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 STRUCTURE 1 Data Structure Layal Mansour Walden University DATA STRUCTURE 2 Data Structure Data structure encompasses a specialized format used in organizing and storing statistical information. In most cases, data structures include various aspects of data such as the use of an array, files, tables, and recordings among other types of data structure. Researcher uses data structures to organize data in a bid to align the information to a specified purpose, enhancing the process of accessing and working with the information in the most appropriate ways. Selected Continuous Variable In statistics, a continuous attribute is a variable that reveals infinite possible values, including zero, decimals as well as fractions, measured as ratio scales. Thus, the selected continuous variable is electricity monthly cost in Puerto Rico/PR housing dataset because the variable provides the amount incurred per a housing unit recorded in US dollars. Converting the Variable into a Categorical variable The conversion of the continuous variable into a categorical variable entailed grouping the recorded electricity amounts into different categories with equal intervals. This involved a calculation of the recorded minimum and maximum amounts and grouping the data into equal intervals of $99 between the lower and the upper limits of each categorical group. This allows the ability to categorize the data into six groups. Descriptive Statistics for the Continuous Variable The table below reveals the descriptive statistics for the selected continuous variable electricity monthly cost. DATA STRUCTURE 3 Table 1: The table illustrates the descriptive statistics for electricity amount Table 1 Descriptives Statistic Mean 97.41 95% Confidence Interval for Lower Bound 95.97 Mean Upper Bound 98.85 5% Trimmed Mean 87.88 Median 70.00 Variance Electricity Amount Std. Error .734 6934.917 Std. Deviation 83.276 Minimum 1 Maximum 540 Range 539 Interquartile Range 80 Skewness 2.208 .022 Kurtosis 6.701 .043 Table 1 above reveals the descriptive statistics for the variable electricity amount. From the table, the average electricity amount recorded per unit house in Puerto Rico is $ 97.41 with a standard deviation of $ 83.28 dollars. Besides, the minimum amount of electricity used was 1 dollar while the maximum amount paid for electricity per housing unit is $ 540 dollars. Frequency for the New Categorical Variable The table below depicts the frequencies for the new converted categorical variable for electricity amount per unit housing in Puerto Rico/PR dataset. DATA STRUCTURE 4 Table 2: The table illustrates the Frequencies for the new electricity amount variable. Table 2 Electricity Amount Frequency Valid Percent Valid Percent Cumulative Percent $1- $100 8871 61.6 61.6 61.6 $101-$200 2932 20.4 20.4 81.9 $201-$300 689 4.8 4.8 86.7 $301- $ 400 216 1.5 1.5 88.2 $ 401- $ 500 72 .5 .5 88.7 1627 11.3 11.3 100.0 14407 100.0 100.0 $ 501 and above Total Table 2 above illustrates the frequencies for the new electricity amount variable paid per housing unit in Puerto Rico/PR dataset. From the frequency table, the highest number of housing unit paid electricity amount between one US dollar and 100 US dollars depicting 8871 units at 61.6 percent of the sampled population. Subsequently, the least number of units paid electricity amount between 401 US dollars and 500 US dollars constituting of 72 housing units. Nonetheless, 1627 housing units paid electricity amount above 501 US dollars based on the frequency table for the new categorical variable.
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