Descriptive Methods
Ch. 6 and 7
Purpose of Descriptive Research
• Purely descriptive research describes the
characteristics or behaviors of a given
population in a systematic and accurate
fashion.
• Correlational research is often considered
descriptive research as well
– Describing the associations or relationships
between 2 or more variables
Types of Descriptive Research
• Survey is type of descriptive research that
may use either questionnaires or interviews
to collect data.
• Cross-sectional survey design: a single
group or respondents (a cross-section of
the population) is surveyed.
• Successive independent sample survey
design: two or more samples of
respondents answer that same questions at
different points in time.
– Validity depends on samples being comparable
1
Surveys
• Longitudinal or panel survey design: a
single group of respondents is questioned
more than once.
– Problem is drop-out – the sample no longer
the same as before.
– Makes it difficult to know whether changes in
data are due to real changes in behavior or
due to changes in the sample who provided
the data.
Internet Surveys
• http://psych.hanover.edu/research/exponn
et.html
• Advantages:
– Inexpensive – don’t require printing, postage,
or team of interviewers
– Don’t have to enter data – program does it
– May be able to recruit subjects it would be
difficult to find in person (late night, distant)
• Disadvantages:
– Little control over selecting sample: pulls
certain types of respondents
– Can’t be sure of the nature of the sample
Types of Descriptive Research
• Demographic research – describes
patterns of basic life events and
experiences, e.g. marriage, birth, divorce,
death, employment, immigration
• Epidemiological research – used to study
the occurrence of disease in different
groups of people
2
Sampling
• Sampling is the process by which a
researcher selects a sample of
participants for a study from the population
of interest.
• Representative sample: a sample from
which we can draw accurate, unbiased
estimates of the characteristics of the
population
Probability Samples
• Sampling error – differences between the
sample and the population
• Sampling error causes the results obtained from
the sample to differ from what would have found
if entire population had been studied.
• Researchers can estimate how much their
results are affected by sampling error.
• Error of estimation (margin of error) – degree to
which the data from the sample are expected to
deviate from the population as a whole.
Probability Samples
• Error of estimation is a function of 3 things:
– Sample size
– Population size
– Variance of the data
• The larger a probability sample, the more
similar to the population the sample tends
to be.
– Can’t always obtain a large sample – opt for
an economic sample – one that provides most
accuracy for least cost and effort
3
Probability Samples
• Size of population – the smaller the
population the smaller the error of
estimation
• The greater the variability in the data, the
more difficult it is to estimate accurately
the population values.
– The larger the variance, the less
representative the mean is of the scores as a
whole.
– Larger variance = need larger sample
Schedule
• Quiz Ch 6 today
• No Quiz on Ch 7
• NO CLASS ON MARCH 8th – since so
many already planning to be gone
• Quiz on Ch 8 when we return from Spring
Break – Monday, March 20th
• Research Hypothesis– with
operationalizations (e.g. how are you
going to measure) of variables - due on
March 20th
Probability Samples
• Error of estimation is only meaningful
when we have a probability sample.
• A probability sample is a sample for which
the researcher knows the probability that
any individual in the population is included
in the sample.
• Usually choose a design in which all
individuals in the population have an equal
chance of being selected for the sample.
4
Probability Samples
• 3 basic ways of drawing a probability
sample:
– Simple random sampling
– Stratified random sampling
– Cluster sampling
• Simple random sampling: every possible
sample of the desired size has the same
chance of being selected.
• Need a sampling frame – a list of the
population from which the sample will be
drawn.
Probability Samples
• Random numbers table or generator: commonly
used method for random selection
• # the cases in your population
• Take 200 #s – the subjects corresponding to
those #s are chosen for the study
4856
3494
2427
1789
4242
3528
1111
2772
2170
27
1210
2820
1532
2446
3742
1979
990
3811
3923
1627
2229
735
1156
1219
4441
2579
1689
4503
3496
3112
2229
3668
2947
4824
339
1331
4071
2396
2053
1082
Probability Samples
• Stratified Random Sampling – rather than
selecting cases directly- first you divide the
population into two or more strata
– Stratum – subset of population that shares a
particular characteristic
– Stratification ensures that you have adequate
#s of subjects from each strata.
– Proportionate sampling method – cases are
sampled from each strata in proportion to their
prevalence in the population.
5
Probability Samples
• Cluster sampling – Break target population
in to groups or clusters, first randomly
select clusters, then select participants
randomly from within each cluster.
