Introduction to Survey Research
Pathways to Careers in Clinical and Translational Research (PACCTR)
Curriculum Core
Introduction to Survey
Research
Harry Truman displays a copy of the Chicago Daily Tribune
newspaper that erroneously reported the election of Thomas Dewey
in 1948. Truman's narrow victory embarrassed pollsters, members
of his own party, and the press who had predicted a Dewey
landslide.
Overview of Sections
•One: Basics of Survey Research
•Two: Measurement Levels and Types of Data
•Three: Elements of Item Construction
•Four: Assessing Reliability and Validity
•Five: Determining the Coding System
•Six: Deciding How to Enter and Store the Data
•Seven: Data Entry, Data Checking and Quality Control
•Resources
Section One
Basics of Survey Research
Section 1
Basics of Survey Research
• Objectives
– Describe what surveys are used to measure
– Give examples of how surveys can assess change
– Define "needs", "assets", "behavior", "opinions", "attitudes",
"beliefs"
Key Terms:
Survey, questionnaire, descriptive research, causal explanation,
prediction, evaluation, single sample, successive samples, panel study
What do surveys measure?
• Behavior, attitudes and beliefs
• Repeated observations.
–
Example: if a researcher were interested in exercise patterns in obese
patients, we could follow them every time they perform any physical activity
to determine frequency, duration and intensity. Similarly, if we were
interested in dietary habits, we could sit at their table in the morning and
record what they ate.
• This is not feasible and there are many behaviors that cannot be
directly observed such as attitudes, beliefs and opinions.
What do surveys of people measure?
• So, what are researchers to do?
Do a Survey
(Questionnaires)
• Surveys are a systematic way of asking people to volunteer information
about their attitudes, behaviors, opinions and beliefs. The success of
survey research rests on how closely the answers that people give to
survey questions matches reality that is, how people really think and
act.
• The first problem that a survey researcher has to tackle is how to
design the survey so that it gets the right information.
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–
–
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Is this survey necessary?
Is the purpose of the survey to evaluate people or programs?
Can the data be obtained by other means?
What level of detail is required?
• The second problem is how accurate does the survey have to be?
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–
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Is this a one-time survey or can the researcher repeat the survey on
different occasions and in different settings?
How will the results be used?
How easy is it to do the survey?
Why do a survey?
• The survey is an appropriate means of gathering information
under three conditions:
– when the goals of the research call for quantitative and
qualitative data,
– when the information sought is specific and familiar to the
respondents and;
– the researcher has prior knowledge of the responses likely to
emerge.
Use of Surveys by study design
• Descriptive research
– Describe phenomena and summarize them.
– The goal of using surveys for descriptive research is to get a
precise measurement of specific concepts i.e. depression, quality of
life.
• Causal explanation
– Measure associations i.e. chemotherapy and quality of life.
– The data from surveys can provide a causal explanation to
phenomena such as why teens become pregnant or why teens do
drugs.
• Evaluation
– Efficacy of a program
• Prediction
– Predict future events
Basic Survey Designs
• Cross-sectional surveys:
– Data collected at one point in time selected to represent a
larger population
• Longitudinal Surveys:
– Trend:
• Surveys of sample population at different time points
– Cohort:
• Study of sample population each time data are collected but
samples studied maybe different
– Panel:
• Data collection at various time points with the same sample of
respondents
Section Two
Measurement Levels and Types of Data
Section Two
Measurement Levels and Types of Data
• OBJECTIVES
–
–
–
–
Define and give examples of levels of measurement
Define and give examples of scales
Contrast the types of measurement scales
Compare and contrast interval versus ordinal scales
Key Terms
Categorical, ordinal, scale, attitudes, opinions, beliefs,
behavior, attributes
Selecting the types of data to collect
• The type of data you collect and how you
collect them affects how you can use the data.
• Therefore, before you start data collection you
must decide what will be measured and how it
will be measured.
Types of data
• Categorical data: numbers or words are used to group
things
– Examples: gender, race, religion, food group, or place of
residence.
