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Create a research hypothesis in your area of study that would be answered using a chi square test of independence. Please list the question and then provide your answer right below each question. Include the following: 1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.2. What is the null hypothesis? What is the research hypothesis?3. Participants/Sampling Method: Describe your sampling method. What is your sample size? Who is your population of interest? How representative is the sample of the population under study?Data Analysis: Describe the statistical analysis. What are the requirements of the chi square? (HINT: This should be a chi square test of independence). What is your IV? What is your DV? What level of measurement are your IV and DV? What is your alpha level?4. Results & Discussion: Did you reject the null hypothesis? What information did you use to lead you to your conclusion? Was your observed p value greater than or less than your alpha? How do you interpret the findings? NOTE: You can just make up numbers, but include your made-up p value. Use a minimum of 2 sources. APA format is required including proper in text citations and a list of references. NOTE: Sources used in answering the Topic Question should come from peer-reviewed journals. This means no tweets, blogs, wikis, CNN.com, etc. should be used as resources. Minimum of 550 words for the text body (word count does not include the Questions and References/Work Cited word count). This is what my professor sent us yesterday through e-mail. “Your response should look like this:1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.Include your response in paragraph form here.2. What is the null hypothesis? What is the research hypothesis?Include your response in paragraph form here.etc.This format should help you to address EVERY question asked and it helps me in grading.You should also be defining the statistical test, its requirements, etc and providing an intext citation for this summary. For example: What is a t test? When should it be used? What are the requirements? Why is the particular test appropriate to answer your research hypothesis? “

Running Head:

CHI SQUARE

CHI SQUARE

Research using chi square

Name

University Affiliation

Course

Course Instructor

Date

This is what my professor sent us yesterday through e-mail.

**“Your response should look like this:
**

1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.

Include your response in paragraph form here.

2. What is the null hypothesis? What is the research hypothesis?

Include your response in paragraph form here.

etc.

This format should help you to address EVERY question asked and it helps me in grading.

You should also be defining the statistical test, its requirements, etc and providing an intext citation for this summary. For example: What is a t test? When should it be used? What are the requirements? Why is the particular test appropriate to answer your research hypothesis? “

Create a research hypothesis in your area of study that would be answered using a chi square test of independence. **Please list the question and then provide your answer right below each question.** Include the following:

1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.

2. What is the null hypothesis? What is the research hypothesis?

3. Participants/Sampling Method: Describe your sampling method. What is your sample size? Who is your population of interest? How representative is the sample of the population under study?

(THIS HAS TO BE INCLDUDED)

Data Analysis: Describe the statistical analysis. What are the requirements of the chi square? (HINT: This should be a chi square test of independence). What is your IV? What is your DV? What level of measurement are your IV and DV? What is your alpha level?

4. Results & Discussion: Did you reject the null hypothesis? What information did you use to lead you to your conclusion? Was your observed p value greater than or less than your alpha? How do you interpret the findings? NOTE: You can just make up numbers, but include your made-up p value.

**INTRODUCTION**

This research mainly involves a research involving a survey that aims at identifying whether voting is dependent with gender. This survey will mainly use chi square test of independence. This is because chi square test is mainly applied to a population with two categorical variables (BoÌhning & Holling, 1998). There our aim will mainly be to determine if there is any significant association between two groups of voters in an election based on the gender. (

WHAT TWO GROUPS? TEENAGERS AND ELDERLY?)

The sampling method used is simple random sampling. When conducting sampling population the population should be should be at least ten times its sample in the categories. Sampling is done and the sample data is displayed in a contingency table. The expected frequency count for each cell of the table is at least 5. This approach mainly includes four steps this is; state the hypothesis, formulate the analysis plan, analyze sample data and lastly interpret the results (BoÌhning & Holling, 1998).

Supposing that Variable A has r levels, and Variable B has c levels. The null hypothesis states that knowing the level of Variable A does not help you predict the level of Variable B. That is, the variables are independent. The research hypothesis is that knowing the level of Variable A can help you predict the level of Variable B. H0: Variable A & B are independent. |

A public view poll surveyed a simple random sample of 1200 voters. Respondents were categorized by gender (male or female) and by voting favorite (Republican, Democrat, or Independent). Results are displayed in the contingency table below.

