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DESCRIPTIVERESEARCH
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MARKETING RESEARCH 103: LECTURE 3
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OUTLINE
Systematic Observation
Sampling
Standardized measurement
Survey
Sampling
Questionnaire Design (if possible) Measurement and Scaling (next class)
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Clearly-defined research question with theobjective of being descriptive
a structured research design guides thesampling and ensures control instandardizing what is being observed and
how it is recorded
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I. SYSTEMATIC OBSERVATION
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Time sampling
Situation sampling
Different locations (e.g., different places beer isconsumed: bar, student center, party etc.)
Sampling determines generalizability
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SAMPLING
TECHNIQUE
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STANDARDIZING OBSERVATION SHEETDate: 22 Aug 2007
Time Gender Type of Packaging P1 P2 P3 P4 P5 P6 P7 P8
1:00 PM
1:05 PM
1:10 PM
1:15 PM
Key
variables 1 2 3 4 5
P1approximate age
group
Below 12 12 to 18 19 to 25 26 to 35 Above 35
P2
attire uniform business casual sporty Dressy(for
mal)
P3 bag none Small medium large Extra large
P4 no. of companions none 1 to 2 3 to 5 6 to 10 Above 10P5 quantity 1 2 3 4 Above 4
P6 Other purchases none snacks meal Non food Others
P7Size of drink Extra small small medium large Extra large
P8
Type of drink Soft/fizzy fruit juice/
fruit-based
Tea/coffee Isotonic
drinks
Milk-based
Observer: GeorgeLocation: Borders
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Nominal scale (checklist)
e.g., Brand of shoes, hair color, cellular phone or not,
with accompany or not.
Summarizing nominal data in terms of frequency,proportion, or percentage of instances in each of
the several categories.
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MEASUREMENT SCALE
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Physical actions
Verbal behavior
Expressive behavior
Spatial relations and locations
Temporal patterns
Physical objectsVerbal and pictorial records
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WHATCANBEOBSERVED?
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Cannot observe motives, attitudes or intentions
Inter-observer reliability
Can sometimes be time consuming
Ethical concern: violation of privacy?
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LIMITATIONSOF OBSERVATIONAL
METHODS
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II. SURVEY
Rationale: To enable quantification of opinions, preferences, etc.
Format: Open-ended or scaled questions, scenarios, ongoingversus one-shot, longitudinal vs. cross-sectional
Tools: mail, telephone, direct intercept, email, internet
Upsides/Downsides: (methodological and implementation)?
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2.1 SAMPLING
A. Sampling jargons and design process
B. Sampling Methods
C. Sampling Size
D. Sampling errors vs. Non-sampling errors
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A. SAMPLINGJARGON
Population
If the goal is to determine the average number of jobinterviewsafter graduation for the students who havetaken Marketing Research Class, then the entire body ofstudents who have taken 103 would be thepopulation
Sampling Frame:A list of students from which a sample isto be chosen (e.g., registration record)
Sampling: the selection of a fraction of the population
Census:all members of the population are measured
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Sampling Frame
ElementOne student
Sample
100 students
Registrars list of currently enrolled students
Population
All students on SMU campus
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SAMPLINGDESIGNPROCESS
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
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B. SAMPLING METHODS
Probability sampling
Simple random
sampling
Proportionate
Disproportionate
Cluster
Non-probabilitysampling
Convenience sampling
Quota sampling
Judgment sampling
Snowball sampling
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SIMPLE RANDOM SAMPLING
Each element in the population has a knownand equalprobability of selection, or
Each possible sample of a given size (n) has a knownand equalprobability of being the sample actuallyselected.
Randomly pickn(e.g., 5) units out ofN is statisticallyequivalent to enumerating all possible samples of size nandpicking one of them at random
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SIMPLE RANDOM SAMPLING (SRS)
Procedure
Number everyone in the sampling frame
Picknelements randomly
Generated by computer or random number table
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RANDOM NUMBERS GENERATORIN SPSS
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ADVANCED PROBABILITYSAMPLING METHODS
Why do I need anything besides SRS?
