodness of fit test: This involves calculating two statistics called goodness-of-fit measures—the Pearson correlation coefficient r and the standardized root mean square error (or “srm”). The Pearson r tells us how well two variables go together; while srm shows how far apart they are from each other when compared against their expected values under perfect conditions (i.e., no errors). Both should be close to zero when comparing datasets from different sources but may not always be exactly zero; thus, more care must be taken when interpreting these results than just looking at them visually!

Discussion and Conclusions

In this paper, we have discussed various methods of Chi-square test. We have also discussed the advantages and disadvantages of each method. Based on these results, we have concluded that Kappa coefficient is more powerful than Pearson’s correlation coefficient in evaluating the relationship between two variables. However, it should be used with caution as it has its own limitations which can be overcome by using other statistical methods such as ANOVA or MANOVA to estimate the effect size between two groups on a continuous scale measure such as grades in school or psychometric scores obtained from intelligence tests given at different times during childhood development stages (see [1]).

Takeaway:

The Chi Square test is a statistical test used to determine if the observed frequencies in a categorical variable differ significantly from the expected frequencies. The Chi Square test is used to test the null hypothesis that there is no association between two or more variables.

Conclusion

In conclusion, it can be said that the use of social media in this study has been a great way to gather data and allow me to see how people are reacting to different events. However, my findings should not be taken as an indication that everyone is happy about everything all the time. The main takeaway from this project was that people care more about their friends than they do their brands or companies.

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