520

Voting Preferences |
Row total |
|||

Republican |
Democrat |
Independent |
||

Male |
230 |
190 |
80 |
500 |

Female |
290 |
330 |
80 |
700 |

Column total |
520 |
160 |
1200 |

We work through those steps below:

· **State the hypotheses.** **(VERY CONFUSING AND VAGUE RESEARCH/NULL HYPOTHESIS…it’s almost as if you are just stating the definition of a null and research hypothesis….you didn’t really give a direct, specific one)
**

Supposing that Variable A has r levels, and Variable B has c levels. The null hypothesis state that with the knowledge of the level of Variable A does not help you forecasts the level of Variable B. That is to say, the variables are independent.

H0: Variable A and Variable B are independent. |

The research hypothesis is that knowing the level of Variable A may help you predict the level of Variable B. From that the first step is to state the null hypothesis and a research hypothesis of our problem. This will be;

H0: Gender and voting are independent.

Ha: Gender and voting are not independent.

· **Formulate an analysis plan**.

For this study, the significance level is 0.05. Using sample data above, we will therefore conduct a chi-square test for independence.

· **Analyze sample data**.

Relating the chi-square test for independence to sample data, we work out the degrees of freedom, the expected frequency counts, and the chi-square test statistic. Based on the chi-square statistic and the degrees of freedom, we define the P-value.

DF = (r – 1) * (c – 1) = (2 – 1) * (3 – 1) = 2

Er,c = (nr * nc) / n

E1,1 = (500 * 520) / 1200 = 260000/1200 = 216.7

E1,2 = (500 * 520) / 1200 = 260000/1200 = 216.7

E1,3 = (500 * 160) / 1200 = 80000/1200 = 66.7

E2,1 = (700 * 520) / 1200 = 364000/1200 = 303.3

E2,2 = (700 * 520) / 1200 = 364000/1200 = 303.3

E2,3 = (700 * 160) / 1200 = 112000/1200 = 93.3

Χ2 = Σ [ (Or,c – Er,c)2 / Er,c ]

Χ2 = (230 – 216.7)2/216.7 + (190 – 216.7)2/216.7 + (80 – 66.7)2/66.7

+ (290 – 303.3)2/303.3 + (330 – 303.3)2/303.3 + (80 – 93.3)2/93.3

Χ2 = 176.9/216.7 + 712.9/216.7 +176.89 /66.7 + 176.7/303.3 + 409.59/303.3 + 176.9/93.3

Χ2 = 0.82 + 3.3 + 2.65 + 0.58 + 1.35 + 1.9 = 10.6

The P-value can be defined or explained as probability that a chi-square statistic with 2 degrees of freedom is more great than 10.6 (BoÌhning & Holling, 1998). We use the Chi-Square Distribution Calculator to find P (Χ2 > 10.6) = 0.995

· **Interpret results**.

Since the P-value (0.995) is greater than the significance level (0.05), we accept the null hypothesis. Thus, we conclude that there is no relationship between gender and voting preference (Siegel, 2005).

CONCLUSION

In conclusion we can see that it plays a major role chi square plays a major role in the analysis of data hence it can be used to find major relationships in data and hence used in making decisions. Also the approach should be noted that the population is at least 10 times the sample size.

HERE ARE SOME EXAMPLES OF WHAT OTHER PEOPLE POSTED:

EXAMPLE 1

: Top of Form

Create a research hypothesis in your area of study that would be answered using a chi square test of independence. Please list the question and then provide your answer right below each question. Include the following:

1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.

This study seems to have grabbed quite a few medical students’ attention as I mentioned the idea around them. Many medical students share one great fear factor: the USMLE score or as known as the step. Many students spend hours trying to figure out how to gain as many points as possible on the exam and if stabbing one’s self with a needle daily guaranteed a score of 240, needles would be sold out worldwide. This study, however, doesn’t require needles or knives; it aims at observing the effect of weather on the step score and if there was a relationship. Step scores are to be compared in different seasons to help students identify the best timing to take the step (although it may not be so convenient to choose the season one has to take their step, it is a study worth looking into).

2. What is the null hypothesis? What is the research hypothesis?

The null hypothesis: there is no relationship between the season when the step 1 exame is administered and the students’ scores.

The research hypothesis: there is a direct relationship between the season when the step 1 exam is administered and the students’ scores.

3. Participants/Sampling Method: Describe your sampling method. What is your sample size? Who is your population of interest? How representative is the sample of the population under study?

Data Analysis: Describe the statistical analysis. What are the requirements of the chi square? (HINT: This should be a chi square test of independence). What is your IV? What is your DV? What level of measurement are your IV and DV? What is your alpha level?

