Page 26 - CleanScience_Fall22

􏰀 􏰁􏰂􏰂􏰃􏰄􏰅􏰆􏰇 􏰉􏰊􏰋􏰌􏰍􏰎􏰅􏰇􏰏􏰉􏰆􏰋 􏰅􏰆􏰐 􏰍􏰌􏰄􏰉􏰍􏰇􏰋 􏰉􏰑 􏰐􏰏􏰍􏰌􏰂􏰇 experience about residential housing.
􏰀 􏰁ccupant health reports other than those 􏰒ith an o􏰑􏰓cial 􏰔edical diagnosis.
All four are considered subjective and anecdotal rather than objective measures of a physical parameter. How􏰕 ever, subjective reporting is still information and can result in additional knowledge about attributes of houses and the people who live in them. Information can be ranked and scored on a numerical scale from low to high for all three characteristics being explored, allowing an investigation of their interactions. The scores can then be used as evaluation criteria both for current and future research for comparisons of tra􏰕 ditional criteria with different house environments. Analysis and validation of house cleaning methods and frequencies could potentially be included.
Challenges of a Complex Database
With over 300 variables and 80,000 responses, analysis and synthesis of the data is a daunting task. As previously stated, the data is derived from three disparate but related categories: physical structure, how the occupants utili􏰖e their indoor environment, and their self􏰕reported health􏰕related symptoms. The combination of these three categories crosses traditional boundaries of analysis and reporting. Instead of the clearly differentiated categories allowing separate analysis of each, a multifaceted exploration which includes open􏰕ended relationships between and among the categories could provide an interesting future project. For example, does the structure affect the behavior of occupants􏰗 􏰘o the occupants impact the structure􏰗 And what is the role of experienced symptoms in relation to the structure and occupant behavior􏰗
Representative Associations
Detailed analysis of even primary relationships contained in a database of over a billion combinations of variables is dif􏰓cult. Traditional statistical analysis, however, can help point to certain possibilities for further exploration. 􏰙arious exploratory subsets of data have been analy􏰖ed by a range of statistical methods, which include:
􏰀 Descriptive
􏰀 􏰚eographic
􏰀 􏰛orrelation Analysis
􏰀 􏰜rincipal 􏰛omponent Analysis
􏰀 FactorAnalysis
􏰀 􏰛lusterAnalysis
􏰀 􏰛lassi􏰓cation 􏰜rocedures
􏰀 􏰝inear 􏰞egression
􏰀 􏰝ogistic 􏰞egression
􏰀 􏰟egative 􏰠inomial 􏰞egression
􏰀 􏰡tructural 􏰢quation 􏰣odeling
Following are a few of the types of associations and percentages explored in the data so far, along with mentions of implications for home occupants and the cleaning industry.
Figure 􏰤 shows a signi􏰓cant increase in the reported number of houses built starting shortly after World War II and continuing until about 􏰤􏰥80. 􏰠ut the si􏰖e of the houses decreased during that same time, as shown in Figure 2. This information is consistent with the historical record according to housing data from 􏰤􏰥􏰦􏰥.1 The responses to the questionnaire also indicate an increase in new houses with larger square footage after the housing crash of 2008􏰧200􏰥. While neither chart is de􏰓nitive because of the subjectivity of responses, both show trends consistent with general knowledge of the times.
Figure 2. Housing size per square footage per year according to the questionnaire responses.
Figure 1. Number of houses built per year according to the questionnaire responses.
26 | The Cleaning Industry Research Institute
FALL 2022