Behavioural Surveillance Surveys - The Wisdom of Whores

Recommended Methods of
Statistical Analysis
Statistical analyses of behavioral surveillance
data can be divided into two categories:
• analysis within one wave of data
• trend analysis over multiple waves
Within each of these categories, variables
can be looked at in several ways. (A variable
is simply an aspect of a person or their
behavior that can be measured or recorded,
for example, their age or their use of condoms.)
The following types of analyses are possible:
Univariate analysis - This involves the
analysis of the distribution of one variable only.
Most of the indicators defined for behavioral
surveillance purposes are calculated through
univariate analysis. They would include the
proportion of young men who have had sex
with more than one partner, during a given
time period, for example, or the proportion
of injecting drug users that shared equipment
last time they injected drugs. Confidence
intervals are calculated for these proportions
to indicate the precision of these estimates.
When multiple waves are analyzed, statistical
techniques are used to calculate whether
changes in the proportions could have occurred
by chance, or whether observed changes are
likely to reflect real changes.
Bivariate analysis - This analysis is
performed to determine whether one variable
influences the distribution of another. In these
analyses, variables are usually divided into two
categories, the independent or explanatory
variable and the dependent or outcome
variable. Bivariate analysis typically looks for
associations between an explanatory and an
outcome variable. For example, there might
be an association between a respondent’s age
(the explanatory variable) and their use of
condoms (the outcome variable). Statistical
tests in bivariate analysis determine whether
any observed difference reflect a true difference,
or may be due to chance.
Multivariate analysis - This analysis is
performed to look at the influence of more
than two variables on another variable since
relationships between variables are often
complex and interwoven. Multivariate
techniques can pin-point the individual effects
of several explanatory variables on an outcome
variable which may be related to each other.
If multi-stage cluster sampling is used,
weighting and cluster effects will need to be
considered. These are discussed in Chapter 5.
Analysis of one data wave
Univariate Analysis
Univariate analysis is the most basic - yet
often the most important - because it shows
the distribution of each variable, some of
which are key prevention indicators. In BSS,
univariate analysis consists primarily of
constructing indicators out of categorical
variables. A categorical variable is a nonnumerical
variable that is often defined in
categories (such as ethnic group) or in answer
to a yes/no question (such as consistent
condom use). Many behavioral surveillance
indicators consist of the percentage of
respondents falling into a certain category,
such as the category of people who used
condoms the last time they had sex with a
non-regular partner.
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C H A PTER 7 B EHAV I OR A L S U R V EI L L A NC E S U R V EY S