Survey Design

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    7126/6667 Survey Research & Design in Psychology Semester 1, 2009, University of Canberra, ACT, Australia James T. Neill http://ucspace.canberra.edu.au/display/7126 http://www.slideshare.net/jtneill/survey-design-ii Image sources: Note from Wikicommons GFDL Photo from http://www.flickr.com/photos/31910792@N05/3163866325/ CC-by-A 2.0 by jamesdal10 http://www.flickr.com/photos/31910792@N05/

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    Survey Design - Presentation Transcript

    1. Lecture 2 Survey Research & Design in Psychology James Neill, 2009 Introduction to Survey Design
    2. Overview
      • The research process
      • Survey construction
      • Sampling
      • Levels of measurement
      • Measurement error
    3. The research process 1. Establish need for info/ research 2. Problem definition/ Hypotheses 3. Research design 4. Sampling/ Data collection 5. Data analysis 6. Reporting
    4. The research process
    5. Survey construction: Overview
      • What is a survey?
      • Types of questionnaires
      • Questionnaire development
      • Writing questions
      • Types of questions
      • Response formats  LOM
      • Survey formatting
      • A standardised stimulus
      • A measuring instrument
      • A way of converting fuzzy psychological stuff into hard data for analysis
      What is a survey?
    6. Types of surveys Types of surveys Self - administered Interview - administered Postal Delivered and collected Telephone Face to face structured interview Web-based
    7. Questionnaire development 1. Formulate generic questionnaire 2. Expand the questionnaire Turn into separate sections based on study objectives. Draft qs & response formats 4. Finalise questionnaire & implement Question order & funnel qs 3. Pre-test, pilot test, & redraft
    8. Writing questions - Dos
      • Define target constructs
      • Check related research & questionnaires
      • Draft items (for important, fuzzy constructs aim to have multiple indicators)
      • Pre-test & revise
    9. Writing questions - Dos
      • Focus directly on topic/issue
      • Be clear
      • Be brief
      • Avoid big words
      • Use simple and correct grammar
    10. Writing questions – Don'ts Inapplicable – must apply to all respondents Over-demanding – e.g., recall of detail or time-consuming, unecessary questioning Ambiguous – meaning must be clear to all respondents Double negative – e.g., Do you not approve of tax reforms?
    11. Writing questions – Don'ts Double-barrelled - e.g., “Do you think speed limits should be lowered for cars & trucks?” Leading - e.g., “don’t you see some danger in the new policy?” Loaded – e.g., “Do you advocate a lower speed limit to save human lives?” vs “Does traffic safety require a lower speed limit?”
    12. Response biases
      • Social desirability
      • Acquiescence
        • yea- and nay-saying
      • Self-serving bias
      • Order effects
    13. Demand characteristics Interview
      • High demand characteristics
      • Can elicit richer information
      Questionnaire
      • Lower demand characteristics
      • Information may be less rich
    14. Objective questions
      • A verifiably true answer (i.e., factual information) exists for each unit.
      • The question could be accurately answered by an observer.
      e.g., How times during 2008 did you visit a G.P.? ______
    15. Subjective questions
      • Asks about fuzzy personal perceptions.
      • There is no “true”, factual answer.
      • Many possible answers per unit.
      • Can't be accurately answered by an observer. e.g.,
      Think about the visits you made to a G.P. during 2008. How well did you understand the medical advice you received? perfectly very well reasoably poorly not at all
    16. Objective vs. subjective questions
      • Both types of questions may be appropriate; depends on the purpose of the study.
      • One criticism of this distinction: There is no such thing as “objective” and that all responses are subjective.