Suppose our population of interest is
allmarketing students, lets say consisting of 700undergraduates (1/5 freshman, 2/5 sophomore,1/5 juniors and 1/5 seniors).
If we take a SRS of 140 students then we mayget a sample which looks like this: Seniors (28),
Juniors (28), Sophomores (56) and Freshman (28)
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PROPORTIONATE STRATIFIEDRANDOM SAMPLING
The population is divided into strata on the basis of anappropriate variable (related to the outcome ofinterest)
Units within each stratum are alike and units acrossstrata are different
The sample size from each stratum is proportional tothe size of the stratum
E.g., N=6000, 2 strata with sizes 2000 and 4000.
In a sample of 120, we sample 40 and 80 respectively from the
2 strata
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OTHERADVANCED METHODS
Disproportionate stratified random sampling
May want to over-sample small stratum or one that is
highly variable
Cluster Sampling
Population is naturally divided into clusters and clustersare selected at random.
All or some of the units within selected clusters aremeasured (e.g., selected blocks or counties)
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NON-PROBABILISTIC SAMPLING METHODS
Conveniencesampling At the convenience of the experimenter (at the corner, at the
supermarket)
Quota sampling
Population is divided into segments on the basis of certain controlvariable
A quota of units to be selected from each segment is determined by theexperimenter
Sampling is done in each segment until the quota is filled
Better than convenience sampling in that you are at least guaranteedbalance in the population with respect to the control variable
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NON-PROBABILISTIC SAMPLING METHODS
Judgment sampling
Snowball sampling
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CLASSIFICATIONOF SAMPLING TECHNIQUES
Sampling Techniques
NonprobabilitySampling Techniques ProbabilitySampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
ProportionateStratified Sampling
ClusterSampling
Simple RandomSampling
DisproportionateStratified Sampling
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C. DETERMINE SAMPLE SIZE
_
_
Common notation:
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95% CONFIDENT
XL
_
XU
_
X
_
0.475 0.475
Estimation: Population vs. Sample
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CONFIDENCE INTERVAL
Definitions:
Confidence interval: The confidence interval is the rangeinto which the true population parameter will fall, assuming agiven level of confidence.
Confidence level: The confidence level is the probability thata confidence interval will include the population parameter.
Precision level: When estimating a population parameter byusing a sample statistic, theprecision level is the desired widthof the estimating interval. This is the maximum permissibledifference between the sample statistic and the populationparameter.
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CONFIDENCE INTERVAL APPROACHTOESTIMATE SAMPLE SIZE
1. Need to know how to construct ConfidenceInterval (CI)
We will see how it depends on n (your sample size)using SRS
2. Derive the sample size (n) that is necessary toconstruct a confidence interval with a desired
width (H)
Need to know the desired confidence level (?%) anddesired width (H)
E.g. want to be 95% confident that the market shareis +/- 3% around the true value (+/-3% is the width)
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CONFIDENCE INTERVALFORUNKNOWNPARAMETERQ
CI = (Best Guess) +/- (Certain #) SE(Best Guess)
What makes a given statistic
T the best guess? And how
do I compute the SE of it?
The certain # depends on
how confident you want
to be
For the two examples we will
talk about today, i.e. unknown
is either a mean or an unknown
proportion p, SE = n/
This is precision level H. Notice, for a given level of confidence
and a given value of (SD in the population), we can
solve for the value ofn that gives you your desired H.
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Best Guess for Population Mean
x ith
iIn general, let be the measurement on the person/household/firm etc.
Then,
m = 1N Xi
i=1
N
X=1
nX
i
i=1
n
Since we typically do not know we have to estimate it(best guess for population mean would be sample mean:
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SE OF BEST GUESS
SE of X =
.
