Key Concepts
Analysis plan – the steps you intend to carry out in order to make the comparisons needed for your evaluation
Basic comparisons – the overall comparisons you will make between pre- and post- program data and between program and comparison group data.
Code – a rule for converting a piece of information, for example the data you collect, into another form. For example, all women will be coded as “1,” or all women who attended all of the workshop sessions will be coded as “intensive participants”. You will decide on codes and all of the team members should apply the codes in the same way.
Coding – the process of applying your codes- organizing, categorizing, and giving meaning to quantitative and qualitative data.
Cross-tabulation – a way to analyze and present 2 or more variables and how they intersect, in a matrix table format. For example, male and female might be on one side of the table and 3 levels of program outcomes might be on the other. This way you can easily see subcategories of variables by gender and program outcome.
Data analysis – the process of examining data to summarize information about particular groups at particular points of time in ways that will allow you to determine if the changes that you are seeking actually occurred, what other changes your program might have produced and whether, how and why the program did and/or did not achieve its intended objectives.
Data processing – careful and systematic procedures to prepare, code, and organize your data for analysis.
Descriptive statistics are used to describe a collection of data in quantitative terms. Descriptive statistics quantitatively summarize a data set, rather than being used to support statements about the population that the data are thought to represent (which is what inductive statistics do).
Frequency – the number of occurrences or observations for a variable. For example, if the variable you are examining is ‘respondent has one child’, and you found that of the 135 teenage girls you interviewed who gave valid responses, 52 had one child, you are stating a frequency.
Inferential Statistics – using statistics so that you can infer, or learn, something about the larger population from the smaller one you have sampled and studied.
Intra-group comparisons – comparisons done by separating out different subgroups within your basic comparison groups such as boys who had highly gender equitable attitudes at the beginning of the program vs. boys who did not.
Mean – the value that is the average of all of the data combined. For example, 9.4 is the mean for this data set: 1,3,3,4,5,10,12,12,16,17,20. The mean can be skewed if there are too many very low or very high values.
Median – the value that is halfway through your data set, at which point an equal number of data points are above and below. For example, 10 is the median for this data set: 1,3,3,4,5,10,12,12,16,17,20
Mode – the most common answer in a data set. You can have more than one mode in each data set. For example, 3 and 12 are the modes for this data set: 1,3,3,4,5,10,12,12,16,17,20.
Statistical Significance – a finding is unlikely to have occurred by chance if it is statistically significant. You can only test for significance with the right amount (usually a sample of at least 200) of properly collected data. Statistical significance does not in and of itself make the finding meaningful for your evaluation.
Typology – a way of analyzing and understanding your findings, it is a type or characterization of a sub-group of experience, person, or outcome.