    17. Accuracy of recall decreases over time
    18. Open-ended questions
      • Rich information can be gathered
      • Useful for descriptive, exploratory work
      • Difficult and subjective to analyse
      • Time consuming
    19. Open-ended questions: Examples What are the main issues you are currently facing in your life? How many hours did you spend studying this week? _________
    20. Closed-ended questions
      • Important information may be lost forever
      • Useful for hypothesis testing
      • Easy and objective to analyse
      • Time efficient
    21. How many hours did you spend studying this week?  less than 5 hours  > 5 to 10 hours  > 10 to 20 hours  more than 20 hours Closed-ended questions: Example (multichotomous)
    22. Closed-ended questions: Example (multiple response) What are the main issues you are currently facing in your life? ( tick all that apply)  financial  physical / health  academic  employment / unemployment  intimate relations  social relations  other (please specify) ________________________________
    23. Closed-ended rating scales
      • Dichotomous
      • Multichotomous
      • Verbal frquency scale
      • The list (multiple response)
      • Ranking
      • Likert scale
      • Graphical rating scale
      • Semantic differential
      • Non-verbal (idiographic)
    24. Dichotomous 2 response options e.g., Excluding this trip, have you visited Canberra in the previous five years? __ Yes __ No Provides the simplest type of quantification
    25. Multichotomous How many hours did you spend studying this week? __ less than 5 hours __ > 5 to 10 hours __ > 10 to 20 hours __ more than 20 hours
    26. Multichotomous More than two possible answers e.g., What type of attractions in your current trip to Canberra most appeal to you? __ historic buildings __ museum/art galleries __ parks and gardens
    27. Verbal frequency scale Over the past month, how often have you argued with your intimate partner? 1. All the time 2. Fairly often 3. Occasionally 4. Never 5. Doesn’t apply to me at the moment
    28. The list (multiple response) Provides a list of answers for respondents to choose from e.g., Tick any words or phrases that describe your perception of Canberra as a travel destination: __ Exciting __ Important __ Boring __ Enjoyable __ Interesting __ Historical
    29. The list (multiple-response) What are the main issues that you are currently facing in your life? (tick all that apply) __ financial __ physical / health __ academic __ employment / unemployment __ relationships __ other (please specify)
    30. Ranking Helps to measure the relative importance of several items Rank the importance of these reasons for taking a holiday to Canberra (from 1 (most) to 4 (least)): __ to visit friends and relatives __ for business __ for educational purposes __ for holiday/ sightseeing
    31. Likert scale Measures strength of feeling or perception. Indicate your degree of agreement with this statement: “ I am an adventurous person.” (circle the best response for you) 1 2 3 4 5 strongly disagree neutral agree strongly disagree agree 1 2 3 4 5 strongly agree neutral disagree strongly agree disagree
    32. Graphical rating scale How would you rate your enjoyment of the movie you just saw? Mark with a cross (X) not enjoyable very enjoyable
    33. What is your view of smoking? Tick to show your opinion. Bad ___:___:___:___:___:___:___ Good Strong ___:___:___:___:___:___:___ Weak Masculine ___:___:___:___:___:___:___ Feminine Unattractive ___:___:___:___:___:___:___ Attractive Passive ___:___:___:___:___:___:___ Active Semantic differential
    34. Non-verbal scale Point to the face that shows how you feel about what happened to the toy. Also called an idiographic scale .
    35. Verbal frequency scale Over the past month, how often have you argued with your intimate partner? 1. All the time 2. Fairly often 3. Occasionally 4. Never 5. Doesn’t apply to me at the moment
    36. Sensitivity & reliability
      • Scale should be sensitive yet reliable.
      • Watch out for too few or too many options.
    37. How many response options?
      • Minimum = 2
      • Average = 3 to 9
      • Maximum = 10?
      Basic guide: 7 +/- 2 Number of response options?
    38. AGREEMENT ABOUT SOMETHING 2-Categories DISAGREE AGREE 3-Categories DISAGREE NEUTRAL AGREE 4-Categories STRONGLY MILDLY MILDLY STRONGLY DISAGREE DISAGREE AGREE AGREE 5-Categories STRONGLY MILDLY MILDLY STRONGLY DISAGREE DISAGREE NEUTRAL AGREE AGREE Number of response options? Likert scale example
    39. Watch out for too many or too few response options “ Capital punishment should be reintroduced for serious crimes ” 1 = Agree 2 = Disagree 1 = Very, Very Strongly Agree 7 = Slightly Disagree 2 = Very Strongly Agree 8 = Disagree 3 = Strongly Agree 9 = Strongly Disagree 4 = Agree 10 = V. Strongly Disagree 5 = Slightly Agree 11 = V, V Strongly Disagree 6 = Neutral
    40. Example: How could this question be improved? How old are you? ___ 18-20 ___ 20-22 ___ 22-30 ___ 30 and over
    41. Are you satisfied with your marriage and your job? __________________________ Example: How could this question be improved?