• Ordinal data: When the numbers are used to order a list
of things
– The ranking of football or basketball teams is done using
ordinal numbers. A list of things to do would also be ordinal
data.
• Interval data.
– Scale or interval data would be things like height, weight,
age.
Types of data collected
• There are six basic types of data that you might collect:
–
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Attitudes
Opinions
Beliefs
Behavior
Attributes (demographic characteristics)
Preferences
Basic types of survey questions
• The way a question or statement is worded and the
response options offered determine the nature of the
data received.
• Types of survey questions include:
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Open-ended response
Closed response
Semantic differential scales
Agreement and rating scales
Ranking scales
Checklists
Basic types of survey questions
• Open-ended question
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Respondent writes response in own words
Considerations for using open-ended questions:
• Need to enter data by hand
• Develop a coding scheme for responses
• Content analysis?
–
Frequently used in exploratory studies to facilitate better understanding of
a concept
• Suggestion: it’s a good idea to always include an open-ended question giving the
respondent the opportunity to add any additional comments.
Basic types of survey questions
• Open-ended question
– Advantages:
• Allows the respondent to answer the question with few
limitations
• Report more information than with discrete answers
– Disadvantage:
• Need qualitative methods or coding system to analyze the
responses
• Require subjective judgements
– Example:
• What habits increase a person’s risk for being overweight?
• Describe the pain you experience with walking?
Basic types of survey questions
• Closed response:
– These are the "multiple-choice" variety where a
person has to choose among several possible answers.
– There are two types of closed response questions:
• ordered answer choices represent points along a
continuum.
– Pain on a scale of 0 (none) to 10 (worst pain
ever)
• unordered answer choices with each choice is an
independent answer.
– Examples: ethnicity and marital status
Basic types of survey questions
• Closed response:
– Advantages:
• Quicker and easier to answer
• Easier to tabulate and analyze
• List of possible responses helps participant understand
the meaning of the question
• Suitable to multi-item scales designed to provide a
single score
– Disadvantages:
• Do not allow participants to express their own answers
• Set of answers may not be exhaustive
• Must be clear about selection of items, one or as
manay as applicable
Basic types of survey questions
• Closed response:
– When question allows more than one answer it does not
force respondent to consider each answer- Not ideal.
– Better to use yes no and multiple items
– Example:
Which of the following do you believe increases your risk for
stroke?
Yes
No
Don’t know
•
•
•
•
Smoking
Overweight
Stress
Drinking alcohol
Hulley et al., (2007) Designing Clinical Research, 3rd Edition pg. 242
Basic types of survey questions
• Partially open response
– Require specific, short answers that do not encourage free
expression.
– are a compromise between closed response and open
response forms.
– provide an “Other” category where a person can provide
additional information.
– Example: blank spaces provided for the questions on racial
background and persons living with you.
Modes of Survey Administration
• Personal (face-to-face)
• Telephone
• Mail
• Web
• Combination of methods
How do you decide on the mode of
data collection?
Population
+
Characteristics Of The Sample
+
Types of Questions
+
Question Topic
+
Response Rate
+
$$ Cost $$
+
Time
Modes of Survey Administration
Personal (face-to-face)
• ADVANTAGES:
– Generally yields highest cooperation and lowest refusal rates
– Allows for longer, more complex interviews
– High response quality
– Takes advantage of interviewer presence
– Multi-method data collection
• DISADVANTAGES:
– Most costly mode of administration
– Longer data collection period
– Interviewer concerns
Modes of Survey Administration
Telephone
• ADVANTAGES:
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–
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Less expensive than personal interviews
RDD samples of general population
Shorter data collection period than personal interviews
Interviewer administration (vs. mail)
Better control and supervision of interviewers (vs.
