DATA ANALYSIS Data analysis helps discover and substantiate patterns and relationships, test our expectations, and draw inferences that make our research fruitful. Analysis is the search for patterns in data and for ideas that help explain why those patterns are there in the first place.

Statistics play a key role in achieving valid research results in terms of measurement, causal validity and generalizability. Some statistics are useful primarily to describe the results of measuring single variables. E.g. frequency distributions, measures of central tendency and dispersion.

Other statistics are useful primarily in achieving causal validity. E.g. cross tabulations, regression. It is possible also to estimate the degree of confidence that can be placed on generalizations from a sample to the population.

Social theory and results of prior research should guide our statistical choices, as they have guided the choice of other research methods.

UNIVARIATE ANALYSIS Simplest form of quantitative analysis – involves describing a case in terms of a single variable. E.g. gender – we would look at how many women and men there are. Graphs and frequency distributions are the two most popular approaches of demonstrating univariate analysis.

Three features of the shape are important in a univariate analysis: a) Central tendency – the most common value or the value around which cases tend to center. b) Variability—the extent to which cases are spread out through the distribution or clustered in just one location. c) Skewness—the extent to which cases are clustered more at one or the other end of the distribution of a quantitative variable.

NOTE that a variable’s level of measurement is the most important determinant of the appropriateness of particular statistics.