Cross Sectional Designs
Research Objective
Study relationships
among variables for
existing groups
Show direct cause &
effect
Explain outcomes
after the fact
Type of Design
Cross-Sectional
Longitudinal
True Experiment
Quasi-Experiment
Explanatory Case Study
Exploratory Case Study
Descriptive Cross-Sectionals
• Many cross-sectionals are purely descriptive of
the characteristics of a population, often of a
single population with no comparison group
– Don’t use one of these in this class; it is not
acceptable
– I will not address these designs further in this class;
deVaus’s comments are sufficient
Explanatory Cross-Sectionals
• Explanatory cross-sectionals identify the
relationships among several (sometimes many)
variables within one or more populations
• Most use two or more comparison groups,
although some use test how well two or more
theories explain the relationships within a single
group
• There are generally numerous predictor
variables based on the constructs in one or
more theories
• There may also be more than one outcome
variable
Distinctive Features
• Select a representative sample from a population
or populations
• Groups or populations are defined based on the
independent or predictor variables, not groups
we create through setting up an intervention or
treatment and comparison or control group
• We then measure the independent or predictor
and outcome or dependent variables
• Assess the strength of the relationships among
variables through qualitative or quantitative
analysis
Did you select one or more characteristics to define the
population(s) of interest?
Yes
No
Not an explanatory
cross-sectional
Did you select a (preferably statistically) representative
sample of each population of interest based on the
characteristics used to define the population(s)?
Yes
No
Not an explanatory
cross-sectional
Did you collect information each sample based on the
predictor and outcome variables of interest?
Yes
No
Not an explanatory
cross-sectional
Explanatory Cross-sectional Design
Simple Cross-Sectional with Two
or More Comparison Populations
• 2 or more existing comparison populations
• A single measurement at one point in time
– In the diagram P=population, RS=representative
sample, M=measurement of variables
Define P1
Define P2
Define P3
RS from P1
RS from P2
RS from P3
M1
M1
M1
Example of Simple Design
• Research question: How does time between
marriage and birth of first child affect marital
satisfaction of women?
Population & Comparison Groups
• Required Characteristics: Married women (not
divorced or separated; women who have been
married only once; and women who have been
married less than 10 years
– These are screening criteria and you use them to try
to control other factors that could affect marital
satisfaction that are not of interest (previous
marriages)
• Comparison populations/groups: P1 consists
of women who were married less than 2 years
when the first child was born; P2 is women who
were married 2 or more years when the first
child was born
Hypotheses
• Primary hypothesis: Marital satisfaction will be
lower for women married less than two years when
the first child was born (one-tailed).
• Other theory-based hypotheses: There would
probably be several of these, all based on
constructs in the theory of this form
– There will be a positive correlation between …….
• Non-theory-based hypotheses: These deal with
various characteristics that could affect the
relationship between time to birth of first child and
marital satisfaction, such as size of the woman’s
family, educational level, profession, financial
resources
Sample & Measure
• Select a statistically representative sample of
each population (or, given all the caveats about
that, as close as you can get)
• Measure for all independent or predictor and
dependent or outcome variables (theory-based
and other)
Analysis
• All analyses can be either statistical or
qualitative
• Test the primary hypothesis first. If there is NO
difference between groups for the outcome, you
can treat the population as a single population
and single sample. However, I recommend
continuing testing as two or more populations.
• Then test the other hypotheses.
• Most researchers want to create some sort of
statistical or qualitative model of the
relationships for the comparison groups.
Cross-Sectional with
Repeated Measures in Time
• Most cross-sectional designs have no time
component, but a time component can be added
with repeated measures
• In cross-sectionals, sampling occurs before each
measurement – unlike longitudinal designs the
participants at each measurement are different
people, a different sample, drawn from the
population(s)
Define P1
Define P2
Define P3
RS1 from P1
RS1 from P2
RS1 from P3
M1
M1
M1
RS2 from P1
RS2 from P2
RS2 from P3
M2
M2
M2
Multiple Cohort Designs
• Multiple cohorts sampled over time allow you to determine
how the relationships between variables changes over
time, the assumption being that such changes are due to
broad societal processes
• The cohorts are simply different age groups in many
cases
– E.g., Financial security over time for people born in 1960-69,
70-79, etc. In these cases, the cohorts are essentially the
theoretical populations. In this example, we might assume that
the financial turbulence since 2000 has affected these cohorts
differently.
