Chapter Fourteen

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Chapter Fourteen

• Fieldwork

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Field Issues

• 1. Cost
• 80 of total project costs many times
• 2. Timing
• deadlines matter
• not all surveys same
• 3. Ethics
• truthfulness is key
• low-paid workers accomplish the work

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Field Issues

• 4. Motivation of field staff
• be there for briefing
• multiple contacts
• be firm, but not pushy
• 5. Incent respondents
• 6. International
• personal interviews dominant mode
• translations expand space used for text (CATI)

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Hammering Down Bud Phillips, Chariman
Emeritus M/A/R/C Group

• If I cant have it the day after tomorrow, it
  wont do me any good!
• President or VP Marketing
• But you cant do good research overnight!
• Fill-in faking
• Bending criteria for quotas
• Heavy-User of Alka-Seltzer 3 times per week
• Have you ever used Alka-Seltzer 3 times per
  week?

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1000 a day are faked!

• Interviewer cant meet quota or tired of
  rejection
• At malls, interviewers get to know retail staff
• Who then volunteer to do a tobacco chewing
  study
• Have found individuals who have done 16 a month.

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Language ability of interviewers matters a lot!

• Robert Berrier really disappointed in one
  telephone center.
• Cant be sloppy speakers.
• Must practice difficult words.
• Best interviewers (qual and quant studies) are
  like a smart waitress.

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Fieldwork/Data Collection Process
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Training of Field Workers

• Making the Initial Contact Interviewers should
  be trained to make opening remarks that will
  convince potential respondents that their
  participation is important.

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Training of Field Workers

• Probing Some commonly used probing techniques
• Repeating the question.
• Repeating the respondent's reply.
• Using a pause or silent probe.
• Boosting or reassuring the respondent.
• Eliciting clarification.
• Using objective/neutral questions or comments.

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Training of Field Workers

• Recording the Answers Guidelines for recording
  answers to unstructured questions
• Record responses during the interview.
• Use the respondent's own words.
•  
• Terminating the Interview The respondent should
  be left with a positive feeling about the
  interview.

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Validation of Fieldwork

• The supervisors call 10 - 25 of the respondents
  to inquire whether the field workers actually
  conducted the interviews.
• The supervisors ask about the length and quality
  of the interview, reaction to the interviewer,
  and basic demographic data.
• The demographic information is cross-checked
  against the information reported by the
  interviewers on the questionnaires.

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Chapter Fifteen

• Data Preparation

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Data Preparation Process
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Editing

• Treatment of Unsatisfactory Results
• Returning to the Field The questionnaires with
  unsatisfactory responses may be returned to the
  field, where the interviewers recontact the
  respondents.
• Assigning Missing Values If returning the
  questionnaires to the field is not feasible, the
  editor may assign missing values to
  unsatisfactory responses.
• Discarding Unsatisfactory Respondents In
  this approach, the respondents with
  unsatisfactory responses are simply discarded.

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Coding

• Coding means assigning a code, usually a number,
  to each possible response to each question. The
  code includes an indication of the column
  position (field) and data record it will occupy.
• Coding Questions
• Fixed field codes, which mean that the number of
  records for each respondent is the same and the
  same data appear in the same column(s) for all
  respondents, are highly desirable.
• If possible, standard codes should be used for
  missing data. Coding of structured questions is
  relatively simple, since the response options are
  predetermined.
• In questions that permit a large number of
  responses, each possible response option should
  be assigned a separate column.

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Coding

• Guidelines for coding unstructured questions
• Category codes should be mutually exclusive and
  collectively exhaustive.
• Only a few (10 or less) of the responses should
  fall into the other category.
• Category codes should be assigned for critical
  issues even if no one has mentioned them.
• Data should be coded to retain as much detail as
  possible.

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An Illustrative Computer File
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Data CleaningConsistency Checks

• Consistency checks identify data that are out of
  range, logically inconsistent, or have extreme
  values.
• Computer packages like SPSS, SAS, EXCEL and
  MINITAB can be programmed to identify
  out-of-range values for each variable and print
  out the respondent code, variable code, variable
  name, record number, column number, and
  out-of-range value.
• Extreme values should be closely examined.

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Data CleaningTreatment of Missing Responses

• Substitute a Neutral Value A neutral value,
  typically the mean response to the variable, is
  substituted for the missing responses.
• Substitute an Imputed Response The respondents'
  pattern of responses to other questions are used
  to impute or calculate a suitable response to the
  missing questions.
• In casewise deletion, cases, or respondents, with
  any missing responses are discarded from the
  analysis.
• In pairwise deletion, instead of discarding all
  cases with any missing values, the researcher
  uses only the cases or respondents with complete
  responses for each calculation.

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Statistically Adjusting the DataVariable
Respecification

• Variable respecification involves the
  transformation of data to create new variables or
  modify existing variables.
• E.G., the researcher may create new variables
  that are composites of several other variables.
• Dummy variables are used for respecifying
  categorical variables. The general rule is that
  to respecify a categorical variable with K
  categories, K-1 dummy variables are needed.

