Scholar-Practitioner Project: Final Report

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Scholar-Practitioner Project: Final Report

Having studied and evaluated different challenges and opportunities associated with the use of secondary data, and having learned how to evaluate, analyze, and present information generated from the analysis of secondary data, now is your opportunity to synthesize this information and present your work.

This week, you will compile all of the Assignments and your Instructor’s recommended changes into a Final Scholar-Practitioner Project Report.

To prepare:

  • Review the Ostchega, et al. (2012) article located in this week’s Learning Resources as an example for formatting your final report.

Your Final Scholar-Practitioner Project Report should be 20–25 pages (excluding title page and references).

Make sure to include the following in your report:

  • Title page
  • Research topic and background
  • Research question
  • Secondary data set used
  • How data collected in this data set
  • Validity and reliability of the data set
  • Data analysis plan (variables and statistical procedures)
  • Data dictionary and data table
  • Results and interpretation (descriptive and inferential tables)
    • Descriptive analysis tables or graphs with interpretation
    • Inferential analysis tables or graphs with interpretation
      • Limitations of study
      • Two recommendations for future research
  • Implications for social change
  • References

Support your paper with additional scholarly resources. Use APA formatting for your paper and to cite your resources.

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Running head: PARAMETERS 1 Parameters Layal Mansour Walden University PARAMETERS 2 Parameters Research topic Assessment of an individual weight in respect to age, class and status of work as a public health initiative towards understanding the risk of health complications related to body weight in a bid to promote healthy living. Research Questions What is the association between an individual weight with respect to age, class of work and work status? Study Population In carrying out a research, a study population entails a collection of individual forming the primary focus for a scientific question and posits various binding characteristics (Taylor, Bogdan, & DeVault, 2015). Since, the study entails a public health issue affecting the entire population; incorporate the entire population because the primary objective for carrying out the public health-related research is to understand the risk of health complications related to individual weight based on age, class of work and the status of work. Study Variables There are a number of variables involved in the study aimed at providing a clear understanding on the public health issue related in body weight. Dependent element: This a variable in a research that depends on other attributes often denoting the point on a researcher’s interest. In our study, the dependent variable is the person’s weight. Independents variable: This comprises of study elements that influence the dependent attribute in a research. In the study, a person's age, class, and status of work are the independent element believed to have a significant influence on the dependent element body weight. PARAMETERS 3 Rationale for Selecting the Data variables in relation to Research Question The research question aims at evaluating the relationship between an individual body weight based on a person's age, class and status of work. This would assist in developing public health insights related to risks of health complication related to the weight of a person in a bid to establish a basis for effective measures that would assist in educating the public regarding the benefit of weight management based on age, class, and status of work. In public health body, weight has been associated with increasing cases of type 2 diabetes, cardiovascular ailment, hypertension, and strokes among other health complication. This has resulted in various public health department carrying out assessments aimed at providing informed solutions towards minimizing the increasing health complications. As a result, the selected variables are highly suitable for carrying out the assessment and provide educative insight on patterns of weight across the three independent elements. Besides, the variables are suitable in the provision of vital association of weight with age, class, and status of work in a bid to provide ways that would assist in educating the population about the risk of excess weight on their health. Sample Selection A sample in research encompasses a group of individuals selected randomly from the entire population that posits the same characteristics as the study question (Flick, 2015). Besides, a sample should representative to heighten the ability to generalize the finding from the study sample to the entire population. The study will utilize available information collected for a different reason referred to as secondary data. This is because the study area is vast and requires many resources to accomplish the objective of the study related to the public health initiative. Specifically, the study will utilize a number of variables derived from dataset collected in Puerto PARAMETERS 4 Rico PR in the United States, a data collected in a personal record census by the American government. Appropriate Sample Size at 95 percent Confidence The expected marginal error for the study is 0.05 with a standard deviation of 3 individual. Therefore, Sample size n =1.962*sd2/ E2 Sample size n= 1.962*32/0.052 Appropriate sample size n = (3.8416*9)/0.0025 Sample size n = 13,830 Power Analysis Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average Source Type III Sum of df Mean Square F Sig. Squares Intercept 34112778.607 1 34112778.607 Error 12010575.726 11185 1073.811 31767.955 Noncent. Observed Parameter Powera .000 31767.955 a. Computed using alpha = .05 Based on the table above, the observed power is 1.000 implying that we have a very high chance that the analysis will reveal statistical significance at 95 percent confidence level. 