personal)
– Better response rate than mail for list samples
• DISADVANTAGES:
–
Biased against households without telephones, unlisted
numbers
– Nonresponse
– Questionnaire constraints
– Difficult to administer questionnaires on sensitive or
complex topics
Modes of Survey Administration
Mail
• ADVANTAGES:
–
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Generally lowest cost
Can be administered by smaller team of people (no field
staff)
– Access to otherwise difficult to locate, busy populations
– Respondents can look up information or consult with others
• DISADVANTAGES:
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Most difficult to obtain cooperation
No interviewer involved in collection of data
Need good sample
More likely to need an incentive for respondents
– Slower data collection period than telephone
Comparison of Modes of Survey Administration
Variable
Mail
Phone
F/F
Cost
Cheapest
Moderate
Costly
Speed
Moderate
Fast
Slow
Response rate
Low to moderate
Moderate
High
Sampling need
Address
Telephone number
Address
Burden on respondent
High
Moderate
Low
Control participation
Of others
Unknown
High
Variable
Short
Moderate
Long
Sensitive questions
Best
Moderate
Poor
Lengthy answer
choices
Poor
Good
Best
Open-ended
responses
Poor
Good
Best
Complexity of
Questionnaire
Poor
Good
Best
Possibility of
interviewer bias
None
Moderate
High
Length of
Questionnaire
Modes of Survey Administration
WEB SURVEYS
• ADVANTAGES:
– Lower cost (no paper, postage, mailing, data entry costs)
– Can reach international populations
– Time required for implementation reduced
– Complex skip patterns can be programmed
– Sample size can be greater
• DISADVANTAGES:
– Approximately 40% of homes own a computer; 30% have
home e-mail
– Representative samples difficult - cannot generate random
samples of general population
– Differences in capabilities of people's computers and
software for accessing Web surveys
– Different ISPs/line speeds limits extent of graphics that
can be used
Section Three
Elements of Item Construction
Section Three
Elements of Item Construction
•
OBJECTIVES
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–
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Identify types of bias in questionnaires
Identify and state how to correct common
wording problems
Define types of response sets
Key Terms
Social desirability, acquiescence, bias, midpoint,
operational definition, response set
How Bias affects response?
Need we say more?
Writing questions
– Good Questions
•
•
•
•
•
•
•
•
Are clear and use simple language
Are concise
Are specific
Are possible to answer
Are relevant to the respondent
Do not use negatives
Avoid bias terms
Have only one part (not two parted question)
Writing questions
• Common Wording Problems
– Writing questions for a particular survey means doing them
for
•
•
•
•
a particular population,
a particular purpose and
for placement next to other questions in the survey.
Words that are too difficult for some to understand may be
perfectly acceptable for others. A question that is fairly vague
may satisfy the objectives of one study but not the ones of
another.
– So that every respondent will understand a question, it is
important to keep the reading level at or below the average
reading level of the population. Complex words may be
replaced by simpler ones or ones more easily understood. If
you are giving a survey to a particular group, you would want
to use words that are common to the group.
Response Options
• Should reflect concepts you are trying to measure, and
fit with the wording of the question
– Avoid simple “yes” or “no” answers and attempt to measure
intensity if possible
• Mutually exclusive (select only one answer)
• Exhaustive (all possible answers are listed, including other
or not applicable or don’t know)
Question Order
• Be attentive:
– Initial questions affect answers to subsequent ones
• Start with easy, salient, non-threatening questions near
the end
• Cluster questions addressing the same topic or concept
together.
• Avoid redundancy
Questionnaire Format
• As short as possible
• Visually attractive and nicely reproduced
• Readable (consider font size)
• Uncluttered
• Broken into logical sections if possible
• Clear skip patterns for contingency questions
• Clear spaces for respondents to mark answers
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Boxes
Parentheses (X)
Or numbers to circle 1…2…3
Avoid lines to put check___
• Amount of space provided for open-ended questions will
determine amount respondent will write.
Questionnaire Format
Example
• The following questions are about Instructions from the Medical
outcomes study
Pain measures the pain or pains you experienced in the past 4
weeks. If you had more than one pain, answer the questions by
describing your feelings of pain in general.
1. How much bodily pain have you generally had during the past
4 weeks?
