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Full summary Applying Research Methods and Statistics ()
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This document contains a complete summary of the book, the lectures and the articles required for the TOE exam.
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TOEAantekeningen boek
189-208 chapter 7 Research methods
Survey: A social research method in which researchers ask a sample of individuals to answer a series
of questions. The uniformity of surveys allows us to compare responses across subgroups and track
how responses change over time. Surveys tend to have better external validity than other methods.
Primary data collection: When social scientists design and carry out their own data collection
Secondary data source: A resource collected by someone else
Key informant: A person who is usually quite central or popular in the research setting and who
shares his or her knowledge with the researcher or a person with professional or special knowledge
about the social setting
Self-administered questionnaire (SAQ): A survey completed directly by respondents through the
mail or online
Mode of administration: The way the survey is administered, such as face to face, by phone or mail,
or online
Cross-sectional survey: A survey in which data are collected at only one time point. Limitation:
researchers cannot easily determine cause and effect
Longitudinal survey: A survey in which data are collected at multiple time points
Repeated cross-sectional survey: A type of longitudinal survey in which data are collected at multiple
time points but from different subjects at each time point
Panel survey: A type of longitudinal survey in which data are collected from the same subjects at
multiple time points
Attrition: The loss of sample members over time, usually to death or dropout
Poll: A very brief single-topic survey
Omnibus surveys: cover multiple topics and can be used by researchers with diverse research
intentions
Split-ballot design: A survey in which a randomly selected subset of respondents, typically 50% of
those persons selected to participate in the survey, receives one topical module while the other 50%
receives a different topical ballot.
Sources of error in surveys:
- Nonresponse
- Measurement error: A type of error that occurs when the approach used to measure a
particular variable affects or biases the response provided
- Coverage error: A type of error that occurs when the sampling frame does not adequately
capture all members of the target population, either by systematically omitting some or
including others more than once
, - Sampling error: The difference between the estimates from a sample and the true parameter
that arise due to random chance
Response rate: Proportion of the people contacted to participate in a survey who actually participate
Interviewer effects: The possibility that the mere presence of an interviewer, or that the
interviewer’s personal characteristics, may lead a respondent to answer questions in a particular
way, potentially biasing the responses
Social desirability bias: A type of bias that occurs when study participants report positively valued
behaviors and attitudes rather than giving truthful responses
Interview schedule: A prepared list of questions and follow-up prompts that the interviewer asks the
respondent
Paradata: Information about the process by which the survey data were collected
Paper-and-pencil interview (PAPI): A survey interview in which the researcher asks questions and
records the respondent’s answers in a preprinted copy of the survey booklet
Computer-assisted personal interview (CAPI): A face-to-face interview in which the researcher uses
a laptop or tablet that is pre-programmed with all of the survey questions and response categories
Audio computer-assisted self-interview: An interview in which the respondent uses a laptop or
tablet to listen to and answer prerecorded questions
Computer-assisted telephone interview (CATI): A telephone interview in which the researcher uses
a laptop or tablet computer that is preprogrammed with all of the survey questions and response
categories
Showcard: A preprinted card that reminds the respondent of all the response options for a particular
question or questions
Skip pattern: A question or series of questions associated with a conditional response to a prior
question
Screener question: A question that serves as a gateway to (or detour around) a follow-up question;
also called a filter question
Telephone surveys strengths: low costs, high quality, little advance planning, gauge national
sentiment on hot-button or emerging issues. Telephone surveys limitations: some people are
reluctant to discuss personal matters over the phone, many people do not want to be bothered
unexpectedly (sample bias), respondent fatigue, no showcard, no paradata
Mail surveys strength: less susceptible to interviewer effects, low cost. Mail surveys limitations:
susceptible to missing data or don’t know responses, no ensuring that the survey is completed by the
intended respondent, low response rate
Online surveys strength: low costs, low risk of data-entry errors, easy to design and administer,
respondents can easily navigate the questions, can be designed with graphics, fewer missing
responses. Online surveys limitations: biased towards younger people and with more economic
resources, no traditional sampling frame, no guarantee potential respondents will complete their
survey, ethical concerns for respondents who get low payments.