    42. You didn’t think the food was very good, did you? _____ Yes _____ No Example: How could this question be improved?
    43. Environmental issues have become increasingly important in choosing hotels. Are environmental considerations an important factor when deciding on your choice of hotel accommodation? ____ Yes ____ No Example: How could this question be improved?
    44. What information sources did you use to locate your restaurant for today’s meal? (please tick appropriate spaces) ____ yellow pages ____ Internet ____ word of mouth Example: How could this question be improved?
    45. Comparison of data collection methods Alreck & Settle (1995; 32) Alreck and Settle (1995:32)
    46. Sampling
    47. Sampling: Overview
      • Sampling terminology
      • What is sampling?
      • Why sample?
      • Sampling methods
      • Example: Shere Hite’s survey
    48. Sampling terminology
      • Target population
        • To whom you wish to generalise
      • Sampling frame
        • Those who have a chance to be selected
      • Sample
        • Those who were selected and responsed
      • Representativeness
        • The extent to which the sample is a good indicator of the target population
    49. What is sampling? “Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.” - Trochim (2006)
    50. Why sample?
      • Reduces cost, time, sample size etc.
      • If the sample is representative, the sample data allows inferences to be drawn about the total population.
    51. Representativeness of a sample depends on:
      • Adequacy of sampling frame
      • Sampling method
      • Adequacy of sample size
      • Response rate – both the % & representativeness of people in sample who actually complete survey
      It is better to have a small, representative sample than a large, unrepresentative sample.
    52. Sampling methods Probability sampling
      • Random
      • Systematic
      • Cluster
        • Multi-Stage Cluster
      Non-probability sampling
        • Quota
        • Convenience
        • Snowball
    53. Random/probability sampling
      • Each unit has an equal chance of selection
      • Selection occurs entirely by random chance
      • Also called representative sampling
      • Simple random sampling
      • Everyone in the target population has an equal chance of selection
      • Useful if clear study area or population is identified
      • Similar to a lottery:
        • List of names are assigned #s and randomly select #s of respondents
        • Randomly select # through table of random #s or by computer
    54. Systematic random sampling
      • Selecting without first numbering
      • Respondents (units) selected from a list/file.
      • Useful when survey population is similar e.g. List of students
      • Select sample at regular intervals from the population e.g., every 5 th person on a list, starting at a random number between 1 and 5
      • Stratified random sampling
      • Sub-divide population into strata (e.g., by gender, age, or location)
      • Then random selection from within each stratum
      • Improves representativeness
      • e.g., Telephone interviews using post-code strata
      • Non-random / non-probability
      • Also called purposive or judgemental sampling
      • Useful for exploratory research and case study research
      • Able to get large sample size quickly
      • Limitations include potential bias and non-representativeness
      • Convenience sampling
      • Sampling is by convenience rather than randomly
      • Due to time/financial constraints
      • e.g. surveying all those at a tourist attraction over one weekend
      • Purposive sampling
      Respondents selected for a particular purpose e.g., because they may be “typical” respondents
      • e.g., select sample of tourists aged 40-60 as this is the typical age group of visitors to Canberra
      • e.g., Frequent flyers to contact regarding service quality in an airline setting
      • Snowball sampling
      • Useful for difficult to access populations e.g., illegal immigratnts, drug users
      • Respondents recommend other respondents
      • e.g., in studying ecstasy users, gain trust of a few potential respondents and ask them to recommend the researcher to other potential respondents
    55. Sampling process
      • Identify target population and sampling frame
      • Select sampling method
      • Calculate sample size for desired power.