1.000 PARAMETERS 5 Reference List Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research project. Sage. Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. John Wiley & Sons. Running head: DATA ANALYSIS PLAN Data Analysis Plan Layal Mansour Walden University 1 DATA ANALYSIS PLAN 2 Data Analysis Plan In performing a research work, data analysis plan is vital because it enlightens a reader in the form of analysis that would explore a hypothesis in the course of answering a research question. Phillips and Stawarski (2008) notes that data analysis plan outlines various aspect of a data evaluation in a research, which includes the data cleaning, analysis assumptions and any form of transformation that a researcher performs. Research Questions The study will examine the following research questions related to housing in Puerto Rico/ PR. o Is the number of bedrooms per housing unit dependent on the lot size? o Is there a relationship between the house heating fuel and the number of bedrooms per housing unit? Selected Variables The variable used to answer the research questions includes the bedrooms in a housing unit, lot size, and house heating fuel. Variable Description Bedrooms: This element describes the number of bedrooms a housing unit has categorized into five attributes, which are no bedroom, one bedroom, two bedrooms, three bedrooms, four bedrooms, five or more bedrooms, DATA ANALYSIS PLAN 3 Lot size: The variable entails the size of land a house lies grouped as a house on less than oneacre land, house on one to less than 10 acres of land as well as a house on ten or more acres of land. House heating fuel: The variable encompasses the type fuel used in heating in a housing unit classified in various fuel attributes, such as utility gas, bottled, tank, or LP Gas, Electricity, Fuel oil, kerosene, etc., Coal or coke, wood, solar energy, other fuel or no fuel used in heating. Types of variables The selected variables for the analysis process depict both independent as well as dependent characteristics. In the first research question that examines whether the number of bedrooms per housing unit dependent on the lot size, the attribute bedrooms will be the dependent variable while the lot size will be the independent attribute. Moreover, in the second research question, the number of bedrooms will be the independent variable and the housing heating fuel will be the dependent element, implying that the type of house heating fuel unit depends on the number of bedrooms per housing unit. Notably, a dependent variable denotes the point of interest for a research often influenced by the independent attribute. Level of Measurement Different levels of measurement describe different statistical variables, which include the ordinal, interval, nominal and ratio scale. In this case, the selected elements depict nominal scales used in assigning events into discrete categories. Statistical Analysis Plan Data analysis entails the process of converting raw data into simple and easy to read statistical information that one can interpret and make meaningful and significant conclusions (Clinical tools, Inc., n.d). Moreover, statistical evaluation assists in making imperative DATA ANALYSIS PLAN recommendation used in effecting necessary changes to enhance the attainment of the objective for carrying out a research. 4 DATA ANALYSIS PLAN 5 Descriptive Method Cooper and Schindler note that researchers use descriptive statistics to describe features of information utilized in a study (2014). Descriptive methods provide summaries about a selected sample often giving the measure of central tendency, dispersion of the sample data among other statistics such as the frequencies depending on the level of measurement. As a result, since the selected data depict a nominal level of measurement, the descriptive method will include frequencies and percentages of the attributes on a selected variable. Analytic Methods for Answering the Research Questions Statistical Test The research will use chi-square independence test in the process of examining the hypothetical statement aimed at answering the research questions. Field (2013) defines Chisquare independence test as a statistical test utilized in the determination of whether there exists a significant association between two nominal attributes. Reason why the Test is Appropriate The study assumes that the variables are dependent on one other, for instance, the number of bedrooms dependent on the lot size while house heating fuel depending on the number of bedrooms per a housing unit. This makes the selected statistical test the most appropriate in answering the research questions. Besides, a chi-square test for independence is the