Fundamentals of epidemiology - an evolving text - Are you looking ...

Algorithms - A procedure that uses a set of criteria according to specific rules or considerations,
e.g., major depressive disorder, "effective" contraception (I have not seen this term used to designate
a type of variable before, but I am not aware of any other term for this concept).
Preparatory work – Exploring the data
Try to get a "feel" for the data – inspect the distribution of each variable. Examine bivariate
scatterplots and cross classifications. Do the patterns make sense? Are they believable?
Observe shape – symmetry vs. skewness, discontinuities
Select summary statistics appropriate to the distribution and variable type (nominal, ordinal,
measurement)
Location - mean, median, percentage above a cut-point
Dispersion - standard deviation, quantiles
Look for relationships in data
Look within important subgroups
Note proportion of missing values
Preparatory work – Missing values
Missing data are a nuisance and can be a problem. For one, missing responses mean that the
denominators for many analyses differ, which can be confusing and tiresome to explain. Also,
analyses that involve multiple variables (e.g., coefficient alpha, crosstabulations, regression models)
generally exclude an entire observation if it is missing a value for any variable in the analysis (this
method is called listwise deletion). Thus, an analysis involving 10 variables, even if each has only
5% missing values, could result in excluding as much as 50% of the dataset (if there is no overlap
among the missing responses)! Moreover, unless data are missing completely at random (MCAR
– equivalent to a pattern of missing data that would result from deleting data values throughout the
dataset without any pattern or predilection whatever), then an analysis that makes no adjustment for
the missing data will be biased, because certain subgroups will be underrepresented in the available
data (a form of selection bias).
Imputation for missing values - optional topic
As theory, methods, and computing power have developed over the years, analytic methods
for handling missing data to minimize their detrimental effects have improved. These
methods seek to impute values for the missing item responses in ways that attempt to
increase statistical efficiency (by avoiding the loss of observations which have one or a few
missing values) and reduce bias. Earlier methods of imputation, now out of favor, include
replacing each missing value by the mean or median for that variable. Even though such
practices enable all observations to be used in regression analyses, these methods do not
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www.sph.unc.edu/courses/EPID 168, © Victor J. Schoenbach 14. Data analysis and interpretation – 459
rev. 11/8/1998, 10/26/1999, 12/26/1999