Researchers use mixed-mode approaches for several reasons:
, - To obtain new or different types of information from a single respondent
- Mixed modes allow researchers to assess the quality of their data and potential sources of
bias
- Some potential study participants might not want or be able to participate in a survey via a
particular mode
- May help increase response rates
Mode effects: The ways that the mode of administration might affect respondents’ answers
203-227 chapter 8 research methods
Bivariate correlation: an association that involves exactly two variables
Effect size: the strength of a relationship between two or more variables. All else being equal, larger
effect sizes are more important. “Small” effect sizes can compound over many observations. CI =
confidence interval. To communicate the precision of their estimate of r, researchers report a 95% CI.
When the 95% confidence interval does not include zero, it is common to say that the association is
statistically significant.
Replication: conduct the study again
Restriction of range: If there is not a full range of scores on one of the variables in the association, it
can make the correlation appear smaller than it really is.
Curvilinear association: the relationship between two variables is not a straight line
Correlation is not causation. To establish causation, a study must satisfy these criteria:
- Covariance of cause and effect
- Temporal precedence/directionality problem
- Interal validity/third-variable problem
Spurious association: The bivariate correlation is there, but only because of some third variable
When the relationship between two variables changes depending on the level of another variable,
that other variable is called the moderator. Moderators can inform external validity.
267-269 chapter 9 research methods
For any multivariate design it is appropriate to interrogate the construct validity of the variables in
the study by asking how well each variable was measured. We can also interrogate the external
validity of a multivariate design. For interrogating a multivariate correlational research study’s
statistical validity, we can ask about the point estimates and confidence intervals and ask whether
the study has been replicated.
499-500 stati sti cs review research methods
The correlation coefficient is considered a descriptive statistic because it describes the direction and
strength of a relationship between two numeric variables. We can evaluate the statistical significance
of r by following the steps of null hypothesis testing. Step 1: assume there is no relationship in the
population Step 2: collect data. Step 3: calculate the probability of getting such data, or even more
extreme data, if the null hypothesis is true. Step 4: decide whether to reject or retain the null
hypothesis. When we reject the null hypothesis, we are concluding that the relationship we observed
in our sample is statistically significant.
, The sampling distribution of r is developed based on the probable values of r we would get if we ran
the study many times on random samples from a population in which the null hypothesis is true. If
the null hypothesis is true, most of the values of r would be around .00. If the null hypothesis is true
and there is a large sample (greater than 30), then the sampling distribution of r is shaped very much
like the t distribution.
A larger r is more likely to be statistically significant. However, the statistical significance of a value of
r depends heavily on the sample size. Therefore, it is important to remember that r can also be used
as a measure of effect size.
248-260 chapter 9 research methods
By using a multivariate design, researchers can evaluate whether a relationship between two key
variables still holds when they control for another variable.
A positive beta indicates a positive relationship between the predictor variable and the criterion
variable, when the other predictor variables are statistically controlled for.
Multivariate designs cannot control for temporal precedence. A randomized experiment is the gold
standard for determining causation.
208-225 chapter 7 research methods
Survey instrument: The types of questions asked, the response categories provided, and guidelines
that help survey designers organize their questions
Stem: The part of the survey question that presents the issue about which the question is asking
Dichotomous outcome: When only two options are available to a question, such as ‘yes’ or ‘no’
Mutually exclusive: Preset response categories that do not overlap with one another, ensuring that
respondents select the single category that best captures their views
Exhaustive: Preset response categories that give all subjects at least one accurate response
Acquiescence bias: The tendency for respondents to answer agree to closed-ended attitudinal
questions
Composite measure: A measure that combines multiple items, whether as a scale or an index, to
create a single value that captures a multifaceted concept
Index: A composite measure that sums responses to survey items capturing key elements of a
particular concept being measured
Scale: A composite measure that averages responses to a series of related items that capture a single
concept or trait, such as depressive symptoms or self-esteem
Double-barreled question: A question that asks about two or more ideas or concepts in a single
question
Codebook: A system of organizing information about a data set, including the variables it contains,
the possible values for each variable, coding schemes, and decision rules.
Response set: The tendency to select the same answer to several sequential questions, perhaps out
of boredom or a desire to quickly finish the survey
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