PUBH8545 Manipulating the Dataset Scholar Practitoner Project

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Mathematics

PUBH8545

Description

Throughout this course, you have practiced various skills that will allow you to identify, procure, and manipulate biosurveillance and secondary data. As addressed in previous sections, public health information needs are constantly growing, and the statistical analysis of data is just one step in this process. Decisions based on this information would rely not only on the accuracy of your analysis but also on the organization of its presentation.

This week for your Scholar-Practitioner Project you will conduct descriptive and inferential analyses using your selected data set, your prepared database from Week 8, and SPSS.

To prepare:

  • Review this week’s Learning Resources

submit interpretation for your statistical analysis based on your selected data set, your prepared database, and SPSS. Mark sure to perform the following tasks for each of your research questions separately:

  • Provide interpretation for descriptive statistical analyses based on your SPSS output.
  • Summarize the numerical results with descriptive analysis tables or graphs, including your interpretation.
  • Provide interpretation of your inferential statistical analyses using SPSS outputs.
  • Summarize the numerical results with inferential analysis tables or graphs, including your interpretation.
  • Provide full answer and interpretation for each of your research question(s).
  • Follow APA guidelines.

Support your analysis with the Learning Resources and current literature. Use APA formatting for your paper and to cite your resources.

References

Kamin, L. F. (2010). Using a five-step procedure for inferential statistical analyses. The American Biology Teacher, 72(3), 186–188.

Marshall, G., & Jonker, L. (2010a). A concise guide to descriptive statistics. Synergy, 22–25

McHugh, M. L. (2003a). Descriptive statistics, part I: Level of measurement. Journal for Specialists in Pediatric Nursing, 8(1), 35–37.

Silva-Ayçaguer, L. C., Suárez-Gil, P., & Fernández-Somoano, A. (2010). The null hypothesis significance test in health sciences research (1995–2006): Statistical analysis and interpretation. BMC Medical Research Methodology, 10(1), 44.

