Things to Think About When You Want To Do a Survey

Things to Think About When You Want To Do a Survey
What do you want to know? How can you find out what you need to know? Similar/same surveys Can help in design Can help validate your results May obviate the need for the study How best to reach people at JMU?

Things to Think About When You Want To Do a Survey
Constraints Money/time Sample vs. population Mailing/Bulk

Things NOT to do Ceiling and floor effects Too Long (dining survey)
Sensitive Questions Near the Front Double-barreled questions Measure more than one item at the same time Confounds responses and interpretation. Ceiling and floor effects

Things NOT to do Indeterminate Time (alumni survey question on volunteerism) Response sets not matched to question or question type. Bias Blatant Subtle

Things NOT to do “Don’t knows,” and “Neutrals,” and “no opinions” that allow “weaseling out” - Lack of “Don’t knows,” and “Neutrals,” and “no opinions” where they could have important meaning. - Ceiling and floor effects in responses

Examples and Discussion
Study of Coffee Flavors on Visitors to a Coffee Shop 8 am 272 Questions

Study of potential clients for new clothing line beginning with questions about measurements and interests in “stretchable fabrics”

Do you believe that HOV lanes help reduce congestion and can help reduce pollution? Yes No High occupancy vehicle lane: In transportation engineering and transportation planning, a high-occupancy vehicle lane (or HOV lane) is a lane reserved for vehicles with a driver and one or more passengers. These lanes are also known as carpool lanes, commuter lanes, diamond lanes, express lanes, and transit lanes.

Do you plan on voting for the independent candidate? Yes No

How often do you use a pencil? 7 times 6 times 5times 4 times 3 times 2 times 1 time

How often do you attend soccer games? Very true Partly true Not true False Entirely False

Most smart people agree that the HOV lane is vital to reducing congestion. What do you think? I agree I disagree

Hybrid vehicles use less gasoline, make less noise, reduce owner costs. Please check which of the following hybrid programs you support. Tax incentives for owners Special HOV permisssions during peak hours None How use after

Was your meal as good as you expected? Yes No Don’t know Alumni survey question about diversity in workplace.

How do you feel about euthanasia? Support Neutral Oppose Don’t know

Was the NATO embargo of Iraq in the 1990s effective? Yes No Don’t know

Please rate your dining experience. Excellent Terrible

Develop response categories that are mutually exclusive Problem: From which one of these sources did you first learn about the tornado in Derby? Radio TV Someone at work While at home While traveling to work 18

During this most recent ride, would you say that your seatbelt was fastened… All the time, that is, every minute the car was moving Almost all the time Most of the time About half the time Less than half the time Not all during the time the vehicle was moving 19

Use cognitive design techniques to improve recall Problem: We would like to ask about the most recent time you drove or rode anywhere in an automobile or other vehicle such as a pickup or van. During this most recent ride, would you say that your seatbelt was fastened… All the time Almost all the time Most of the time About half the time Less than half the time Not at all 20

Measurement in Marketing Research

Basic Question-Response Formats
Open-ended Close-ended Scaled-response

Open-Ended Unprobed Open-ended question: presents no response options to the respondent Unprobed format: seeks no additional information Advantages: Respondent frame of reference; allows respondent to use his or her own words Disadvantages: Difficult to code and interpret Respondents may not give complete answers

Open-Ended Probed Open-ended question: presents no response options to the respondent Probed format: includes a response probe instructing the interviewer to ask for additional information or answer clarification Advantages: Elicits more-complete answers Respondent frame of reference Disadvantages: Difficult to code, analyze, and interpret

Close-Ended Dichotomous Close-ended question: provides a set of answers from amongst which the respondent must choose Dichotomous: has only two response options, such as “yes” - “no”; “have” – “have not”; “male” – “female” Advantages: Simple to administer, code, analyze Disadvantages: May oversimplify response options May be in researcher frame of reference

Close-Ended Multiple Category Close-ended question: provides a set of answers from amongst which the respondent must choose Multiple response: has more than two answer choices; must have “mutually exclusive” and “collectively exhaustive” answer set Advantages: Allows for broad range of possible responses Simple to administer, code, and analyze Disadvantages: May be in researcher frame of reference May not have all appropriate respondent answer options

Scaled-Response Unlabeled Scaled-response question: uses a scale (parts:a statement, instructions, a response format) to measure respondent feeling, judgment, perception … Unlabeled: uses a scale that may be purely numerical (no words/phrases) or only the endpoints of the scale are identified Advantages: Allows for degree of intensity to be expressed without researcher options Simple to administer, code, and analyze Disadvantage: Scale may not reflect respondents’ view