P1 (60-69)
P2 (70-79)
P3 (80-89)
P4 (90-99)
RS from P1
RS from P2
RS from P3
RS from P4
M1
M1
M1
M1
Multiple Measures with Cohorts
• In other cases, researchers combine multiple cohorts with
multiple measures over time. In our example this would reveal
how financial security changes over time for people born in
the different decades. We might hypothesize that financial
security will decline for all populations over time and that the
decline will be greater for P1 than P2, P2 than P3, etc.
• Again, remember that we draw a new sample for each
measurement.
P1
P2
P3
P4
RS1 from P1
RS1 from P2
RS1 from P3
RS1 from P4
M1
M1
M1
M1
RS2 from P1
RS2 from P2
RS2 from P3
RS2 from P4
M2
M2
M2
M2
Cross-Sectional
Before & After Some Event
• These are almost always retrospective designs
and are subject to all the limitations of
retrospective designs, which include things like
people’s ability to remember past feelings and
actions, events coloring or changing how we
perceived things prior to the event.
• However, sometimes we get an opportunity that
we just cannot ignore and we use these.
• More commonly, people try to treat these as
some form of “quasi-experiment.” They are NOT
a quasi-experiment. The “event” was not a
planned or intentional intervention. NO intentional
poke = NOT an experiment.
Example
• An example might be why people did (or did not)
vote for a woman for president (Hilary Clinton). The
two populations would be those that voted for her (in
the primary) and those who did not.
– We could use several of the gender theories for this study
and I won’t lay all that out here.
• Assume we did measurement 1 right after the
election. We could add a second measurement now,
which would improve our study because we could
see if the reasons given for voting for her or not
remain stable (remember Gersten from last module;
his ideas apply here, too).
P1 (yes) RS from P1
P2 (no) RS from P2
M1
M1
RS2 from P1
RS2 from P2
M2
M2
Sampling
• Both statistical and theoretical generalization
depends almost entirely on adequacy of the
sample
• Usually (not always) need a statistically
representative sample with regard to the
characteristics of the population that we think
may affect the variables (independent and
dependent) under study or – given the
difficulty in getting a true statistically valid
sample, something close to it (review Bernard
and Wretman readings from module 6)
Describing the Sample
• Describe the sample fully in the results
section of your study – the reader must have
good information to decide if your sample is
adequate or not.
• DeVaus calls this “descriptive analysis,” but
it’s not really analysis at all, just description. I
have no idea why he uses this term. I have
never heard it before.
What to Include
• The norm is to include demographic data about the
sample to show whether it is or is not broadly
representative of the population(s) of interest with
regard to these characteristics.
• But focus on the characteristics of the sample that
may affect the outcomes of the study – that’s what
really matters in terms of the adequacy of the
sample.
• Review Gersten in particular (module 11). All of his
comments about procedures you can use to
establish whether two comparison groups are
equivalent in important ways can be used in crosssectionals. For example, you could use multiple
forms of data collection similar to multiple pre-tests.
Take Home Points
• Cross-sectionals are not a substitute for
experiments or any other design.
• They fail to meet all the requirements required to
demonstrate direct cause and effect (see
Shadish).
• If well designed, they may provide tentative
indications of causality.
• They allow the researcher to explore
relationships among several theoretically related
variables, which can add significantly to
explanatory power.
• Cross-sectionals depend on the use of comparison
groups to indicate causality. All one group designs
are basically descriptive in nature because there is
no way to know whether absence of the condition of
interest produces the same outcome as presence
of the condition without a comparison group.
• They depend heavily on a representative,
independent sample of each comparison population,
preferably a statistically representative sample in
many cases.
• However, they are over-utilized in much social
research, probably because they are relatively quick
and inexpensive – especially the single population
descriptive study.