• Cluster sampling often involves a
multistage sampling process – begin by
sampling large clusters, then sample
smaller clusters from within large clusters,
then even smaller clusters, then
participants.
Problems with Probability Samples
• The problem of nonresponse: failure to
obtain response from individuals that
researchers select for their sample
• Possible solutions:
– Try to decrease nonresponse rate - follow-up
on mailings with phone calls, include return
postage and envelopes (e.g. make it as easy
as possible for subject to respond)
– Determine whether respondents and nonrespondents differ systematically
Problems with Probability Samples
• Misgeneralization: if researcher
generalizes results to a population that
differs from the one from which the sample
was drawn
6
Non-probability Samples
• Don’t know the probability that a particular
case will be chosen for the sample, thus
can’t calculate the error of estimation.
• When research is designed to test
hypotheses regarding how variables
related to behavior, then non-probability
sample is appropriate.
• 3 basic types of non-probability samples
– Convenience samples
– Quota samples
– Purposive samples
Non-probability Samples
• Convenience sample: Researchers use
subjects who are readily available.
• Quota sample: Convenience sample in
which the researcher takes steps to
ensure that certain kinds of participants
are obtained in particular proportions.
• Purposive sample: Researchers use their
judgment to decide which participants to
include in the study, trying to choose those
who are more typical of population.
Before Doing Survey Research
• What is your hypothesis?
– Surveys best answer descriptive hypotheses
– describing a phenomena or population
– Let hypotheses guide creation of survey
• Otherwise may end up with a lot of useless
information
• Helps to plan analyses first – make sure you are
getting the data that you need
• Run through different possible outcomes to see if
different outcomes have different implications (e.g.
opposite of your hypothesis – would you have the
data to explain difference)
7
Different Survey Instruments
• How to choose depends on:
– Cost
– Response rate
– Honesty of respondents
– Standardization
Different Survey Instruments
• Self-administered questionnaires: filled out
by participants without assistance from
investigator (often investigator absent)
– Advantages: can be easily administered to a
lot of people, allows anonymity
– Drawbacks: low return rate may lead to
biased sample, can’t correct problems with
respondent’s understanding of the
questionnaire, may not be able to control
conditions in which respondent answers
questions (e.g. people helping, distractions)
Different Survey Instruments
• Investigator-Administered Questionnaires:
filled out in presence of investigator
• Advantages:
– Can be done in a variety of locations
– Investigator can clarify questions for
respondent
– Investigator presence encourages responding
• Disadvantages:
– Reduces sense of anonymity
– Increased cost of investigator presence
8
Different Survey Instruments
• Interviews
• Advantages:
– Interaction with the participant – can clarify
questions, follow-up on unclear or interesting
responses
– Personal interaction increases response rate
• Disadvantages
– More time consuming and costly
– Interviewer bias
– Greater demand for respondent to “impress”
interviewer – social desirability bias
Different Survey Instruments
• Telephone Interviews
• Advantages:
– Can be monitored/recorded to check
standardization
– Because can’t see respondent nonverbal
cues can’t contribute to interviewer bias or
social desirability bias
– Also greater anonymity over phone
– Fewer problems with sampling bias – use
random digit dialing
– Convenient, less time consuming
Different Survey Methods
• Telephone interviews
• Disadvantages:
– Still possibility of sampling bias (not all have
phones, or listed #s)
– Nonresponse is problem
– Have to ask simple, short questions
– Only know what respondent tells you – can’t
see for yourself (similar to problems of
internet surveys)
9
Format of Questions
• Fixed alternative versus open-ended
• Dichotomous versus continuous
• Fixed Alternative Questions: respondents
choose between 2 or more responses
– True/false, multiple choice, rating scales
• Open-Ended Questions: participants free
to respond in their own words
– Advantages: don’t put words in participants’
mouths, find out beliefs/opinions behind their
response
– Disadvantages: greater demands for
participants, difficult to score due to wide
variety
Format of Questions
• Dichotomous items: participants only have
2 answer choices (e.g. yes or no)
– Advantages: easier to decide, easier to
interpret so tends to be reliable
– Disadvantages: frustrating to reduce view
point to yes or no answer, may lose power by
artificially dichotomizing a variable
Format of Questions
• Likert-type items: respondents choose
from a range, usually 5 point scale ranging
from strongly disagree to strongly agree
– Yields more information, can use more
powerful statistical tests
– Can sum to get a total score
10