Scaled-Response Labeled Scaled-response question: uses a scale (parts: a statement, instructions, a response format) to measure respondent feeling, judgment, perception … Labeled: a scale where all choices/positions are identified with some descriptive word/phrase Advantages: Allows for degree of intensity to be more clearly expressed Simple to administer, code, and analyze More consistency in responses Disadvantage: Scale choices more limited or too detailed

Considerations in Choosing a Question-Response Format
The nature of the property being measured Gender=dichotomous; liking for chocolate=scale Previous research studies Use format of previous study if comparing The data collection mode Cannot use some scales on the phone The ability of the respondent Kids can only relate to certain types of visual scales The level of analysis points to scale type needed

Basic Concepts in Measurement
Measurement: determining how much of a property is possessed; numbers or labels are then assigned to reflect the measure Properties: specific features or characteristics of objects, persons, or events that can be used to distinguish them from others Objective properties are physically observable or verifiable Subjective properties are mental constructs

Scale Characteristics Determine the Level of Measurement (Level of Data)
Nominal data: The use of a descriptor, name, or label, to stand for each “unit” on the scale: “yes” “no”, “male” “female”,etc. Ordinal data: Objects, persons, events are placed in rank order on some characteristic in a specific direction. Zero and distance have no meaning; rank 1, 2, 3, etc. Interval data: Units of distance have meaning. There is an arbitrary zero point. Examples are temperatures in degrees Fahrenheit or Celsius; map distance Chicago to ? Ratio data:Multiples have meaning. There is an absolute or natural zero point. Examples: the Kelvin temperature scale, sales/costs in $, market share in %

Primary Scales of Measurement
7 3 8 Scale Nominal Numbers Assigned to Runners Ordinal Rank Order of Winners Interval Performance Rating on a 0 to 10 Scale Ratio Time to Finish, in Seconds Third place Second First Finish 8.2 9.1 9.6 15.2 14.1 13.4

Primary Scales of Measurement
Table 8.1

A Classification of Scaling Techniques
Figure 8.2 Likert Semantic Differential Stapel Scaling Techniques Noncomparative Scales Comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort and Other Procedures Continuous Rating Scales Itemized Rating Scales

A Comparison of Scaling Techniques
Comparative scales involve the direct comparison of stimulus objects. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties. In noncomparative scales, each object is scaled independently of the others in the stimulus set. The resulting data are generally assumed to be interval or ratio scaled.

Relative Advantages of Comparative Scales
Small differences between stimulus objects can be detected. Same known reference points for all respondents. Easily understood and can be applied. Involve fewer theoretical assumptions. Tend to reduce halo or carryover effects from one judgment to another.

Relative Disadvantages of Comparative Scales
Ordinal nature of the data Inability to generalize beyond the stimulus objects scaled.

Comparative Scaling Techniques Paired Comparison Scaling
A respondent is presented with two objects and asked to select one according to some criterion. The data obtained are ordinal in nature. Paired comparison scaling is the most widely-used comparative scaling technique. With n brands, [n(n - 1) /2] paired comparisons are required. Under the assumption of transitivity, it is possible to convert paired comparison data to a rank order.

Obtaining Shampoo Preferences Using Paired Comparisons
Instructions: We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of shampoo you would prefer for personal use Recording Form: aA 1 in a particular box means that the brand in that column was preferred over the brand in the corresponding row. A 0 means that the row brand was preferred over the column brand. bThe number of times a brand was preferred is obtained by summing the 1s in each column.

Preference for Toothpaste Brands Using Rank Order Scaling
Brand Rank Order 1. Crest _________ 2. Colgate _________ 3. Aim _________ 4. Gleem _________ 5. Sensodyne _________ 6. Ultra Brite _________ 7. Close Up _________ 8. Pepsodent _________ 9. Plus White _________ 10. Stripe _________ Form

Importance of Bathing Soap Attributes Using a Constant Sum Scale
Instructions On the next slide, there are eight attributes of bathing soaps. Please allocate 100 points among the attributes so that your allocation reflects the relative importance you attach to each attribute. The more points an attribute receives, the more important the attribute is. If an attribute is not at all important, assign it zero points. If an attribute is twice as important as some other attribute, it should receive twice as many points.

