## Archive for the ‘Essays’ Category

Visual Representation and Analysis Data

1. From the data in the table above, using the top 7 states for number of murders, construct a table showing the number of murders by each category of firearms. 2. From the data in the table above, using the top 7 states for number of murders, construct a pie chart representing the number of murders in each state committed with handguns; use percentages to make sure that the pie chart is correctly proportioned. 3. From the data in the table above, using the top 7 states for number of murders, construct a bar chart representing the number of murders in each state committed with handguns; use percentages to make sure that the bar chart is correctly proportioned. 4. From the data in the table above using the top 7 states for number of murders, construct an area chart representing the number of murders in each state committed with handguns; use actual numerical values and make sure the area chart is correctly proportioned. 5. Which of these three chart types do you think gives the most useful visual information about the data represented? 6. Is there another chart type that you think would better represent these data? Why or why not?

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The assignment: Complete factorial analysis of variance in Smart Alex’s Task #1 on p. 541, using the Fugazi.sav data set from the Field text. You can follow the steps outlined on pp. 520–525 as a guide. Report your findings in APA format according to the guidelines in the PASW Application Assignment Guidelines handout. The final document should be 2–3 pages long.

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(ANOVA) According to Field (2013) factorial analysis is being show to test the hypothesis whether one’s age influences his or her preference or likeness for particular music. Data on 90 participants (n = 90) grouped by age are analyzed using the statistical technique called Factorial Analysis of Variance (ANOVA). SPSS ver 21 is the statistical software used to performed the analysis. Underlying Assumptions of Statistical Test Factorial ANOVA is a statistical analysis technique or model, the use of which requires that the following assumptions are met: (a) the independent variable is nominal and that it has two or more levels; (b) the sample groups are independent; (c) the level of measurement of the dependent variable must be interval or ratio data, (d) the two or more groups from which the samples are collected are normally distributed; and (e) the groups have equal variance (StatSoft, n.d.; Laurette Education, Inc., n.d.). The data set being analyzed have met all of these assumptions. The level of measurement for the independent variables is norminal with two or more levels; the sample groups (grouped by age) are independent; the level of measurement of the dependent variable is interval or ratio data evident by the data values that range from -100 to 100; and the groups have equal variance. The equality of group variance was verified using the Levene’s Test of Equality which shows a Sig. level of .322, a value that indicates that there is no significant diference in variance given that the p-Value is (p < .05).

Research Question Is there a relationship between age and music preference? Hypotheses Null Hypothesis (H0): There is not a relationship between age and music preference. Alternative Hypothesis (HA): There is a relationship between age and music preference.

Variables Independent Variables (IV): There are two independent variables in this analysis – Age and music preference. Age has two levels – age above 40 and age below 40. In the dataset, participants that are above 40 years old are coded with the value of 1, while participants who are 40 years and younger are coded with the value of 2. Dependent Variables (IV): The dependent variable in this analysis is liking. Liking is a personal music rating scale that range from -100, which means the participant really hates the music, through 0, which means the participant is completely indifferent to the music, to +100, which means the participant loves the music very much (Field, 2013). Results As shown in Table 1, the descriptive statistics from this analysis reveals that young people (age < 40) gave high ratings to Fugazi music compared to their older (age > 40) counterparts. The mean rating given to Fugazi music by younger people is 66.2 with a standard deviation of 19.9, while the mean rating given to Fugazi by older people is -75.8 with a standard deviation of 14.3. The reverse is true for the ratings given by both groups to Garf Brooks music. The mean rating given to Garf Brooks music by younger people is -71.4 with a standard deviation of 23.7, while the mean rating given to Garf Brooks music by older people is 74.2 with a standard deviation of 22.2. Both age groups gave similar ratings to Abba music (older people: mean = 59.9, std = 19.9; younger people: mean = 64.1 , std =16.9). Table 1 Ratings by age group Music Age Group Mean (Average) Std Deviation Fugazi 40+ -75.86 14.37 0-40 66.2 19.9 Abba 40+ 59.93 19.98 0-40 64.13 16.99 Garf Brook 40+ 74.26 22.29 0-40 -71.46 23.17 Note: Std = Standard The answer showned in the error bar chart shown in Figure 1. The error bar chart, young people like Fugazi music and older people like Garf Brooks music. according to chart,both young and old people like Abba. Figure 1 Error Bar Chart

