1. 1 …continued… Part III. Performing the Research 3 Initial Research 4 Research Approaches 5 Hypotheses 6 Data Collection 7 Data Analysis

2. 2 We analyze data to make something meaningful and useful out of it: specifically we attempt to generate knowledge and understanding that will answer the research objectives it may take several sequential steps to get the data into a form in which it is informative

3. 3 A dichotomy of analytical methods is qualitative vs. quantitative: qualitative: –where we look for possible correlations or cause-effect relationships between variables, and general principles –example qualitative methods: graphs plotting dependences between variables (such as assignment 2’s energy cost plotted against R-value); distribution plots showing variance and skew scatter plots showing clusters and sparse areas within the problem domain (such as assignment 2 plotting R- value against exterior surface area classifications of data to try to identify inherent structure or groupings (such as trying to order comments from users in an unstructured interview) –this is good for preliminary analysis where we are trying to get to understand a problem in its most general terms first.

4. 4 quantitative: –where we have a good idea of the relationships between the variables, but need now to measure extent; –examples of quantitative analysis includes: measures of distribution (mean, standard deviation, skew, etc…) correlations simulation outputs such as idle time or production rates; –good for applying the findings to future variations of the problem –often involves statistical methods

5. 5 Statistical methods (see example applications in the course text): –Non-parametric tests free of a distribution examples include: rank sum tests - used to test whether independent samples have been selected from the same distribution – eg: whether the cost of constructing office buildings is the same in Florida and Georgia. chi-square test – used to compare observed and expected frequencies of a variable over at least 3 categories – eg: whether there are differences in safety performance between three construction sites goodness of fit used to measure how well a set of data fit a given distribution - eg: whether a company’s salaries fit a Beta distribution.

6. 6 –Parametric tests assume that the distribution is known examples include: t test – used to determine if the mean of a sample is similar to the mean of the population ANOVA (analysis of variance) – used to test the significance of differences among more than two sample means

7. 7 –Regression and correlation used to establish whether a relationship exists between variables – they do not determine cause-effect

8. 8 –Times series Many of the data used by researchers concern variables that change over time Usually considered to have 4 component parts: Secular trends – seemingly non cyclical changes over the long term - eg: the cost of construction increases over time due to various inflationary factors Seasonal variation – short term cycles over a year, month, week, etc. – eg: productivity on construction sites is higher in summer as it is warmer Cyclical fluctuations – longer term cycles – eg: ups and downs in the national economy Random factors – disturbances due to random )or apparently random) factors – eg: productivity fluctuations due to inclement the weather, illness, etc.. There are many statistical tools that can be used here, including line fitting.

9. 9 –Index numbers strictly, these are a special case of time series, except that they are usually dimensionless, being measured relative to a value at a given point in time eg, cost of living index.