Importance of Bathing Soap Attributes Using a Constant Sum Scale
Fig. 8.5 cont. Form Average Responses of Three Segments Attribute Segment I Segment II Segment III 1. Mildness 2. Lather 3. Shrinkage 4. Price 5. Fragrance 6. Packaging 7. Moisturizing 8. Cleaning Power Sum 8 2 4 17 3 9 7 53 19 5 20 13 60 15 100

Noncomparative Scaling Techniques
Respondents evaluate only one object at a time, and for this reason non-comparative scales are often referred to as monadic scales. Non-comparative techniques consist of continuous and itemized rating scales.

Continuous Rating Scale
Respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. The form of the continuous scale may vary considerably. How would you rate Sears as a department store? Version 1 Probably the worst I Probably the best Version 2 Probably the worst I Probably the best Version 3 Very bad Neither good Very good nor bad Probably the worst I Probably the best

Itemized Rating Scales
The respondents are provided with a scale that has a number or brief description associated with each category. The categories are ordered in terms of scale position, and the respondents are required to select the specified category that best describes the object being rated. The commonly used itemized rating scales are the Likert, semantic differential, and Stapel scales.

Likert Scale The Likert scale requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects. Strongly Disagree Neither Agree Strongly disagree agree nor agree disagree 1. Sears sells high quality merchandise X 3 4 5 2. Sears has poor in-store service X 3 4 5 3. I like to shop at Sears X 4 5 The analysis can be conducted on an item-by-item basis (profile analysis), or a total (summated) score can be calculated. When arriving at a total score, the categories assigned to the negative statements by the respondents should be scored by reversing the scale.

Semantic Differential Scale
The semantic differential is a seven-point rating scale with end points associated with bipolar labels that have semantic meaning. SEARS IS: Powerful --:--:--:--:-X-:--:--: Weak Unreliable --:--:--:--:--:-X-:--: Reliable Modern --:--:--:--:--:--:-X-: Old-fashioned The negative adjective or phrase sometimes appears at the left side of the scale and sometimes at the right. This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels. Individual items on a semantic differential scale may be scored on either a -3 to +3 or a 1 to 7 scale.

A Semantic Differential Scale for Measuring Self- Concepts, Person Concepts, and Product Concepts
1) Rugged :---:---:---:---:---:---:---: Delicate 2) Excitable :---:---:---:---:---:---:---: Calm 3) Uncomfortable :---:---:---:---:---:---:---: Comfortable 4) Dominating :---:---:---:---:---:---:---: Submissive 5) Thrifty :---:---:---:---:---:---:---: Indulgent 6) Pleasant :---:---:---:---:---:---:---: Unpleasant 7) Contemporary :---:---:---:---:---:---:---: Obsolete 8) Organized :---:---:---:---:---:---:---: Unorganized 9) Rational :---:---:---:---:---:---:---: Emotional 10) Youthful :---:---:---:---:---:---:---: Mature 11) Formal :---:---:---:---:---:---:---: Informal 12) Orthodox :---:---:---:---:---:---:---: Liberal 13) Complex :---:---:---:---:---:---:---: Simple 14) Colorless :---:---:---:---:---:---:---: Colorful 15) Modest :---:---:---:---:---:---:---: Vain

Stapel Scale The Stapel scale is a unipolar rating scale with ten categories numbered from -5 to +5, without a neutral point (zero). This scale is usually presented vertically. SEARS X HIGH QUALITY POOR SERVICE -4X The data obtained by using a Stapel scale can be analyzed in the same way as semantic differential data.

Basic Noncomparative Scales
Characteristics Examples Advantages Disadvantages Continuous Rating Place a mark on a continuous line Reaction to TV commercials Easy to construct Scoring can be cumbersome unless computerized Itemized Rating Scales Likert Scale Degrees of agreement on a 1 (strongly disagree) to 5 (strongly agree) scale Measurement of attitudes Easy to construct, administer, and understand More time - consuming Semantic Differential Seven point scale with bipolar labels Brand, product, and company images Versatile Controversy as to whether the data are interval Stapel Unipolar ten point scale, 5 to +5, witho ut a neutral point (zero) and images administer over telephone Confusing and difficult to apply Table 9.1

Summary of Itemized Scale Decisions
Table 9.2 1) Number of categories Although there is no single, optimal number, traditional guidelines suggest that there should be between five and nine categories 2) Balanced vs. unbalanced In general, the scale should be balanced to obtain objective data 3) Odd/even no. of categories If a neutral or indifferent scale response is possible for at least some respondents, an odd number of categories should be used 4) Forced vs. non-forced In situations where the respondents are expected to have no opinion, the accuracy of the data may be improved by a non-forced scale 5) Verbal description An argument can be made for labeling all or many scale categories. The category descriptions should be located as close to the response categories as possible 6) Physical form A number of options should be tried and the best selected