most appropriate to test the variable because the selected data satisfy the categorical level of measurement, which is a condition necessary to carry out the statistical test. How the Statistical Test help in Answering the Research Question The results from the statistical test will assist in testing hypothetical statement formulated from the research question with an aim to reject the null hypothesis at α=0.05. In case the DATA ANALYSIS PLAN 6 findings lead to rejecting the null hypothesis, this will imply that the test accepts the alternative hypothesis. Accepting the alternative hypothesis depict that the research questions are true. Method for Presenting the Findings The statistical findings both the descriptive and inferential results will be presented using standalone tables and histogram. Notably, standalone implies that the tables and histogram will offer all necessary information, such that an audience of the research would easily read and understand the presented content without necessary reviewing the provided inference. DATA ANALYSIS PLAN 7 Reference List Clinical tools, Inc. (n.d). Guidelines for Responsible Data Management in Scientific Research. Cooper, D. R., & Schindler, P. S. (2014). Business research methods (Vol. 12). New York: McGraw-Hill Irwin. Field, A. (2013). Discovering statistics using IBM SPSS statistics. sage. Phillips, P. P., & Stawarski, C. A. (2008). Data Collection : Planning for and Collecting All Types of Data. San Francisco: Pfeiffer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=218523&site=ehostlive Running head: DATA MANIPULATION Manipulating the Dataset in SPSS Layal Mansour Walden University 1 DATA MANIPULATION 2 Manipulating the Dataset in SPSS Manipulating data involve the process of converting available data into a more organized and easy to understand in a bid to make informed decisions. As a result, the end-results from a data manipulation is displaying of information to the users, in a meaningful and usable way. Research Questions The research questions in the study are o Is the number of bedrooms per housing unit dependent on the size of land a house occupies? o Is there a relationship between the fuel used in heating the house and the number of bedrooms per housing unit? Null Hypothesis o The number of bedrooms per housing unit is not dependent on the size of the land the house occupies. o There is no association between the fuel used in heating a house and the number of bedrooms per a housing unit. Variables Definition and Categories Bedrooms in a housing unit, lot size, and house heating fuel are the variable that will answer the defined research questions. The variable bedrooms contain a number of categories included no bedroom, one bedroom, two bedrooms, three bedrooms, and four bedrooms, five or more bedrooms. Similarly, the element lot size has a number of categories, which include a house on less than one-acre land, house on one to less than 10 acres of land as well as a house on ten or more acres of land. House heating fuel as well contain elements such as utility gas, DATA MANIPULATION 3 bottled, tank, or LP Gas, Electricity, Fuel oil, kerosene, etc., Coal or coke, wood, solar energy, other fuel or no fuel used in heating. Data Conversion from Continuous to Categorical The selected elements for the analysis are categorical in nature, revealing a nominal level of measurement. Therefore, none of the elements requires converting from continuous to a categorical variable. Exploration of the Categorical Variables Results The data exploration entails both the descriptive and analytical analysis in SPSS and presentation of the findings in standalone tables for easier interpretation of the results. Descriptive Statistics 1. Lot size The table below illustrates the frequencies for the variable Lot size involving the number of acres. Table 1 Lot size Frequency N/A (not a one-family house or mobile home) Valid Percent Cumulative Percent 2207 15.3 15.3 15.3 11298 78.4 78.4 93.7 House on one to less than ten acres 770 5.3 5.3 99.1 House on ten or more acres 132 .9 .9 100.0 14407 100.0 100.0 House on less than one acre Valid Percent Total Inference The frequency table 1 above reveals various the number and percentage for each category in the variable lot size. From frequency table, 15 % of the sampled houses were mobile home/ not a one family house, while 78.4% entailed houses built on less than one acre of land. DATA MANIPULATION 4 Correspondingly, houses on one to less than ten acres and house on ten or more acres depicts 5.3% and 0.9% respectively. 2. Bedrooms The table below reveals the frequencies for the variable bedroom, indicating the number of bedrooms per unit house. Table 2 Bedrooms Frequency Percent Valid Valid Percent Cumulative Percent No bedrooms 392 2.7 2.7 2.7 1 Bedroom 884 6.1 6.1 8.9 2 Bedrooms 2972 20.6 20.6 29.5 3 Bedrooms 7676 53.3 53.3 82.8 4 Bedrooms 2103 14.6 14.6 97.4 380 2.6 2.6 100.0 14407 100.0 100.0 5 or more Bedrooms Total Inference Table 2 reveals the frequencies for the number of bedrooms per a unit house based on the house record dataset collected from Puerto Rico. From the table, 3 bedroomed houses revealed the highest percent of the sample houses at 53.3%, followed closely by 2 bedroom houses at 20.6 percent of the total houses in the house record dataset. Subsequently, houses with no bedrooms depicted 2.7 percent, while 5 or more bedrooms house revealed 2.6 % of the collected house record data in Puerto Rico/ PR. Nonetheless, 1 and 4 bedroomed houses in the dataset posited 6.1% and 14.6 % respectively from the total house record data in the dataset. 