(Circle one)
None……………………………………………….1
Very mild……………………………………….2
Mild………………………………………………….3
Moderate……………………………………….4
Severe…………………………………………….5
Very Severe………………………………….6
Questionnaire Format
• Helpful Hint…
– Get consultation on questions and response options
from statisticians and data management people.
Instructions to Respondents
• General instructions should be provided at the beginning
of the self-administered survey
– Brief explanation includes:
•
•
•
•
Purpose
Significance of the answers
How to answer the questions
Stress: CONFIDENTIALITY OF RESPONSES
– Provide specific instructions to questions as needed
Instructions to Respondents
Example
• The following questions are about Instructions from the
Medical outcomes study
Pain measures the pain or pains you experienced in the
past 4 weeks. If you had more than one pain, answer
the questions by describing your feelings of pain in
general.
Pre-test (pilot)
• Be sure to pilot test the survey instrument before the
actual administration, especially if it is a new instrument
that you constructed or has not been used previously in
your population of interest.
– Helpful Hint:
• Also good idea to test your sample design, data collection
methods, data processing and analysis if possible.
Pre-test (pilot)
• Use a similar population or identical population you will be
measuring.
• Testing:
– Question clarity
•
•
•
•
Failure to answer?
Multiple answers?
“Other” answers and how to handle them in data entry
Qualified answers
– Questionnaire format
• Instructions?
• Flow?
• Layout?
– Variance in responses
• Consistency in responses among respondents
Pre-test (pilot)
• Revise instrument as needed
• Repeat pre-test
– Ideally with some of the original pre-test respondents and
new respondents
Data collection
Hints
• Mail survey should include:
– Survey instrument
– Return envelope
• Self-addressed and stamped
Data collection
Hints
• Self-administered survey should include:
– Cover letter
• On letterhead
• Signed by PI or most significant person possible
• Brief explanation of
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Purpose
How results will be used
Why respondent was selected
Why answers are important
Emphasize confidentiality (if applicable)
Provide contact information for questions
Describe/explain any incentives for participation or consequences or
not for non-participation
Monitor survey returns
• Open surveys as they are returned
• Assign unique ID number (if not already assigned)
• Stamp date on survey
• Track number returns daily
– Used to inform timing of follow-up reminders
Monitor survey returns
• Optimal response occurs with 2 reminders
– Timing
• Based on returns (1-2 weeks after original)
– Materials (mail survey)
• Reminder card to have new survey sent or
• Send entire packet again
Section Four
Assessing Reliability and Validity
Section four
Assessing Reliability and Validity
• OBJECTIVES
– Compare and contrast types of reliability
– Compare and contrast types of validity
Key Terms
Face validity, content validity, concurrent validity,
discriminant validity, construct validity, test-retest
reliability, internal consistency, stability.
Reliability and validity
• In order for information to be useful, it has to
be:
– consistent,
– dependable,
– accurate and,
– most of all, true.
• Too often, we are presented with information
that fails on one or more of these criteria.
• In research, these criteria are represented by
the concepts of reliability and validity.
Reliability and validity
• Reliability:
– Expect to obtain the same information time after time.
• Assessed by correlation coefficient
• The concept of reliability can be applied to sampling
• If we repeatedly draw random samples of equal size from a
population, we can expect to get the same sample values each
time (plus or minus a certain amount due to sampling error).
• Validity:
– Measures the concept intended to measure
• Instrument is presented or used in the way for which it was
intended
• An IQ test is valid only if it is used to measure intelligence it
is not valid if it used to assign individuals to groups.
• A psychological test that is a valid measure of anxiety is not a
valid measure of stress.
Types of validity
• Face validity
– Information collected appears to be what was expected. (Face
value)
– A question that asked Do you smoke? would appear to have face
validity as a measure of smoking behavior.
• Content validity
– A question adequately reflects the underlying behavior or body of
knowledge.
– Content validity is established by having a panel of experts
evaluate and agree on the relevance of the test items.
