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Unit 3: Language of Research

2. Relational: When a study is designed to look at the relationships between two or more Notes
variables.

Example: A public opinion poll that compares what proportion of males and females
say they would vote for a Democratic or a Republican candidate in the next presidential election
is essentially studying the relationship between gender and voting preference.
3. Causal: When a study is designed to determine whether one or more variables (e.g., a
program or treatment variable) causes or affects one or more outcome variables.

Example: If we did a public opinion poll to try to determine whether a recent political
advertising campaign changed voter preferences, we would essentially be studying whether the
campaign (cause) changed the proportion of voters who would vote Democratic or Republican
(effect).

The three question types can be viewed as cumulative. That is, a relational study assumes that
you can first describe (by measuring or observing) each of the variables you are trying to relate.
And, a causal study assumes that you can describe both the cause and effect variables and that
you can show that they are related to each other. Causal studies are probably the most demanding
of the three.

3.2.2 Time in Research

Time is an important element of any research design; let us discuss one of the most fundamental
distinctions in research design nomenclature:

Cross-sectional versus Longitudinal Studies

A cross-sectional study is one that takes place at a single point in time. In effect, we are taking a
‘slice’ or cross-section of whatever it is we’re observing or measuring. A longitudinal study is
one that takes place over time – we have at least two (and often more) waves of measurement in
a longitudinal design.
A further distinction is made between two types of longitudinal designs: Repeated measures
and time series.
There is no universally agreed upon rule for distinguishing these two terms, but in general, if
you have two or a few waves of measurement, you are using a repeated measures design. If you
have many waves of measurement over time, you have a time series. How many is ‘many’?
Usually, we wouldn’t use the term time series unless we had at least twenty waves of measurement,
and often far more. Sometimes the way we distinguish these is with the analysis methods we
would use. Time series analysis requires that you have at least twenty or so observations.
Repeated measures analyses (like repeated measures ANOVA) aren’t often used with as many as
twenty waves of measurement.

3.2.3 Types of Relationships

A relationship refers to the correspondence between two variables. When we talk about types of
relationships, we can mean that in at least two ways: the nature of the relationship or the pattern
of it.

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