In the CI calculations, simply replace 2 with s2
.
s 2 = 1n - 1
Xi- X( )
2
i=1
n
n/
Since population variance is often unknown,
an unbiased estimate of the population variance is the sample variance definedas
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Example 1: Confidence Interval for
We can construct a 100(1- q)% confidence interval for population mean by computing
where
= the sample mean
= the sample standard deviation
n = the sample size
= 1.645, 1.96, 2.575 for a 90%, 95% and 99% confidenceinterval
x
s
qz
What we will talk about later is the inverseproblem. What n is necessary so that zq*s/ =H?n
XZqs
n
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APPLIED EXAMPLE 1
We are interested in the mean starting income of all SMU graduates sowe go out and obtain a SRS of 100 SMU graduates. The mean startingincome in our sample is $115,000 with standard deviation equal to$20,000. Construct a 95% confidence interval (CI) of the true
population mean starting income of all SMU graduates.
Answer:
)100
000,2096.1000,115,
100
000,2096.1000,115(
= (111080 , 118920)
X
s
qz n
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EXAMPLE 2: CONFIDENCE INTERVALFORPROPORTIONP
A proportion is a mean where each observation is a 0 or 1, so we cancompute a CI for a population
We can construct a 100(1- q)% CI for population meanp by computing
where
= the sample proportion
n = the sample size
= 1.645, 1.96, 2.575 for a 90%, 95% and 99% confidenceinterval
p
qz
pZqp(1- p)
n
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APPLIED EXAMPLE 2
We are interested in the proportion of students who havetried a new soup. We decide to go out and obtain an SRS of400 students and ask them if they have tried the new soup.
Suppose 20% of the 400 students say they have tried it.Construct a 90% confidence interval (CI) of the truepopulation market share.
Answer:
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THE SIZEOFTHE SAMPLE (MEAN)
Question: What sample size do I need so that if I constructa 100(1-q)% confidence interval it will be an interval with
width H?
Compute Size of the Sample
Determine q, the desired CI you want, e.g., = 1.645 is a 90% CI
Determine the interval width you want, e.g.,
Compute an estimate of the standard deviation of the population of interest,denoted bys.
Compute the necessary sample size
0.10Z
5000$
n =Zq
2s
2
H2
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Where do you get s from?
Historical data
Worst case scenario (largest possible s).
Pre-test
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EXAMPLE
Suppose you want a 95% CI for mean graduatestarting salaries such that the interval will have
width $5000. Using the results of the previousexample where s= $20,000, how many graduatesdo I need to obtain in my SRS to obtain thisdesired width?
Answer: 2
22
5000
000,2096.1 n
61.5Note, you need to do this forevery question in your surveyand then take the maximum
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THE SIZEOFTHE SAMPLE (PROPORTION)
Compute Size of the Sample
Determine qthe desired CI you want, e.g., = 1.645
is a 90% CI Determine the interval width you want H, e.g.,
Compute an estimate of population proportion
Compute the necessary sample size:
0.10Z
%3
p
n =Zq
2p(1- p)
H2
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An approximate n for proportions (assuming 95% confidence)in a worst-case scenario is :
n = 1/H2
WHEREDOYOUGETANESTIMATEOFP?
The largest sampling error occurs when p =
0.51
SE
P0
p(1-p)
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EXERCISE
Estimate the sample size for the following scenario:
95% CI
Interval width (H) = Assume 50% of people like the new ad copy
%3
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D. SAMPLINGVS. NON-SAMPLING ERRORS
Total Survey Error
Non-Sampling
Error
Non-Response
Error
Response
Error
Random
Sampling Error
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SOURCESOF ERRORSIN INFORMATIONFROM RESPONDENTS
POPULATION RESPONDENT INTERVIEWER
Inaccuracy in
response
Ambiguity
of answer
Ambiguity ofquestion
Interviewererror
sample Question
Answer
Samplingerror
Non-responsedue to refusalsor not-at-home
The method you select usually leadsto trade-offs in these areas.
Mail, Phone, Internet, Personal Interview.