3. House heating fuel DATA MANIPULATION 5 The table below illustrates the frequencies for the variable house heating fuel indicating various forms of fuel energy used in heating the houses based on the sampled house record data from Puerto Rico/PR. Table 3 House heating fuel Frequency Percent N/A (Vacant) Utility gas “Bottled, tank, or LP gas” Electricity “Fuel oil, kerosene, etc.” Valid Coal or coke Wood Solar energy Other fuel No fuel used Total Valid Percent 1547 22 10.7 .2 10.7 .2 Cumulative Percent 10.7 10.9 357 2.5 2.5 13.4 2457 17.1 17.1 30.4 7 .0 .0 30.5 1 1 115 25 9875 14407 .0 .0 .8 .2 68.5 100.0 .0 .0 .8 .2 68.5 100.0 30.5 30.5 31.3 31.5 100.0 Inference From the frequency table 3 above, the largest number of houses did not use any form of fuel in heating the house at 68.5 % of the sample houses in Puerto Rico/ PR house record dataset. Nonetheless, 17.1 percent of the houses used electricity to heat the house, with 2.5 and 0.2% of the home using Bottled, tank, or LP gas and Utility gas respectively and the house heating fuel. Moreover, 0.8% and 0.2 of the houses used solar energy and other fuel respectively as house heating fuel while 10.7% of the houses in the database were vacant. Analytical Statistics The analytical process involved chi-square tests for independence aimed at answering the research questions. The table below illustrates the chi-square test between the number of bedrooms per house and the size of the land the house occupies. DATA MANIPULATION 6 Table 4 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 1725.580a 15 .000 Likelihood Ratio 1446.661 15 .000 Linear-by-Linear Association 749.667 1 N of Valid Cases 14407 a. 2 cells (8.3%) have expected count less than 5. The minimum expected count is 3.48. .000 Inference From table 4 above, the Pearson Chi-square value is 1725.580 at 15 degrees of freedom. Besides, the p-value is 0.000 less than α= 0.05, implying that we reject the null hypothesis. Therefore, we are 95 % confident that the number of bedrooms per housing unit dependents on the size of the land the house occupies. Moreover, the table below illustrates the chi-square test between the fuel used in heating the house and the number of bedrooms per housing unit. Table 5 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 381.670a 45 .000 Likelihood Ratio 354.759 45 .000 Linear-by-Linear Association 175.228 1 .000 N of Valid Cases 14407 a. 29 cells (48.3%) have expected count less than 5. The minimum expected count is .03. Inference From the chi-square table 5 above, the Pearson Chi-square value is 381.670 with 45 degrees of freedom. The p-value from the table is 0.000 less than α= 0.05 depicting that we reject DATA MANIPULATION the null hypothesis. Thus, we are 95 percent confident that the fuel used in heating the house is associated with the number of bedrooms in a house. 7 Running head: INTERPRETING RESULTS Interpreting Results Layal Mansour Walden University 1 INTERPRETING RESULTS 2 Interpreting Results The resulting interpretation will justify the findings from statistical assessment aimed at addressing hypothetical statements developed from research questions in a bid to provide explanations for the research. Bryman states that a hypothesis is a supposition developed with minimal evidence, for further statistical investigation (2016). The interpretation of the result will include both descriptive and inferential statistics obtained from SPSS output. The research questions under investigation are Is the number of bedrooms per housing unit dependent on the size of land a house occupies? Is there a relationship between the fuel used in heating the house and the number of bedrooms per housing unit? Null Hypothesis o The number of bedrooms per housing unit is not dependent on the size of the land the house occupies. o There is no association between the fuel used in heating a house and the number of bedrooms per a housing unit. Interpretation of Descriptive Results Descriptive statistics describe the basic features revealed by data under investigation, through the provision of summaries about a selected sample (Bryman & Bell, 2015). In this case, the selected samples include three variables, the number of bedrooms, size of land and house heating fuel. Since the variables depict nominal levels of measurement, the descriptive statistic INTERPRETING RESULTS 3 for the elements encompasses the attributes frequencies mainly the counts as well as the percentage. Lot size The first variable under descriptive investigation is lot size depicting the size of land in acres that a sampled house covers. The table below reveals the frequencies for the element lot size. Table 1 Lot size Frequency N/A (not a one-family house or mobile home) Valid Percent Cumulative Percent 2207 15.3 15.3 15.3 11298 78.4 78.4 93.7 House on one to less than ten acres 770 5.3 5.3 99.1 House on ten or more acres 132 .9 .9 100.0 14407 100.0 100.0 House on less than one acre Valid Percent Total Interpretation From the table, it is evident that the largest proportion of the sampled houses covered less than one acre of land with 11,298 houses out of the total sampled house of 14407 house in Puerto Rico. This presented 78.4 percent of the total sampled houses in the region. Therefore, from a