Balanced and Unbalanced Scales
Jovan Musk for Men is: Jovan Musk for Men is: Extremely good Extremely good Very good Very good Good Good Bad Somewhat good Very bad Bad Extremely bad Very bad

Rating Scale Configurations
-3 -1 +1 +2 -2 +3 Cheer Cheer detergent is: 1) Very harsh Very gentle 2) Very harsh Very gentle 3) . Very harsh . . Neither harsh nor gentle . Very gentle 4) ____ ____ ____ ____ ____ ____ ____ Very Harsh Somewhat Neither harsh Somewhat Gentle Very harsh Harsh nor gentle gentle gentle 5) Very Neither harsh Very harsh nor gentle gentle Fig. 9.2

Some Unique Rating Scale Configurations
Thermometer Scale Instructions: Please indicate how much you like McDonald’s hamburgers by coloring in the thermometer. Start at the bottom and color up to the temperature level that best indicates how strong your preference is. Form: Smiling Face Scale Instructions: Please point to the face that shows how much you like the Barbie Doll. If you do not like the Barbie Doll at all, you would point to Face 1. If you liked it very much, you would point to Face 5. Fig. 9.3 Like very much Dislike very much

Some Commonly Used Scales in Marketing
Table 9.3 CONSTRUCT SCALE DESCRIPTORS Attitude Importance Satisfaction Purchase Intent Purchase Freq Very Bad Not all All Important Very Dissatisfied Definitely will Not Buy Never Bad Not Important Dissatisfied Probably Will Not Buy Rarely Neither Bad Nor Good Neutral Neither Dissat Nor Satisfied Might or Might Not Buy Sometimes Good Important Satisfied Probably Will Buy Often Very Good Very Important Very Satisfied Definitely Will Buy Very Often

Scale Evaluation Fig. 9.5 Discriminant Nomological Convergent
Test/ Retest Alternative Forms Internal Consistency Content Criterion Construct Generalizability Reliability Validity Scale Evaluation

Potential Sources of Error on Measurement
1) Other relatively stable characteristics of the individual that influence the test score, such as intelligence, social desirability, and education. 2) Short-term or transient personal factors, such as health, emotions, and fatigue. 3) Situational factors, such as the presence of other people, noise, and distractions. 4) Sampling of items included in the scale: addition, deletion, or changes in the scale items. 5) Lack of clarity of the scale, including the instructions or the items themselves. 6) Mechanical factors, such as poor printing, overcrowding items in the questionnaire, and poor design. 7) Administration of the scale, such as differences among interviewers. 8) Analysis factors, such as differences in scoring and statistical analysis.

Reliability Reliability can be defined as the extent to which measures are free from random error, XR. If XR = 0, the measure is perfectly reliable. In test-retest reliability, respondents are administered identical sets of scale items at two different times and the degree of similarity between the two measurements is determined. In alternative-forms reliability, two equivalent forms of the scale are constructed and the same respondents are measured at two different times, with a different form being used each time.

Reliability Internal consistency reliability determines the extent to which different parts of a summated scale are consistent in what they indicate about the characteristic being measured. In split-half reliability, the items on the scale are divided into two halves and the resulting half scores are correlated. The coefficient alpha, or Cronbach's alpha, is the average of all possible split-half coefficients resulting from different ways of splitting the scale items. This coefficient varies from 0 to 1, and a value of 0.6 or less generally indicates unsatisfactory internal consistency reliability.

Validity The validity of a scale may be defined as the extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random error. Perfect validity requires that there be no measurement error (XO = XT, XR = 0, XS = 0). Content validity is a subjective but systematic evaluation of how well the content of a scale represents the measurement task at hand. Criterion validity reflects whether a scale performs as expected in relation to other variables selected (criterion variables) as meaningful criteria.

Validity Construct validity addresses the question of what construct or characteristic the scale is, in fact, measuring. Construct validity includes convergent, discriminant, and nomological validity. Convergent validity is the extent to which the scale correlates positively with other measures of the same construct. Discriminant validity is the extent to which a measure does not correlate with other constructs from which it is supposed to differ. Nomological validity is the extent to which the scale correlates in theoretically predicted ways with measures of different but related constructs.