Note: CI = Confidence Interval

There are three factors being considered in interpreting the output of this analysis: the main effect of music on the ratings; the main effect of age on the ratings; and the interaction effect of music and age on the ratings. These factors are supported by the degree of freedom (df) and the F-ratios in Table 2 – Tests of Between-Subjects Effects table. Table 2 Test of Between-Subjects Effects

Source Type III Sum of Squares df Mean Square F Sig Corrected Model Intercept 39265.933 5 78530.987 202.639 .000 34339.800 1 34339.600 88.609 .000 Music Age 81864.067 2 40932.033 105.620 .000 .711 1 .711 .002 .966 Music + Age Error 310790.156 2 155395.078 400.977 .000 32553.467 84 387.541 Note: df = Degree of freedom, F = F-ratios, Sig = Significance level Table 2 shows that the relationship between type of music and preference is statistically significant ( F(2, 84) = 105.62, p < .05) which is less than the set Alpha (p = .05). It is safe to conclude that the main effect of music on the ratings is significant. Given an F-ratio of 105 and a degree of freedom (df) of 2, this output can be written as F(2, 84) = 105.62, p < .05. Age is not shown to have a statistically signficiant relationship to music preference (p=.966) which indicates that this null hypothesis should not be rejected. The interaction of music and age however is statistically significant (p

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Article 1: Evaluating a social marketing campaign for enhancing mothers initiation to HPV vaccination Reference

Cates, J. R., Shafer, A., Diehl, S. J., & Deal, A. M. (2011). Evaluating a County-Sponsored Social Marketing Campaign to Increase Mothers’ Initiation of HPV Vaccine for their Pre- teen Daughters in a Primarily Rural Area. Social Marketing Quarterly, 17, 4–26. doi:10.1080/15245004.2010.546943 The goal of the study was evaluating a social marketing campaign with an aim of increasing the mother’s initiation to human papillomavirus vaccination for their daughters and other women in the reproductive age(Cates, Shafer, Diehl, & Deal, 2011). Cervical cancer has been a major problem for girls and mothers’ resulting from HPV and in regards to this, the Human Papilloma Virus (HPV) vaccine has been developed to provide new opportunities in the fight against the disease (Cates, Shafer, Diehl, & Deal, 2011). However, despite its development, the acceptability and uptake at the community level has been very low. Routine vaccination against the human papillomavirus, the leading cause of cervical cancer is recommended for girls 10-12 years of age. Evaluating a social marketing campaign, which is the purpose of the study, is quite relevant in determining measures to undertake in enhancing HPV vaccine adoption (Cates, Shafer, Diehl, & Deal, 2011). The study took three months allowing enough time to collect and analyze information about mothers of girls between the age of 11 and 12 years as well as healthcare practitioners serving these pre-teens being the main target groups. The principles of social marketing used in the study were relevant and they included the principle of product that was the type of vaccine recommended, the price to include the perception of efficacy, cost and safety and place principle that is whether the vaccine was readily available. The study analyzed cross-sectional surveys of health care providers and mothers as well as human papillomavirus immunization rates in non-intervention versus intervention countries. From the study results, 82 percent of the respondents were aware of ongoing HPV vaccination campaigns and 94 percent of the countries used brochures to reach out these mothers. Within six months of the campaign, the rate of HPV vaccination rose by 2 percent in comparison to countries where there was no intervention undertaken. Therefore, the study provided evidence on the importance of using a social marketing campaign in increasing HPV vaccination adoption. Article 2: HPV vaccination and parental health beliefs Reference

Reiter, P. L., et al. (2009). Parents’ health beliefs and HPV vaccination of their adolescent daughters. Social Science & Medicine. doi:10.1016/j.socscimed.2009.05.024