• Concurrent validity
– One instrument or question is comparable to another that has been
shown to validly measure the same content or construct.
– Concurrent validity is established by correlating one question with
another that has previously been validated.
Types of validity
• Discriminant validity
– A question or survey that is able to discriminate between group
differences.
– Example
• Depressions scales have discriminate validity if individuals who are
depressed score differently from those who are not clinically
depressed.
• Predictive validity
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–
A question can be used to predict behavior.
Example:
• Can you walk 5 blocks?
• Construct validity
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–
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A construct is a theoretical dimension like self-esteem that is measured by
having several questions that all relate to how people view themselves.
Self-esteem does not exist by itself but is represented by how people
respond to these questions.
In this example, construct validity measures the extent to which these
responses can be called, self-esteem.
Types of reliability
• Test-retest reliability
– Obtained by administering the same test on two or more
successive occasions and then correlating the scores.
– Statistic that reflects reliability is correlation coefficient, higher
is better.
• Internal consistency
– Obtained by correlating the scores on several questions that
pertain to the same content to the sum total of the scores.
– The average item-total correlation is a measure of how
consistently people respond to related items on a test.
– For example: if a math test had several sets of items that
required people to multiply two and three digit numbers, you would
expect persons who could not correctly multiply two digit numbers
to be unable to multiply three digit numbers. Likewise, you would
expect persons who can multiply three digit numbers to be able to
multiply two-digit numbers. These expectations should be
consistent from person to person and would be borne out by high
item-total correlations of the items.
Types of reliability
• Stability
– How much variation exists in scores upon repeated
administrations of the instrument.
– Stable measures will reproduce the same score on repeated
administrations of the instrument.
– This concept is similar to test-retest except that in testretest situations there is no assumption that the absolute
value of each persons test score will stay the same.
– Considerations:
• Time between administration, if too close may be remembering
answers
• Learning effect on repeated administrations
Section Five
Determining the Coding System
Section Five
Determining the Coding System
• OBJECTIVES
– Develop a coding scheme that reduces a data to
quantifiable terms.
– Compare and contrast different types of coding schemes
– List coding requirements of various computer programs
Key Terms
coding, alphanumeric, missing values, fixed field, free field,
recoding, reconversion
The fine art of coding
– Coding:
• The process of converting information into a quantifiable format
(usually numerical) so that a systematic analysis of the information
can be done.
• The coding process is made easier by precoding responses and
enabling circled or checked numbers to be entered directly into a
data set.
• The codebook is like a foreign language dictionary in that it
translates English responses into numerical or categorical values.
• The coding system used should model or be easily convertible to
the format required by the computers software.
– If the information is categorical in nature, then, the choice of
whether to represent a data value as a number or a label depends upon
the analyses that will be done.
– If you are only going to measure frequency counts and percentages,
then either a number or label is acceptable.
The fine art of coding
• Statistical analyses requires numbers.
• Labels cannot be manipulated mathematically.
• Qualitative data requires content analysis
– data elements might be entered as words and phrases instead of
numbers.
• Missing values
– When data cant be collected on one or more variables from one or
more source.
– Statistical computer programs deal with missing values in different
ways.
– Generally, they give you the option of identifying what values are to
be considered Missing, or allowing the computer to assume that zeroes
or blank spaces represent missing values.
– Should you assign a number to represent missing values?
– Yes, if you intend on either analyzing the missing values or replacing
them with other values.
– You must identify or define your missing values before doing any
analyses.
Data organization
• When data is entered into a computer, it will generally be
organized in a spreadsheet orientation where the rows
represent different cases and the columns represent different
variables.
• If you leave spaces blank instead of typing in a value, you may
not know if the space represents a missing value or a missing
data entry.
• By entering values in every space provided, you will know
exactly what data has or has not been collected.
Section Six
Deciding How to Enter and Store the Data
Section Six
Creating the database structure
• OBJECTIVES
– Define the elements of a database
– Describe the steps in creating a database
– Describe how to use a form to enter data
Key Terms
database, table, records, fields
Creating the database structure
• Generally, the first column of a data table
will have an unique ID number that will
identify the survey.