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DATA MANIPULATION Scholar-Practitoner Project: Manipulating the Dataset PUBH 8545 April 21st, 2019 Manipulating the Dataset DATA MANIPULATION Manipulating data is a process that encompasses the conversion of data into a more organized and easier to understand information essential in making informed decisions. The results of a data manipulation involved the statistical display of information in standalone presentations enhancing making supported decisions. Research Questions The study questions are i. Are the numbers of bedrooms dependent on the size of land occupied by a housing unit? ii. Is house heating fuel and the number of bedrooms statistically associated? Hypotheses Null Hypothesis i. The number of bedrooms is not statistically dependent on the size of land occupied by the house. ii. House heating fuel is not statistically associated with the number of bedrooms in a housing unit. Variable Definitions and Categories The selected variables to answer the research question are a bedroom (BDS), house heating fuel (HFL) and lot size (ACR). The variable bedrooms contain several categories such as no bedroom, one bedroom, two bedrooms, three bedrooms, and four bedrooms, five or more bedrooms. In addition, lot size as well contain categories such as 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, while the variable house heating fuel contains elements 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. 2 DATA MANIPULATION Data Conversion from Continuous to Categorical The selected variables lot size, bedrooms, and house heating fuel are categorical elements depicting a nominal level measurement. As a result, there is no need to convert the elements into categorical variables. Results The data examination involves both descriptive and analytical analysis in SPSS and presented on stand-alone tables for easier understanding and interpreting of results. Descriptive Statistics According to Bell, Bryman, and Harley (2018), descriptive statistics provide the description and features of a variable, such as the frequencies in a categorical variable. The tables below illustrate the descriptive statistic for the selected variables in the scholar-practitioner project. Lot Size The table below reveals the frequencies for the variable lot size involving the number of acres. 3 DATA MANIPULATION Lot size Frequency N/A (GQ/not a one-family house or mobile home) House on less than one acre Valid House on one to less than ten acres House on ten or more acres Total Percent Valid Percent Cumulative Percent 1672 34.0 34.0 34.0 2849 58.0 58.0 92.1 360 7.3 7.3 99.4 30 .6 .6 100.0 4911 100.0 100.0 Table 1: The table illustrates the frequency table for the variable lot size (Data Source: Hawaii Housing Survey) Inference From the table above, the highest percentage of households occupied less than one acre at 58.0 percent followed closely by one family or mobile homes at 34.0%. Subsequently, houses on one to less than 10 acres of land in the dataset constitutes of 7.3% while the house on 10 and more acres of land revealed the 0.6 percent of the total household in the Hawaii dataset. Bedrooms The table below depicts the frequencies for the variable bedroom, indicating the number of bedrooms per unit house. Bedrooms Frequency Valid Percent Valid Percent Cumulative Percent No bedrooms 211 4.3 4.3 4.3 1 Bedroom 683 13.9 13.9 18.2 2 Bedrooms 1208 24.6 24.6 42.8 3 Bedrooms 1810 36.9 36.9 79.7 4 Bedrooms 688 14.0 14.0 93.7 5 or more bedrooms 311 6.3 6.3 100.0 4911 100.0 100.0 Total Table 2: The table illustrates the frequency table for the variable bedrooms (Data Source: Hawaii Housing Survey) Inference 4 DATA MANIPULATION From the table, the majority of the houses contained 3 bedrooms at 36.9 % followed closely by 2 bedroom houses at 24.6 percent. In addition, 4 bedroom and 1 bedroom houses constituted of 14.0% and 13.9% respectively of the total houses in Hawaii housing survey dataset. Nonetheless, only 6.3 % of the houses contained 5 and more bedrooms in the Hawaii household survey dataset. House Heating Fuel 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 Hawaii housing survey dataset. House Heating Fuel Frequency Valid Percent Cumulative Percent N/A (GQ/vacant) 452 9.2 9.2 9.2 Utility gas 151 3.1 3.1 12.3 Bottled, tank, or LP gas 113 2.3 2.3 14.6 1744 35.5 35.5 50.1 Solar energy 68 1.4 1.4 51.5 No fuel used 2372 48.3 48.3 99.8 11 .2 .2 100.0 4911 100.0 100.0 Electricity Valid Percent Fuel oil, kerosene, Wood, Other Fuel, etc Total Table 3: The table illustrates the frequency table for the variable house heating fuel (Data Source: Hawaii Housing Survey) Inference From the table above, most of the houses used no fuel for house heating at 48.3% while electricity depicted 35.5% of the fuel used in house heating. Utility gas, Bottled, tank, or LP gas, Solar energy and Fuel oil, kerosene, Wood, Other Fuel, etc. depicted 3.1 %, 2.3 %, 1.4%, and 0.2 %. 5 DATA MANIPULATION Analytical Statistics Since the selected variables for the scholar-practitioner project are categorical, the inferential statistic encompasses the chi-square test, in the process of answering the research questions. Notably, the inferential statistic provides information that assists in making new discoveries and developing new insights from a data thereby making supported decisions. The table below illustrates the Chi-Square between the variable size of land and the number of bedrooms consisted of a house. Chi-Square Tests Value df Asymp. Sig. (2-sided) Pearson Chi-Square 2098.632a 15 .000 Likelihood Ratio 2283.297 15 .000 994.459 1 .000 Linear-by-Linear Association N of Valid Cases 4911 a. 4 cells (16.7%) have expected count less than 5. The minimum expected count is 1.29. Table 4: The table illustrates a Chi-square between lot size and bedrooms (Data Source: Hawaii Housing Survey) Inference From the table above, the Pearson Chi-Square value is 2098.632 and a p-value = 0.000 less than α=0.05. This implies that there a statistical significance between the two variables, therefore, reject the null statement. As a result, we are 95% confidence that the size of land occupied by a house depends on the number of bedrooms in a housing unit. In addition, the table below illustrates the chi-square test between the house heating fuel used and the number of bedrooms per housing unit. Chi-Square Tests Value df Asymp. Sig. (2-sided) 6 DATA MANIPULATION Pearson Chi-Square 313.835a 30 .000 Likelihood Ratio 282.977 30 .000 30.938 1 .000 Linear-by-Linear Association N of Valid Cases 4911 a. 9 cells (21.4%) have expected count less than 5. The minimum expected count is .47. Table 5: The table illustrates the Chi-Square between bedrooms and house heating fuel (Data Source: Hawaii Housing Survey) Inference From the Chi-Square table above, the Pearson Chi-Square value is 313.835 and the pvalue 0.000 less than α=0.05. Similarly, this indicates the presence of a statistically significant association between the number of bedrooms and the house heating fuel used. Therefore, reject the null supposition stating that house heating fuel is not statistically associated with the number of bedrooms in a housing unit. Thus, we are 95% confident that house heating fuel used depends on the number of bedrooms in a house. Conclusion Based on the analytical analysis, we make a conclusion at 95% confidence that the size of land occupied by a house depends on the number of bedrooms in a housing unit. In addition, the analytical analysis reveals that there is a statistically significant association between the house heating fuel used and the number of bedrooms in a housing unit. 7 DATA MANIPULATION References Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press. Document: Walden University. Hawaii Data Dictionary (dataset file) Laureate Education (Producer). (2015a). Introduction to secondary data [Video file]. Baltimore, MD: Author. 8
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Explanation & Answer

Attached.

Running head: INTERPRETING RESULTS

Interpreting Results
Student’s Name
Institutional Affiliation

1

INTERPRETING RESULTS

2
Interpreting Results

Interpreting results entail the justification of statistical findings in the process of
answering research questions and addressing hypothetical suppositions in a statistical research.
Research questions are answerable inquiry regarding a specific issue under statistical
investigations while hypothetical statements are supposition developed from the research
questions with minimal information thereby open for further investigation (Bell, Bryman &
Harley, 2018). The result interpretation will include both the descriptive and inferential statistical
obtained from SPSS for the scholar-practitioner project.
Research Questions
The study questions are
i.

Is the number of bedrooms dependent on the size of land occupied by a housing unit?

ii.

Is house heating fuel and the number of bedrooms statistically associated?
Hypotheses

Null Hypothesis
i.

The number of bedrooms is not statistically dependent on the size of land occupied by the
house.

ii.

House heating fuel is not statistically associated with the number of bedrooms in a
housing unit.
Descriptive Analysis
According to Marshall and Jonker (2010), descriptive statistics elaborates the basic

features revealed by data in a statistical investigation. The descriptive statistics will describe the
selected variables size of land, the number of bedrooms and the house heating fuel. The variable
reveals a nominal level of measurement. McHugh and Villarruel (2003) note that descriptive

INTERPRETING RESULTS

3

statistic for nominal variables include descriptions of the frequencies for each attribute in a
variable depicts as counts and percentages.
Descriptive results
Lot Size
Lot size
Frequency
N/A (GQ/not a one-family house
or mobile home)
House on less than one acre
Valid

House on one to less than ten
acres
House on ten or more acres
Total

Percent

Valid Percent

Cumulative Percent

1672

34.0

34.0

34.0

2849

58.0

58.0

92.1

360

7.3

7.3

99.4

30

.6

.6

100.0

4911

1...


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