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RANDOM SAMPLING ERROR
Sample selected is not a perfect representation oftest population
Can be controlled by:
Random sampling procedure
Quota sampling
Increase sample size
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NON-RESPONSE ERRORS
A non-responseerroroccurs when units selected aspart of the sampling procedure do not respond in
whole (called unit non-response) or in part (calleditem non-response)
If non-respondents are not different from those that didrespond, there is NOT a non-response error
It only happens when the existence of non-respondentscauses respondents to be poor representatives of totalsample
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RESPONSE ERRORS
A Response or data erroris any systematic bias thatoccurs during data collection, analysis orinterpretation
Respondent error
Interviewer bias
Recording errors
Poorly designed questionnaires
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DILEMMA: LARGEVS. SMALL
Large Sample Size: Reduce random sampling error
Small Sample Size: Better interviewer controls,higher response rate and higher accuracy
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ATT STUDY
Purpose: compare a list of research procedures in terms of themagnitude of various sampling and non sampling errors.
4 methods of survey administration (same questionnaire):
mail telephone interviews
personal interviews
questionnaire drop-off
2 methods of prior notification:
mail and telephone
2 methods of follow up
mail and telephone
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SPECIAL CHARACTERISTICSOFTHISSTUDY
Survey questions:
telephone usage
attitudes in telephone usage
organizational characteristics
Telephone usage for each company were obtained from ATT forboth respondents and non respondents (allowing for assessing nonresponses and response error)
Comparisons focused on the following three questions:
Company's average monthly telephone bill over the last 3 months.
Quantity of companys telephone lines.
Quantity of companys telephone sets (stations).
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FINDING 1:
RandomSampling Error
~5% Non-SamplingError
~95%
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RANDOM SAMPLING ERRORVS. NON-
SAMPLING ERROR
What does the 95% mean?Given random sampling in place, the need to get
large representative samples is likely to have been
exaggerated
What should we focus on instead?Obtain smaller samples so as to better spend
resources on: Interviewer control Performing pre-tests of questionnaires Trying to achieve greater reliability of survey
responses
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FINDING 2:
>
>
>
Higher valuessuggest that
respondents arelarger companiesthan non-respondents
Respondentsmore economicallyupscale than non-respondents
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DIRECTIONOF RESPONSE & NON-
RESPONSE BIASES
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CONCLUSIONS
Given random sampling, focus on minimize non-sampling error:
Response Error
Control interviewer-respondent interface and/or questionnaireformat
Enough time
Non-response error
Ensure respondents are representative of the selected sample
Check whether respondents represent population
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REVIEW:
Understand SRS and why you might depart from SRS
Understand the confidence approach to estimate sample
size Understand various errors in sampling
The proof of the pudding is in the eating. By a small
sample we may judge of the whole piece.
Miguel de Cervantes Saveedra
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2.2 QUESTIONNAIRE DESIGN
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Specify the Information Needed
Design the Question to Overcome the Respondents Inability andUnwillingness to Answer
Determine the Content of Individual Questions
Decide the Question Structure
Determine the Question Wording
Arrange the Questions in Proper Order
Reproduce the Questionnaire
Specify the Survey Administration Method
Identify the Form and Layout
Eliminate Bugs by Pre-testing
Process
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STEP 0: MAKESURETHEDATAYOUGETWILLBEUSEFUL
Population has been defined correctly
Sample is representative of the population
Respondents selected to be interviewed are availableand willing to cooperate
Respondents understand the question and have
knowledge Motivate them to provide information (incentives)
response rates
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STEP 1: WHATINFORMATIONTOCOLLECT?
Translating research objectives into information
requirements
Question relevancy
Use backward approach- What would you do with the
answer if you knew it?
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STEP 2: COMPARISONOF SURVEYADMINISTRATION METHODS
Criteria Best Second Third
VersatilityNumber of Questions
Amount /Var of info
Presentation of stimuli
Personal
Personal
Internet
Mail/Internet
Phone/Internet
Personal
Phone
Mail
Mail/PhoneResponse Time Internet/Phone Personal Mail
Cost Internet/Mail
$1-$10
Phone
$15-$30
Personal
$50+
AccuracySampling Control
Supervisory control
Opport. for clarification
PersonalInternet/Mail
PersonalPhonePhone
Phone
Mail/InternetPersonal
Mail/Internet
Respondent Convenience Internet/Mail Telephone Personal
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STEP 3: DETERMINETHE SPECIFICQUESTIONS
Is this question necessary?