–
This is to allow the researcher to go back and
check the data on the survey with the data
entered on the computer.
– The next columns of the table will usually contain
the data from the survey in the order of the
questions on the survey.
Creating the database structure
 To make it easier to identify and manipulate the data,
each column is given a unique short name that refers to
the question number or subject on the survey.
o If the questions on a survey are numbered, then an easy
way to label the columns would be Q1, Q2, etc
Section Seven
Data Entry, Data Checking and Quality Control
Section Seven:
Data Entry, Data Checking and Quality Control
• OBJECTIVES
– Define the variables of interest in the study
– Demonstrate procedures for developing a
questionnaire
– Define the major methods of data collection in
quantitative studies and qualitative studies
– Demonstrate how to determine if responses are
random and representative
Key Terms
variable, coding scheme, tabulation, missing data,
non-response bias
Data entry and quality control
• Data entry is the process of transferring or transcribing
the information from coding sheets, field notes,
questionnaires and surveys onto a computer.
– The process of data entry can be facilitated by use of
machine-readable sheets or by image scanners and
optical character recognition software
• manual data entry is not required.
– Important to check the accuracy of the data
– Analyses hinges on the accuracy of the data, good
quality control procedures are needed for error checking
and entry consistency.
Data entry and quality control
• There are computer programs that facilitate the quality
control function of data entry directly by checking for
acceptable values at the time they are entered.
– Example:
• if you recording ages of adults in a study, values lower
than 18 would be errors.
• Manual checking of the data may or may not spot the
error.
– The data entry program would immediately reject any
entered value that does not fall within the accepted
range or is not of the proper form.
Data coding problems
• Missing data handling
– Prepare for nonresponse as treating it as one of the categories
(example, a No response).
– Other situations, you might want to assign the average value to
retain the rest of the data for analyses that require it.
– Individual item analysis, drop the missing data adding constants
dont change the overall results.
– For statistical purposes, to keep as much of the original sample
size as possible.
Data coding problems
• Another solution: Regression
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–
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can be used to estimate missing data using answers to other responses.
The problem may be avoided by having more than one question dealing
with a topic.
Not everything you ask will be of equal importance some items you may
afford to lose data.
The main problem with nonresponse is that you dont know if someone
skipped a question or decided not to respond.
Including a non-response category will ensure that people respond.
Remind them both at the beginning of the questionnaire and at the end
to answer ALL times.
Dont give people a way not to respond give them the opportunity to
respond.
You can identify respondents who essentially are non-participants: people
who mark the middle response for all items. Only slightly better than
nonresponders.
Do you keep them in the data bank? No, since their responses will skew
the data
Avoiding bias from non-respondents
• Its important to know whether those who did not
respond differ greatly from those who responded.
– Relatively few refusals provide the theoretical potential
for introducing considerable error into estimates of the
sample characteristics.
– The extent of the differences between respondents and
nonrespondents can seldom be determined.
– Indirect methods may be used.
Avoiding bias from non-respondents
• Mail questionnaires are also susceptible to other factors
that affect response rate.
– Interest in the subject of the questionnaire may affect ones
answers and also determine if the questionnaire will be
completed and returned.
– Another consideration is the ability of the respondent to
provide written responses.
– People with less education are likely to be underrepresented,
partly because of lower educational attainment, but also
because of more difficulties with their seeing and writing
capabilities.
Web Resources:
• http://gsociology.icaap.org/methods/surveys.htm
• http://athome.harvard.edu/dh/vsr.html
• http://www.socialresearchmethods.net/kb/survtype.php
• http://www.hcp.med.harvard.edu/statistics/survey-soft/
• http://gking.harvard.edu/projects/survey.shtml
• http://www.whatisasurvey.info/
• Http://www2.acs.ncsu.edu/UPA/surveyt/uapr.survey_research/
• http://www.srl.uic.edu
Resources
• Hulley, Cummings, Browner, Grady, Newman (2007). Designing Clinical
Research, 3rd Edition. Philadelphia. Lippincott, Williams & Wilkins.
