Healthy homes are essential in promoting wellness, i.e., proper mental and physical health, an aspect linked to homes being safer for occupants (Hernández & Swope, 2019; Pevalin et al., 2017). In society, being in good health depends on multiple factors, of which in this research, quality housing forms a significant factor. In this research, house prices or value is used as a measure of quality housing. Since inadequate or poorly designed houses are associated with an increase in health problems, e.g., physical hazards likely causing injuries, it becomes useful to examine factors that impact house prices. In this and can have harmful effects on childhood development
Research question:
What factors are associated with Housing Prices/Value?
Variables, Descriptions and Measurement Levels
Property value (price), this forms the study´s dependent variable. Notably, house prices depict the offered price for the house. From the 2005 Housing Hawaii dataset, property value has an ordinal measurement level, with housing values spread across different orders. For example, based on the Hawaii dataset, there is value allocation ranging from Less than $ 10000, $ 10000 - $ 14999, $ 15000 - $ 19999, to as much as $750000 - $999999, and finally over $1000000.
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No. of rooms, Insurance (Fire/hazard/flood insurance), Year built, Property taxes are the independent variables in this study. Of these, property taxes has an ordinal level of measurement, with taxes ranked across different categories, e.g., None, $ 1 - $ 49, $ 50 - $ 99, $ 100 - $ 149 to $9000 - $9999, and finally over $10000+. Secondly, no. of bedrooms has an ordinal level of measurement, i.e., the grouping ranges from no bedrooms, 1, 2, 3, 4 and finally 5 or more bedrooms.
Finally, as another independent variable, Year built (YBL) is also ordinal. The timeline is ordered comprising items like 2005 or later, from 2000 to 2004 to as far as 1940 to 1949, and finally 1939 or earlier.
Statistical Analysis Plan
Descriptive Methods for Describing Data
Descriptive statistics help in organizing as well as describe collected data collected, either from a respective sample or population (Frankfort-Nachmias, Leon-Guerrero & Davis, 2020). In this research, the planned descriptive statistics will first entail frequency measures, covering the specific frequencies and count in the dataset, e.g., total samples. Secondly will be measures of central tendency, which as explained by Wagner (2020), entails items like mode, mean, as well as the median, which will be vital in describing the sample dataset used in this project. Finally, the last descriptive method will encompass measures of variation/dispersion, some of which will comprise the range in describing the data items, variance, as well as the respective standard deviation (Wagner, 2020). In offering information using these descriptive items, it becomes simplified in making fact-based conclusions and supplementing subsequent inferential findings.
Analytic Methods: Statistical Test Planned for Answering Questions
The main analytical methods planned for use are Correlation and regression analysis.
First, correlation ranks as an essential technique in depicting if relationships exist, across chosen variables that are normally distributed, i.e., how the pre-chosen study variables are related (Wagner, 2020), and will be useful for adoption in this research. Using SPSS, the resulting outputs giving Pearson r, as the primary measure for correlation, will be applied to effectively gauge relationships among the examined variables to answer the study´s research objectives and questions.
Secondly, by explanation, regression analysis is useful in allowing the prediction of one variable, based on the information of other variables (Wagner, 2020). Hence, with this project focusing on key variables, i.e., independent and dependent, regression analysis will allow making conclusions on prediction, for future recommendations. After using SPSS, the resulting tables with outputs will act as the key in supporting statistical conclusions. First, with regression outputs giving an ANOVA table (Wagner, 2020), the presented t-values and p-values will be used on concluding if any evident relationship, for the examined variables is statistically significant.
Finally, since the study focuses on characteristics linked with housing prices, i.e., housing quality, summaries from the correlation and regression help support the planned objectives. Using the outcomes, on existing correlation and statistically significant, it will be possible in concluding whether the applied dataset helps answer the research question. From these relationships, either positive, negative or statistically significant, the information will help write the research and make health-based conclusions for supporting social change and health improvement.
Methods for Presenting Findings
As a health researcher, presenting findings is essential in giving readers opportunities to review and use the findings for supporting social change and other impacts. The primary ways to present the findings borrow mainly from what is depicted in Wagner (2020) and Frankfort-Nachmias, Leon-Guerrero and Davis (2020). First will be a textual presentation, mainly documenting the data and other findings from literature in fact-based paragraphs. With this format, there will be offering data, and presentation in a simplified readable format, incorporating pre-selected figures. Secondly, the presentation will use tables, covering specific statistical outputs copied from IBM-SPSS, with the tables containing rows and columns depicting useful data.
References
Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Hernández, D., & Swope, C. B. (2019). Housing as a Platform for Health and Equity: Evidence and Future Directions. American Journal of Public Health, 109(10), 1363–1366. https://doi-org.ezp.waldenulibrary.org/10.2105/AJPH.2019.305210
Pevalin, D. J., Reeves, A., Baker, E., & Bentley, R. (2017). The impact of persistent poor housing conditions on mental health: A longitudinal population-based study. Preventive Medicine, 105, 304–310. https://doi-org.ezp.waldenulibrary.org/10.1016/j.ypmed.2017.09.020
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