statistical point of view, we may conclude that the largest number of houses in Puerto Rico covered less than one acre of land with only a few houses occupying a larger space of more than one acre. However, a significant number of houses occupied more than 10 acres of lands totaling to132 houses out of the total sampled houses. Bedrooms The selected sample variable reveals the number of bedrooms each sampled house contained. The frequency table below indicates the number of bedrooms per unit house. INTERPRETING RESULTS 4 Table 2 Bedrooms Frequency Percent Valid Valid Percent Cumulative Percent No bedrooms 392 2.7 2.7 2.7 1 Bedroom 884 6.1 6.1 8.9 2 Bedrooms 2972 20.6 20.6 29.5 3 Bedrooms 7676 53.3 53.3 82.8 4 Bedrooms 2103 14.6 14.6 97.4 380 2.6 2.6 100.0 14407 100.0 100.0 5 or more Bedrooms Total Interpretation Form the table above, it is evident that a majority number of sample houses contained three bedrooms with 7676 houses out of the total the 14,407 sampled units. This is more than 50 % of the sampled data which indicated that there is a likelihood that majority of the houses built in Puerto Rico contain three bedrooms. However, the distribution of the number of bedrooms reveals a normal curve implies that the number of bedrooms contained in the house in Puerto Rico is normally distributed (see Appendix chart 1). 1. House heating fuel The variable house heating fuel indicates various forms of fuel energy used in heating the houses based on the sampled house record information from Puerto Rico/PR region. The table below depicts the frequencies for the variable house heating fuel. INTERPRETING RESULTS 5 Table 3 House heating fuel Frequency Percent N/A (Vacant) Utility gas “Bottled, tank, or LP gas” Electricity “Fuel oil, kerosene, etc.” Valid Coal or coke Wood Solar energy Other fuel No fuel used Total Valid Percent 1547 22 10.7 .2 10.7 .2 Cumulative Percent 10.7 10.9 357 2.5 2.5 13.4 2457 17.1 17.1 30.4 7 .0 .0 30.5 1 1 115 25 9875 14407 .0 .0 .8 .2 68.5 100.0 .0 .0 .8 .2 68.5 100.0 30.5 30.5 31.3 31.5 100.0 Interpretation From the frequency table, most of the houses did not utilize any form of fuel in heating totaling to 9875 houses out of the 14,407 houses sample from the Puerto Rico region. This indicates that the region has favorable climatic conditions that do not require heating the house to keep warm. However, some areas on higher altitudes experience cool weather requiring heating with electricity revealing the commonly used heating fuel in the area with 17.1 percent of the total sampled houses. Interpretation of the Inferential Statistics Sekaran and Bougie define inferential statistics are data analysis result obtained for statistical tests aimed at providing solutions to a question under scrutiny. Inferential assessments enhance the ability of a researcher to make conclusions that answers a research question using statistical evaluation (2016). The inferential statistics involved Chi-square tests aimed at rejecting the null hypotheses developed from the research question for further statistical investigation. INTERPRETING RESULTS 6 Research Question 1 Is the number of bedrooms per housing unit dependent on the size of land a house occupies? The table below illustrates the chi-square test between the number of bedrooms per house and the size of the land the house occupies. Table 4 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 1725.580a 15 .000 Likelihood Ratio 1446.661 15 .000 Linear-by-Linear Association 749.667 1 N of Valid Cases 14407 a. 2 cells (8.3%) have expected count less than 5. The minimum expected count is 3.48. .000 Interpretation From the chi-square table 4 above, we reject the null hypothesis because the p-value for Pearson Chi-square is less than 0.05 at 95% level of confidence. This implies that the number of bedroom per housing unit depends on the size of land a house occupies. Therefore, houses built on large tracts of land are likely to have a higher number of bedroom compared to houses occupies small portions of land say less than a one-acre piece in Puerto Rico. Research Question 2 Is there a relationship between the fuel used in heating the house and the number of bedrooms per housing unit? The table below depicts a chi-square test between the variables fuel used in heating the house and the number of bedrooms per housing unit. INTERPRETING RESULTS 7 Table 5 Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 381.670a 45 .000 Likelihood Ratio 354.759 45 .000 Linear-by-Linear Association 175.228 1 .000 N of Valid Cases 14407 a. 29 cells (48.3%) have expected count less than 5. The minimum expected count is .03. Interpretation The table reveals a p-value of 0.00 less than 0.05, implying that we reject the null hypothesis. This is an indicator that there is a relationship between the fuel used in heating the house and the number of bedrooms per housing unit. Therefore, we can deduce houses with higher the number of bedrooms could be using electricity as house heating fuel due to efficiency in the heating process. INTERPRETING RESULTS 8 Reference list Bryman, A. (2016). Social research methods. Oxford university press. Bryman, A., & Bell, E. (2015). Business research methods. Oxford University Press, USA. Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons. INTERPRETING RESULTS 9 Appendix Appendix Chart 1 Appendix Chart 2 INTERPRETING RESULTS Appendix Chart 3 10
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