Cervical cancer is an extremely preventable condition yet it remains one of the most troublesome and prevalent conditions. The disease is caused by persistent human papillomavirus infection. In this regards, the study was conducted with an aim of examining correlations of vaccination initiation by identifying modifiable correlations of HPV vaccination initiation among the adolescent girls in the community. The study also identified whether these correlates varied by the status group or race. A cross-sectional survey was conducted involving 889 respondents of the adolescent girls that were aged between 10 to 18 years in areas of higher cervical rate incidence. Information collected was analyzed with the use of logistic regression. From the study results, constructs of the health belief model were associated with the initiation of vaccination as well as the physician recommendations to get vaccinated, perceived potential harms and barriers of obtaining the vaccination. The study also identified that same parent beliefs were essential in correlating HPV vaccination initiation regardless of their status or racial group. These, beliefs provide well-defined targets for future interventions to be implemented in increasing HPV adoption and coverage in the communities (Reiter et al., 2009). Article 3: Understanding reasons for adopting or not receiving HPV vaccination Reference Watson-Jones, D., Baisley, K., Ponsiano, R., Lemme, F., Remes, P., Ross, D., Hayes, R. (2012). Human papillomavirus vaccination in tanzanian schoolgirls: Cluster-randomized There are a number of factors that influence human papillomavirus vaccination adoption. In regards to differences in uptake of the HPV vaccines, the study objective was determining reasons affecting the uptake of the vaccination. The study examined the characteristics of the non-receivers and receivers in an effort of identifying reasons for adopting either of the choices. A case-control study among the receivers and non-receivers was conducted within in a cluster randomized trial in 134 primary schools. This was important in ensuring the accuracy of the results and preventing any bias occurrence. The target group for the study was girls who had failed to receive the vaccination and those whose had been vaccinated together with their parents and guardians in both cases with one group being the control group. The target group was relevant to the research study, and both groups were enrolled to take part in the study in a ratio of 1:1. They were interviewed about cervical cancer, HPV knowledge and reasons as to why they never or received the vaccination. The conditional logistic method of regression was used in determining factors that were independently associated with not being immunized. From the study results, adult factors that were associated with non-compliance included old age, poverty, lack of education in regards to cervical cancer, HPV, and not knowing anyone suffering from cancer. Pupil factors associated with non-adoption included inadequate knowledge, non-positive opinion about vaccination and illiteracy. The reasons for refusing HPV vaccination included concerns about the associated side effects of the vaccine and infertility concerns. However, the respondents reported that they would accept vaccination if they had the relevant knowledge that HPV vaccination was beneficial. It was clear that sensitization messages are critical in enhancing vaccination acceptance from the study (Watson-Jones et al., 2012).

Reference Cates, J. R., Shafer, A., Diehl, S. J., & Deal, A. M. (2011). Evaluating a County-Sponsored Social Marketing Campaign to Increase Mothers’ Initiation of HPV Vaccine for their Pre- teen Daughters in a Primarily Rural Area. Social Marketing Quarterly, 17, 4–26. doi:10.1080/15245004.2010.546943 Reiter, P. L., et al. (2009). Parents’ health beliefs and HPV vaccination of their adolescent daughters. Social Science & Medicine. doi:10.1016/j.socscimed.2009.05.024 Watson-Jones, D., Baisley, K., Ponsiano, R., Lemme, F., Remes, P., Ross, D., Hayes, R. (2012). Human papillomavirus vaccination in tanzanian schoolgirls: Cluster-randomized trial comparing 2 vaccine-delivery strategies. Journal of Infectious Diseases, 206, 678–686. doi:10.1093/infdis/jis407

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Introduction- According Morrow A.J (2009), ANCOVA is an extension of analysis of variance in which additional variable is added called covariate to the equation. And covariate is added to statistically control for effect of the variables and that besides covariate increases the sensitivity of the statistical test. In smart Alex task the researchers want to see the effect of two different therapies ( stalking-cruel-to-be kind therapy and psychodynamic therapy) on stalking behavior. The independent variables are the therapy approaches and the dependent variable is the staking behavior after therapy (Posttest). The co variable is Pretest staking behavior.