Are several questions needed instead of one?
Do you think Coca-Cola is a tasty and refreshing soft drink?
Why do you shop at Nike Town?
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STEP 4: OVERCOME INABILITYORUNWILLINGNESSTO ANSWER
Inability to answer
Filter/screening questions
Dont know option
Provide context to facilitate the memory
Unwillingness to answer
Reduce the effort (recall vs. recognition task, rating vs. rank order)
Justify purpose (legitimate reason)
Sensitive information
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STEP 5: CHOOSE QUESTION STRUCTURE
Unstructured: Open-response questions
What do you think of people who patronize discount department
stores?
Structured: Closed-response questions
Multiple choice questions
Dichotomous questions
Scales
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Response Formats Open-Ended
Advantages:
Respondents can give general (unbiased) reactions toquestions such as Why do you say that brand X isbetter?
Response given in real world terminology. i.e.,consumers own language
Can help interpret closed ended data.
e.g. Why was color the most important product attribute?
May suggest additional alternatives to be used in closed-ended questions.
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Response Formats Open-Ended
Drawbacks:
Often not good for self-administered surveys
Answers depend on respondent ability toarticulate
Interviewer bias can be a problem
Post-coding is tedious
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Response Formats Closed-Ended
The respondent is provided with pre-determineddescriptions and selects one or more of them
Advantages:
easy to use in field
less threatening for respondent
ability to reduce interviewer bias
simple to code and enter data listing responses may jog respondents memory
cheaper to administer
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Response Formats Closed-Ended
Disadvantages:
Usually requires pre-testing
Presumes the list of responses is complete
Exploratory research using open-ended questions
can be used to suggest the items and scales in thequantitative survey
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STEP 6: DETERMINETHE QUESTIONWORDING
Be specific (who, what, when, where etc.)
Which brand of shampoo do you use?
Use simple, conventional (ordinary) words Do you think the distribution of soft drinks is adequate?
Avoid ambiguity (usually, normally, often etc.)
In a typical month, how often do you shop in department stores?(Never, occasionally, sometimes, often, regularly)
Dual Statements: agreement and disagreement, like anddislike
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Question wording-contd
Avoid leading and biasing questions
Do you think that we should buy imported goods when that would put
local labor out of work? Is Colgate your favorite toothpaste?
Avoid implicit alternatives
Do you like to fly when traveling short distances?
Avoid estimates
What is the annual per capita expenditure on groceries in yourhouseholds?
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STEP 7: ARRANGEIN PROPERORDER
Responses to earlier questions may bias responses tolater questions
Funnel Approach
asking specific questions first may bias responses to general questionsasked later
What considerations are important to you in selecting a department store?
In selecting a department store, how important is convenience of location?
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EXAMPLEOF ORDERBIAS:DOES THE QUESTION CREATE THE ANSWER?
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1. No question asked 2.8
2. Asked only about advantages 16.7
3. Asked only about disadvantages 0.0
4. Asked about both advantages anddisadvantages
5.7
Percentage of Respondents
Very Much Interested inBuying New ProductQuestions Preceding Buying Interest Question
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STEP 8: IDENTIFYTHE LAYOUT
Layout Guidelines
Open the survey with an easy and non-threatening question
Smooth and logical flow
From general to specific
Sensitive or difficult questions should not be placed at thebeginning of the questionnaire
Aesthetically pleasing: Font size, paragraph distance, thenumber of questions in one page, questions are numberedclearly
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STEP 9-10: PRE-TESTINGANDCORRECTING PROBLEMS
Pre-testing specific questions
Variation
Meaning
Task difficulty
Respondent interest and attention
Response latencies
Question saliency
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REVIEW:
Understand the advantages and disadvantages of eachtype of measurement scale
Conduct a mini
market research
when designing aquestionnaire
Search for and use proven questions
Pretest questionnaire
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HOMEWORK
Assigned reading from course pack (Harvard SchoolSurvey, Causal Inference)
Textbook reading (chapter 8, 9, 10)
Group Assignment 1 due on Friday night, hardcopy, print,in my mailbox
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