Data Collection Paper Res341
Data Collection Paper
Research and Evaluation I
April 19, 2010
Jack McNicholas
Data Collection Paper Team B is conducting research to examine variables that affect the price of homes. The problem statement used in our research is: what effect, if any, does proximity to the city have on the value of a home? To better answer this question, Team B will review literature related to the topic in an effort to provide context to the situation, and assess the sampling used in the research to determine if there is potential for bias based on the population and the size. Data collection methods are considered to determine the most appropriate approach and data is displayed in various formats to provide a visual review as well as an in-depth analysis. Additionally, Team B must address ethical considerations related to this research to ensure that any conclusions drawn do not compromise the validity of the conclusions that are drawn.
Summary Paragraph
During last week’s research, three testable outcomes were identified: the value will increase if the home is closer to the city, the value will decrease if the home is closer to the city, or there is no direct correlation. Team B hypothesizes that if a property is closer to the city, the value will be higher. It has been determined that the median, mean, minimum, and maximum values should be assessed and examined to determine if this hypothesis is accurate. Team B will be using the mean home prices in group one and two to determine if there is a significant difference in home prices for homes less than 15 miles from the city compared to those equal to or greater than 15 miles from the center of the city. Based on the possible testable outcomes, Team B will use the Null Hypothesis and Alternate hypothesis with the mean prices in group one denoted as µH1 and mean home prices in group two denoted as µH2. 1. Null Hypothesis: There is no significant difference in the mean Home Prices in group one and the mean
Research and Evaluation I
April 19, 2010
Jack McNicholas
Data Collection Paper Team B is conducting research to examine variables that affect the price of homes. The problem statement used in our research is: what effect, if any, does proximity to the city have on the value of a home? To better answer this question, Team B will review literature related to the topic in an effort to provide context to the situation, and assess the sampling used in the research to determine if there is potential for bias based on the population and the size. Data collection methods are considered to determine the most appropriate approach and data is displayed in various formats to provide a visual review as well as an in-depth analysis. Additionally, Team B must address ethical considerations related to this research to ensure that any conclusions drawn do not compromise the validity of the conclusions that are drawn.
Summary Paragraph
During last week’s research, three testable outcomes were identified: the value will increase if the home is closer to the city, the value will decrease if the home is closer to the city, or there is no direct correlation. Team B hypothesizes that if a property is closer to the city, the value will be higher. It has been determined that the median, mean, minimum, and maximum values should be assessed and examined to determine if this hypothesis is accurate. Team B will be using the mean home prices in group one and two to determine if there is a significant difference in home prices for homes less than 15 miles from the city compared to those equal to or greater than 15 miles from the center of the city. Based on the possible testable outcomes, Team B will use the Null Hypothesis and Alternate hypothesis with the mean prices in group one denoted as µH1 and mean home prices in group two denoted as µH2. 1. Null Hypothesis: There is no significant difference in the mean Home Prices in group one and the mean
References: Gelfand, A.E., Ecker, M.D., Knight, J.R., Sirmans, C.F., (2004). The Dynamics of Location in the Home Price. Journal of Real Estate Finance and Economics, 29:2, 149-166. Retrieved April 17, 2010 from: http://ideas.repec.org/a/kap/jrefec/v29y2004i2p149-166.html Lind, Marchal, and Wathen. (2008). Statistical techniques in business & economics (13th ed.). New York, NY: McGraw-Hill Mok, D. (2001, October). Sharing the risk of home-ownership: a portfolio approach. Urban Studies, 39(7), 1095-1112. Sekaran, U. (2003). Research methods for business: A skill-building approach (4th ed.). Hoboken, NJ: John Wiley & Sons, Inc Sirpal, R. (1994, Fall). Empirical modeling of the relative impacts of various sizes of shopping centers on the value of surrounding residential properties. The Journal of Real Estate Research. Zietz, J., Norman Zietz, E, Sirmans, G.S., (2007). Determinants of House Prices: A Quantile Regression Approach. Journal of Real Estate Finance and Economics, Retrieved April 17, 2010 from: http://www.springerlink.com/content/q58603744006v445/