      • Maximise return rate
    56. Summary of sampling strategy
      • Identify target population and sampling frame
      • Selection sampling method
      • Calculate required sample size
      • Maximise return rate
    57. Sampling Example: Shere Hite ‘American Sexology’
    58. Hite's survey of American male-female relations (early 1980's)
      • Shere Hite ‘doyenne of sex polls’
      • Media furors & worldwide attention
      • 127-item questionnaire about marriage & relations between sexes
      • Sample: 4500 USA women, 14 to 85 years
      • Conclusion: Society and men need to change to improve lives of women
    59. Some of Hite’s findings about American women....
      • Only 13% married for 2+years were still in love
      • 70% married for 5+ years were having affairs...
        • usually more for 'emotional closeness’ than sex
        • 76% of these women did not feel guilty
      • 87% had a closer female friend than husband
      • 98% wanted “basic changes” to love relationships
      • 84% were emotionally unsatisfied
      • 95% reported emotional & psychological harassment from their men
    60. Some of the critical comments.... She goes in with prejudice & comes out with a statistic. The survey often seems merely to provide an occasion for the author’s own male-bashing diatribes. Hite uses statistics to bolster her opinion that American women are justifiably fed up with American men.
    61. Hite's response rate & selection bias
      • 100,000 questionnaires were sent to a variety of women’s groups (feminist organisations, church groups, garden clubs etc.)
      • 4,500 replied (4.5% return rate)
    62. “ We get pretty nervous if respondents in our survey go under 70%. Respondents to surveys differ from nonrespondents in one important way: they go to the trouble of filling out what in this case was a very long, complicated, and personal questionnaire.” - Regina Herzog, University of Michigan Institute for Social Research Hite's response rate & selection bias
      • Sample size – it's now big, it's how representative
      • Objectivity – watch out for manipulating the survey questions and results interpretation to suit your personal conjectures
      Lessons from Hite's male-female relations survey
    63. Survey Format Checklist
    64. Introduction or cover letter:
      • Few will read it without good prompting and being easy-to-read
      Instructions
      • Provides consistency - helps to ensure standard conditions across different administrations
      • Explain how to do the survey in a user-friendly manner
      Survey format checklist
    65. Cover letter / ethics statement Outline details of research project e.g.,:
      • Who are you? Are you bona fide?
      • Purpose of survey?
      • What's involved?
      • Explain any risks/costs/rewards
      • How will results be used?
      • Human ethics approval #
      • How is consent given / not given?
      • Voluntary - can choose not to continue anytime
      • More info: Complaints, how to obtain results, contact details etc.
    66. Instructions: Example
      • Group like questions together
      • Order effects:
        • Habituation
        • Fatigue
        • Minimise switching between response formats
      Survey format checklist
    67. Layout
      • Font (type, size)
      • No. of pages
      • Margins
      • Double vs. single-siding
      • Colour, etc.
      Survey format checklist
    68. Demographics
      • single section, usually at beginning or end of questionnaire
      • only include relevant questions
      Survey format checklist
      • Space for comments?
      • Indicate the end
      • Say thanks!