Data Collection in the Field, Response Error, and Questionnaire Screening

Nonsampling Error in Marketing Research
Nonsampling (administrative) error includes All types of nonresponse error Data gathering errors Data handling errors Data analysis errors Interpretation errors

Possible Errors in Field Data Collection
Field worker error: errors committed by the persons who administer the questionnaires Respondent error: errors committed on the part of the respondent

Nonsampling Errors Associated
With Fieldwork

Field-Worker Errors Intentional Intentional field worker error: errors committed when a fieldworker willfully violates the data collection requirements set forth by the researcher Interviewer cheating: occurs when the interviewer intentionally misrepresents respondents. May be caused by unrealistic workload and/or poor questionnaire Leading respondents: occurs when interviewer influences respondent’s answers through wording, voice inflection, or body language

Field-Worker Errors Unintentional Unintentional field worker error: errors committed when an interviewer believes he or she is performing correctly Interviewer personal characteristics: occurs because of the interviewer’s personal characteristics such as accent, sex, and demeanor Interviewer misunderstanding: occurs when the interviewer believes he or she knows how to administer a survey but instead does it incorrectly Fatigue-related mistakes: occur when interviewer becomes tired

Respondent Errors Intentional Intentional respondent error: errors committed when there are respondents that willfully misrepresent themselves in surveys Falsehoods: occur when respondents fail to tell the truth in surveys Nonresponse: occurs when the prospective respondent fails 1) to take part in a survey or 2) to answer specific survey questions Refusals (respondent does not answer any questions) vs. Termination (respondent answers at least one question then stops)

Respondent Errors Intentional Refusals typically result from the topic of the study or potential respondent lack of time, energy or desire to participate Terminations result from a poorly designed questionnaire, questionnaire length, lack of time or energy, and/or external interruption

Respondent Errors Unintentional Unintentional respondent error: errors committed when a respondent gives a response that is not valid but that he or she believes is the truth

Respondent Errors Unintentional…cont. Respondent misunderstanding: occurs when a respondent gives an answer without comprehending the question and/or the accompanying instructions Guessing: occurs when a respondent gives an answer when he or she is uncertain of its accuracy Attention loss: occurs when a respondent’s interest in the survey wanes Distractions: (such as interruptions) may occur while questionnaire administration takes place Fatigue: occurs when a respondent becomes tired of participating in a survey

} { How to Control Data Collection Errors
Types of Errors Control Mechanisms Intentional Field Worker Errors Cheating Good questionnaire, Reasonable work expectation, Supervision, Random checks Leading respondent Validation Unintentional Field Worker Errors Interviewer Characteristics Selection and training of interviewers Misunderstandings Orientation sessions and role playing Fatigue Require breaks and alternate surveys } {

{ How to Control Data Collection Errors…cont. {
Types of Errors Control Mechanisms Intentional Respondent Errors Assuring anonymity and confidentiality Falsehoods Incentives Validation checks Third person technique Nonresponse Incentives { {

} { How to Control Data Collection Errors…cont. { {
Types of Errors Control Mechanisms Unintentional Respondent Errors Well-drafted questionnaire Misunderstandings Direct Questions: Do you understand? Guessing Response options (e.g., “unsure”) Attention loss Reversal of scale endpoints Distractions Fatigue Prompters { { } {

Data Collection Errors with Online Surveys
Multiple submissions by the same respondent (not able to identify such situations) Bogus respondents and/or responses (“fictitious person,” disguises or misrepresents self) Misrepresentation of the population (over-representing or under-representing segments with/without online access and use)

Nonresponse Error Nonresponse: failure on the part of a prospective respondent to take part in a survey or to answer specific questions on the survey Refusals to participate in survey Break-offs (terminations) during the interview Refusals to answer certain questions (item omissions) Completed interview must be defined (acceptable levels of non-answered questions and types).

Nonresponse Error…cont.
Response rate: enumerates the percentage of the total sample with which the interviews were completed Refusals to participate in survey Break-offs (terminations) during the interview Refusals to answer certain questions (item omissions)

Nonresponse Error…cont.
CASRO response rate formula (not mathematically correct):

Reducing Nonresponse Error
Mail surveys: Advance notification Monetary incentives Follow-up mailings Telephone surveys: Callback attempts

Preliminary Questionnaire Screening
Unsystematic (flip through questionnaire stack and look at some) and systematic (random or systematic sampling procedure to select) checks of completed questionnaires What to look for in questionnaire inspection Incomplete questionnaires? Nonresponses to specific questions? Yea- or nay-saying patterns (use scale extremes only)? Middle-of-the-road patterns (neutrals on all) ?

Unreliable Responses Unreliable responses are found when conducting questionnaire screening, and an inconsistent or unreliable respondent may need to be eliminated from the sample.

Things to Think About When You Want To Do a Survey

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