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Week 6 Blueprint Multiple Regression – Be sure to answer each question 1-8. Don’t mix up assumptions #1 with assumption tests #2 and with results in #2. You run your assumption tests based on the assumptions. This allows you to think about how reliable and valid the results may be. Then when you run your analysis that is recorded in #7. To prepare for this Application: • Review Chapter 8 of the Field text for a description of the simple regression and an example of conducting a simple regression using PASW. • Review Chapter 5 from the APA manual, “Displaying Results.” • Review the media resources demonstrating the multiple regression. The assignment: Complete Smart Alex’s Task #4 on p. 355 to perform a multiple regression analysis using the Supermodel.sav dataset from the Field text. You can follow the steps outlined on pp. 316-320 as a guide. Create a master MS Word document (you should only be submitting ONE document each week). Label it in accordance with the convention described in your syllabus. Inside of this document, include the following information. 1. (5 pts) State the underlying assumptions for the statistical test. 2. (5 pts) State whether the assumptions have been met. If the assumptions were not met (either in actuality or hypothetically), state what alternatives you have available to you. This section will be the longest section of the paper. Especially with MR as we are running outliers. 3. (5 pts) State the null and alternative (research) hypotheses. 4. (5 pts) Copy your syntax file and paste it into your MS Word Document. 5. (5 pts) For your output file: Select all ? Copy all objects ? Paste into your MS word document. This will ensure that your output is in a form that your instructor can read. 6. (10 pts) Create a results table consistent with requirements from the APA style manual. 7. (10 pts) Report the results using correct APA format. a. For Multiple and Logistic Regression models, ensure that you include appropriate measures of model fit as well as the specific procedure used (e.g., Hierarchical, Enter, Stepwise, Forward, Backward). 8. (5 pts) Describe how you would compute the sample size to achieve 80% power, alpha = .05, and the effect size of .50. Does your analysis support the sample size of the data you ran? We are going to mix it up for this assignment. The IV’s are as follows: Salary per day, Age, Years worked as a model. The DV is beauty Also, you need to determine the type of entry method. The key to the type of entry method is the purpose of the regression study. What is the purpose of this study? Look at pages 321 – 324 and you can see which type of method we should use to answer this question. Hint, it is not Forced entry method.

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Week 6 Blueprint Multiple Regression – Be sure to answer each question 1-8. Don’t mix up assumptions #1 with assumption tests #2 and with results in #2. You run your assumption tests based on the assumptions. This allows you to think about how reliable and valid the results may be. Then when you run your analysis that is recorded in #7. To prepare for this Application: • Review Chapter 7 of the Field text for a description of the simple regression and an example of conducting a simple regression using PASW. • Review Chapter 5 from the APA manual, “Displaying Results.” • Review the media resources demonstrating the multiple regression.. The assignment: Complete Smart Alex’s Task #4 on p. 355 to perform a multiple regression analysis using the Supermodel.sav dataset from the Field text. You can follow the steps outlined on pp. 316-320 as a guide. Create a master MS Word document (you should only be submitting ONE document each week). Label it in accordance with the convention described in your syllabus. Inside of this document, include the following information. 1. (5 pts) State the underlying assumptions for the statistical test. 2. (5 pts) State whether the assumptions have been met. If the assumptions were not met (either in actuality or hypothetically), state what alternatives you have available to you. This section will be the longest section of the paper. Especially with MR as we are running outliers. 3. (5 pts) State the null and alternative (research) hypotheses. 4. (5 pts) Copy your syntax file and paste it into your MS Word Document. 5. (5 pts) For your output file: Select all ? Copy all objects ? Paste into your MS word document. This will ensure that your output is in a form that your instructor can read. 6. (10 pts) Create a results table consistent with requirements from the APA style manual. 7. (10 pts) Report the results using correct APA format. a. For Multiple and Logistic Regression models, ensure that you include appropriate measures of model fit as well as the specific procedure used (e.g., Hierarchical, Enter, Stepwise, Forward, Backward). 8. (5 pts) Describe how you would compute the sample size to achieve 80% power, alpha = .05, and the effect size of .50. Does your analysis support the sample size of the data you ran? We are going to mix it up for this assignment. The IV’s are as follows: Salary per day, Age, Years worked as a model. The DV is beauty Also, you need to determine the type of entry method. The key to the type of entry method is the purpose of the regression study. What is the purpose of this study? Look at pages 321 – 324 and you can see which type of method we should use to answer this question. Hint, it is not Forced entry method.