      • Pre-test & pilot-test
      Survey format checklist
    69. Pre- & pilot-testing
      • Pre-test items on convenient others - ask for feedback
      • Revise items e.g.,
        • Which don’t apply to everybody
        • Are redundant
        • Are misunderstood
        • Are non-completed
      • Reconsider ordering & layout
      • Pilot test on a small sample from the target population, analyse, & revise
    70. Ethical issues: How to treat respondents
      • Minimise risk / harm to respondents
      • Informed consent
      • Confidentiality / anonymity
      • No coercion
      • Minimal deceit
      • Fully debrief
      • Honour promises to provide respondents with research reports
      • Be aware of potential sources of bias/ conflicts of interest
      • Represent research literature fairly
      Ethical issues: Other
      • Don’t search data for pleasing findings
      • Acknowledge all sources
      • Don’t fake (or unfairly manipulate) data
      • Honestly report research findings
      Ethical issues: Other
    71. Levels of Measurement = Type of Data Stevens (1946)
    72. Levels of measurement
      • N ominal / Categorical
      • O rdinal
      • I nterval
      • R atio
    73. Discrete vs. continuous Discrete - - - - - - - - - - Continuous ___________
    74. Each level has the properties of the preceding levels, plus something more!
    75. Categorical / nominal
      • Conveys a category label
      • (Arbitrary) assignment of #s to categories
      e.g. Gender
      • No useful information, except as labels
    76. Categorical / nomimal example: Phrenological labels
    77. Ordinal / ranked scale
      • Conveys order , but not distance
      e.g. in a race, 1st, 2nd, 3rd, etc. or ranking of favourites or preferences
    78. Ordinal / ranked example: Ranked importance Rank the following aspects of the university according to what is most important to you (1 = most important through to 5 = least important)  Quality of the teaching and education  Quality of the social life  Quality of the campus  Quality of the administration  Quality of the university's reputation
    79. Interval scale
      • Conveys order & distance
      • 0 is arbitrary
      e.g., temperature (degrees C)
      • Usually treat as continuous for > 5 intervals
    80. Interval example: 8 point Likert scale
    81. Ratio scale
      • Conveys order & distance
      • Continuous, with a meaningful 0 point
      e.g. height, age, weight, time, number of times an event has occurred
      • Ratio statements can be made
      e.g. X is twice as old (or high or heavy) as Y
    82. Ratio scale: Time
    83. Why do levels of measurement matter? Different analytical procedures are used for different levels of data. More powerful statistics can be applied to higher levels
    84. Levels of measurement: Practice exam question Fill in all cells Descriptive Statistics Ratio Interval Ordinal / Rank Nominal / Categorical Graphs Examples Prop-erties Level
    85. Golden rule of data analysis Choose analysis techniques to match :
        • Research questions / hypotheses
        • Type (level) of data
    86. Levels of measurement and data analysis categorical & ordinal DVs  non-parametric interval & ratio DVs  parametric
    87. Levels of measurement and data analysis For more info, see an Inferential Statistics Decision-Making Tree
    88. Parametric statistics = procedures which estimate PARAMETERS of a population, usually based on the normal distribution
      • Any procedure which uses M , SD - e.g. t-tests, ANOVAs
      • Any procedure which uses r - e.g. bivariate correlation, linear regression
    89. Non-parametric statistics (Distribution-free Tests) = procedures which do not rely on estimates of population parameters
      • Any procedure which uses frequency - e.g. sign test, chi-squared
      • Any procedure which uses rank order - e.g. Mann-Whitney U test, Wilcoxon matched-pairs signed-ranks test
      • More powerful
      • More sensitive to violations of assumptions
      Parametric statistics
    90. Measurement Error
    91. Measurement error
      • Observed score =
      true score + measurement error
      • Measurement error =
      systematic error + random error
      • Any deviation from the true value caused by the measurement procedure.
    92. Sources of measurement error Non-sampling (e.g., unreliable or invalid tests) Sampling (e.g., non-rep. sample) Personal bias (e.g., researcher favours a hypothesis) Paradigm (e.g., Western focus on individualism)
    93. To minimise measurement error Use well designed measures:
      • Multiple indicators
      • Sensitive to target constructs
      • Clear wording on questions/instructions
    94. Reduce demand effects:
      • Train interviewers
      • Use standard protocol
      To minimise measurement error
    95. To minimise measurement error Maximise response rate:
      • Pre-survey contact
      • Minimise length / time / hassle
      • Offer rewards / incentives
      • Coloured paper
      • Call backs / reminders
    96. Ensure administrative accuracy
      • Set up efficient coding, with well-labelled variables
      • Check data
      To minimise measurement error
    97. Summary - 1
      • Survey research has developed into a popular research method since the 1940's.
      • A survey is a standardised stimulus designed to convert fuzzy psychological phenomenon into hard data.
    98. Summary - 2
      • Survey development - types of questions and response formats.
      • Sampling - probability & non-prob.
      • Levels of measurement & parametric / non-parametric stats
      • Ethical considerations
      • Sources of measurement error
    99. References Alreck, P. & Settle, R. (1995). The survey research handbook (2 nd ed.). New York: Irwin. Stevens, S.S. (1946). On the theory of scales of measurement. Science , 103 , 677-680. Trochim, W. M. K. (2006). Sampling . In Research Methods Knowledge Base . Wikipedia (2009). Shere Hite - Methodology .

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