• ESSENTIALS OF SURVEY RESEARCH AND ANALYSIS A WORKBOOK
FOR COMMUNITY RESEARCHERS © 1998 Developed by Dr. Ronald
Jay Polland for the Adolescent Pregnancy Prevention Grant, Duval
County Health Department.
• Babbie, Earl (2002), The Basics of Social Research 2nd ed.
Wadsworth Thomson Learning: CA.
• Dillman, D.A. (2000), Mail and Internet Surveys 2nd ed. Wiley: NY.
• Fink, Arlene and Jacqueline Kosecoff (1998), How to Conduct Surveys.
Sage: CA.
http://gsociology.icaap.org/methods/surveys.htm
• Fowler, Floyd J. (1993), Survey Research Methods. Sage: CA.
• Fowler, Floyd J. (1995), Improving Survey Questions. Sage: CA.
• Sudman, Seymour and Bradburn, Norman (1982), A Practical Guide to
Questionnaire Design. Jossey-Bass: San Francisco.
• Tourangeau, R., and Smith, T.W. (1996), “Asking Senstive Questions:
The Impact of Data Collection Mode, Question Format, and Question
Context, “ Public Opinion Quarterly, 60:275-304.
• Also see American Association of Public Opinion Research, “Best
Practices for Survey and Public Opinion Research.”
http://www.aapor.org/ethics/best.html
Resources
• Aday, L. A. (1996). Designing and Conducting Health Surveys, 2nd ed.
San Francisco: Jossey-Bass.
• Biemer, P., Groves, R., Lyberg, L., Mathiowetz, N., & Sudman, S.
(eds.) (1991). Measurement Errors in Surveys. New York: Wiley.
• Dillman, D. (1978). Mail and Telephone Surveys: The Total Design
Method. New York: Wiley.
• Dillman, D. (2000). Mail and Internet Surveys: The Tailored Design
Method. New York: Wiley & Sons.
• Fink, A., & Kosecoff, J. (1985). How to Conduct Surveys: A Stepby-step Guide.
Beverly Hills, CA: Sage, 1985.
http://gsociology.icaap.org/methods/surveys.htm
• Fowler, F. J., Jr. Survey Research Methods, 2nd ed. Newbury Park,
CA: Sage, 1993.
• Groves, R. (1989). Survey Errors and Survey Costs. New York: Wiley,
1989.
• Groves, R., Biemer, P., Lyberg, L., Massey, J., Nicholls, W., II, &
Waksberg, J. (eds.) (1988). Telephone Survey Methodology. New
York: Wiley.
Resources
• Lavrakas, P. J. (1993). Telephone Survey Methods: Sampling,
Selection, and Supervision. Newbury Park, CA: Sage.
• Lessler, J. T., & Kalsbeek, W. D. (1992). Nonsampling Error in
Surveys. New York: Wiley.
• Lyberg, L., Biemer, P., Collins, M., deLeeuw, E., Dippo, C., Schwarz,
N., & Trewin, D. (eds.) (1997). Survey Measurement and Process
Quality. New York: Wiley.
• Marín, G,. & Marín, B. V. (1991). Research with Hispanic Populations.
Newbury Park, CA: Sage.
• Turner, C. F., & Martin, E. (eds.) (1984). Surveying Subjective
Phenomena (2 volumes). New
York: Russell Sage. Journals: Public Opinion Quarterly and Journal of
Official Statistics
PACCTR* Curriculum Core
• Roberta Oka RN, ANP, DNSc, School of Nursing
• George Sawaya MD, School of Medicine
• Rebecca Jackson MD, School of Medicine
• Susan Hyde DDS, MPH, PhD, School of
Dentistry
• Jennifer Cocohoba PharmD, School of Pharmacy
• Joel Palefsky MD School of Medicine
* Pathways to Careers in Clinical and Translational Research