For this study, I chose to conduct it in the same test center location in Virginia where a difference in seasonal weather can be observed. The scores of 200 students are collected, 100 scores taken in the summer time on days when the weather was between 80-90 degrees during the months of June and July. On the other hand, 100 scores from the winter time when the weather was below 45 degrees during the months of January and February. The scores will be based on 220 and above as acceptable while anything below that as unacceptable performance. In this study the independent variable is the step 1 exam score while the dependent variable is the weather. The alpha level for this experiment was .05.

4. Results & Discussion: Did you reject the null hypothesis? What information did you use to lead you to your conclusion? Was your observed p value greater than or less than your alpha? How do you interpret the findings? NOTE: You can just make up numbers, but include your made-up p value.

After conducting the research and collecting the data, it was determined that 87 students out of those who took their step during the summer had a score over 220 versus 79 students who took it during the winter. After calculating the chi square, it was determined that the Chi square has 2 degrees of freedom leading us to determine a p-value of .05. With that p-value, it is safe to reject the null hypothesis proving that there is a relationship between the weather and students’ performance on the step.

EXAMPLE 2:

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Top of Form

1. Introduction: Brief description of the study including the purpose and importance of the research question being asked.

Recently, electronic cigarette have grown in popularity among smoker as an alternative to cigarette. Electronic cigarette are much cleaner and contain the nicotine with the tobacco. The liquid nicotine is vaporized by a heater and inhaled by the user (Cassidy). Some use this as a way quit smoking. This study will find out if quitting smoking is dependent on electronic cigarette.

2. What is the null hypothesis? What is the research hypothesis?

The null hypothesis here is that quitting smoking is not dependent on the use of electronic cigarette.

The research hypothesis here is that quitting smoking is dependent on the use of electronic cigarette.

3. Participants/Sampling Method: Describe your sampling method. What is your sample size? Who is your population of interest? How representative is the sample of the population under study?

For this study, I looked for smokers around my city to participate. I will ask 100 smokers that have smoked continuously for the past 5 years to qualify, 50 smokers will be given the electronic cigarette, and 50 smokers will not be given the electronic cigarette. They are given a 1 month period to try to quit smoking and use their given treatmen to satisfy their craving. The 50 smokers will use nothing but the electronic cigarettes to satisfy their smoking need. The other non-electronic cigarette smoker can use nothing but cigarettes to satisfy their smoking need. After the 1 month period, the participants will stop all use of nicotine related use. They will be monitored for 1 month to see if they start to smoke again. Any smokers that use any cigarette, electronic cigarette, or nicotine related product during that time period will count as 1 smoker after treatment.

Data Analysis: Describe the statistical analysis. What are the requirements of the chi square? (HINT: This should be a chi square test of independence). What is your IV? What is your DV? What level of measurement are your IV and DV? What is your alpha level?

This statistical analysis will have a 2 x 2 contingency table. The 2 rows will be categorized to “smoking cigarette,” and “smoking electronic cigarette.” The 2 columns will be categorized to “quit smoking,” and “didn’t quit smoking.” This will be a chi square test with alpha value of 0.05. The independent variables here is the electronic cigarette and cigarette. The dependent variables is if they did or did not quit smoking. The measure for IV is a count of participant that use electronic or tobacco cigarette. The measure of DV is the count of participant that quit smoking or did not quit smoking.

4. Results & Discussion: Did you reject the null hypothesis? What information did you use to lead you to your conclusion? Was your observed p value greater than or less than your alpha? How do you interpret the findings? NOTE: You can just make up numbers, but include your made-up p value.

From the chi square analysis, the chi square calculated value is 7.572. The critical chi square value is 3.841. The p value is 0.005 which is less than the alpha value of 0.05. Additionally, the calculated chi square value is larger than the critical chi square value. This warrant sufficient evidence to reject the null hypothesis and evidence for the research hypothesis that quitting smoking is dependent on the use of electronic cigarette. Keep in mind these are made up numbers.

References

Cassidy, S. (undefined). 10 Little-known Facts About E-cigarettes. In Discovery Fit & Health. Retrieved October 17, 2013, from http://health.howstuffworks.com/wellness/smoking-cessation/10-facts-about-e-cigarettes.htm.

Triola, M. (2011). Elementary statistics. (11 ed., Vol. 10). Addison – Wesley Longman, Inc.

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References

BoÌhning, D., & Holling, H. (1998). On minimizing chi-square under the hypothesis of independence for a two-way contingency table. OsnabruÌˆck [Germany: Fachbereich Psychologie, UniversitaÌˆt OsnabruÌˆck.

Siegel, J. G. (2005). Schaum’s quick guide to business decision-making tools for business, finance, and accounting students. New York, N.Y.: McGraw-Hill.