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This week, you have been introduced to several concepts of logistic regression, specifically the odds ratio. In this Application, you will report the odds ratio in a given test.

To prepare for this Application:

Review Chapter 19 of the Field text for a description of logistic regression and the odds ratio. Review the media resources demonstrating the odds ratio. The assignment:

Complete Smart Alex’s Task #5 on p. 812 of the Field text. Include only Burnout as the dependent variable and Coping Style and Stress from Teaching as the two independent variables. Report the odds ratio ONLY.

Use APA format according to the guidelines in the PASW Application Assignment Guidelines handout. The final document should be 2–3 pages long.

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Introduction

“Well, what are the odds of that happening?”

That’s a phrase you probably hear often in your everyday life. As you know, odds are an important statistical concept for everyone from Las Vegas high rollers to medical researchers. Odds are used commonly in health research (for example, the odds ratio might be used to describe the odds that one ethnic group will contract some disease compared to another).

This week, you will learn the foundations of the odds ratio and determine when it is appropriate for use in your field.

Reminder: Your literature review for your Final Project will be due at the end of Week 9.

Learning Outcomes By the end of this week, you will be able to (for linear multiple regression):

State underlying assumptions Determine whether assumptions have been met Propose alternatives if assumptions are not met State null and alternative hypotheses Analyze data using PASW Interpret and report the results with PASW, including effect size Describe sample size Report results in APA format

Odds Ratio

This week, you have been introduced to several concepts of logistic regression, specifically the odds ratio. In this Application, you will report the odds ratio in a given test.

To prepare for this Application:

Review Chapter 19 of the Field text for a description of logistic regression and the odds ratio. Review the media resources demonstrating the odds ratio. The assignment:

Complete Smart Alex’s Task #5 on p. 812 of the Field text. Include only Burnout as the dependent variable and Coping Style and Stress from Teaching as the two independent variables. Report the odds ratio ONLY.

Use APA format according to the guidelines in the PASW Application Assignment Guidelines handout. The final document should be 2–3 pages long.

Create a master MS Word document (you should only be submitting ONE document each week). Label it in accordance with the convention described in your syllabus. Inside of this document, include the following information. 1. (5 pts) State the underlying assumptions for the statistical test. 2. (5 pts) State whether the assumptions have been met. If the assumptions were not met (either in actuality or hypothetically), state what alternatives you have available to you. This section will be the longest section of the paper. Especially with MR as we are running outliers. 3. (5 pts) State the null and alternative (research) hypotheses. 4. (5 pts) Copy your syntax file and paste it into your MS Word Document. 5. (5 pts) For your output file: Select all ? Copy all objects ? Paste into your MS word document. This will ensure that your output is in a form that your instructor can read. 6. (10 pts) Create a results table consistent with requirements from the APA style manual. 7. (10 pts) Report the results using correct APA format. a. For Multiple and Logistic Regression models, ensure that you include appropriate measures of model fit as well as the specific procedure used (e.g., Hierarchical, Enter, Stepwise, Forward, Backward). 8. (5 pts) Describe how you would compute the sample size to achieve 80% power, alpha = .05, and the effect size of .50. Does your analysis support the sample size of the data you ran?

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For this Application, you will build upon the sampling strategy you described in your Discussion Assignment. You will compute the sample size and run a power analysis on your sampling strategy for your research proposal.

To prepare for this Application:

Review Chapter 8 in the Frankfort-Nachmias and Nachmias book.

Review the sampling strategy for your research proposal as you described in this week’s Discussion Assignment.

Review the Sheperis media in this week’s Learning Resources to learn how to run a power analysis using G*Power.

The assignment:

Compose a 1- to 3-page paper in which you do the following:

Describe the sampling strategy for your research proposal.

For each strategy that you did not choose, state why that one is not appropriate for your research questions, hypotheses, and variables.

Run a G*Power analysis to determine the appropriate sample size. http://www.gpower.hhu.de/

Support your work with references to the literature.

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