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Statistically Adjusting the DataVariable
Respecification

• Product Usage Original Dummy Variable Code
• Category Variable
• Code X1 X2 X3
• Nonusers 1 1 0 0
• Light users 2 0 1 0
• Medium users 3 0 0 1
• Heavy users 4 0 0 0
•  
• Note that X1 1 for nonusers and 0 for all
  others. Likewise, X2 1 for light users and 0
  for all others, and X3 1 for medium users and 0
  for all others. In analyzing the data, X1, X2,
  and X3 are used to represent all user/nonuser
  groups.

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Statistically Adjusting the DataScale
Transformation and Standardization

• Scale transformation involves a manipulation of
  scale values to ensure comparability with other
  scales or otherwise make the data suitable for
  analysis.
• A more common transformation procedure is
  standardization. Standardized scores, Zi, may be
  obtained as
• Zi (Xi - )/sx

X
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Chapter 16

• Frequency Distribution,
  Hypothesis Testing
  and Cross-tabulations

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Data Analysis

• Descriptive
• Computing measures of central tendency and
  dispersion,as well as constructing one-way tables
• Inferential
• Data analysis aimed at testing specific
  hypotheses is usually called inferential analysis

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Frequency Distribution

• In a frequency distribution, one variable is
  considered at a time.
• A frequency distribution for a variable produces
  a table of frequency counts, percentages, and
  cumulative percentages for all the values
  associated with that variable.

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Frequency Distribution of Familiaritywith the
Internet
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Frequency Histogram
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Frequency
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1
0
2
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Familiarity
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Statistics Associated with Frequency
DistributionMeasures of Location

• Mean
• Mode
• Median

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Variance

• Variance of a set of data is a measure of
  deviation of the data around the arithmetic mean

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Standard Deviation

• Standard deviation is the square root of the
  variance

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Statistics Associated with Frequency
DistributionMeasures of Variability

• The range measures the spread of the data. It is
  simply the difference between the largest and
  smallest values in the sample. Range Xlargest
  Xsmallest.
• The interquartile range is the difference between
  the 75th and 25th percentile. For a set of data
  points arranged in order of magnitude, the pth
  percentile is the value that has p of the data
  points below it and (100 - p) above it.

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Statistics Associated with Frequency
DistributionMeasures of Shape

• Skewness. The tendency of the deviations from the
  mean to be larger in one direction than in the
  other. It can be thought of as the tendency for
  one tail of the distribution to be heavier than
  the other.
• Kurtosis is a measure of the relative peakedness
  or flatness of the curve defined by the frequency
  distribution. The kurtosis of a normal
  distribution is zero. If the kurtosis is
  positive, then the distribution is more peaked
  than a normal distribution. A negative value
  means that the distribution is flatter than a
  normal distribution.

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Skewness of a Distribution
Symmetric Distribution
Skewed Distribution
Mean Median Mode (a)
Mean Median Mode (b)
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Null and Alternative Hypotheses

• H0 -gt Null Hypotheses
• Ha -gt Alternative Hypotheses
• Hypotheses always pertain to population
  parameters or characteristics rather than to
  sample characteristics. It is the population, not
  the sample, that we want to make an infernece
  about from limited data

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Cross-Tabulations Chi-square Contingency Test

• Technique used for determining whether there is a
  statistically significant relationship between
  two categorical (nominal or ordinal) variables

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Telecommunications Company

• Marketing manager of a telecommunications company
  is reviewing the results of a study of potential
  users of a new cell phone
• Random sample of 200 respondents
• A cross-tabulation of data on whether target
  consumers would buy the phone (Yes or No) and
  whether the cell phone had access to the Internet
  (Yes or No)
• Question
• Can the marketing manager infer that an
  association exists between Internet access and
  buying the cell phone?

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Two-Way Tabulation of Internet Access and Whether
they Would Buy the Cellular Phone
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Cross Tabulations - Hypotheses
H0 There is no association between Internet
access and buying the cell phone (the two
variables are independent of each other). Ha
There is some association between Internet access
and buying the cell phone (the two variables are
not independent of each other).
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Conducting the Test

• Test involves comparing the actual, or observed,
  cell frequencies in the cross-tabulation with a
  corresponding set of expected cell
  frequencies(Eij)

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Expected Values

• ninj
• Eij -------
• n
• where ni and nj are the marginal frequencies,
  that is, the total number of sample units in
  category i of the row variable and category j of
  the column variable, respectively

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Computing Expected Values

• The expected frequency for the first-row,
  first-column cell is given by
• 100 ? 100
• E11 ------------ 50
  200

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Observed and Expected Cell Frequencies
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Chi-square Test Statistic
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Chi-square Test Statistic in a Contingency Test
For d.f. 1, Assuming ? .05, from Appendix 2,
the critical chi-square value (?2c)
3.84. Decision rule is-- Reject H0 if ?2 ?
3.84. Computed ?2 72.00 Since the computed
Chi-square value is greater than the critical
value of 3.84, reject H0. The apparent
relationship between "Internet access"and "would
buy the cellular phone" revealed by the sample
data is unlikely to have occurred because of
chance
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Interpretation

• The actual significance level associated with a
  chi-square value of 72 is less than .001 (from
  Appendix 2). Thus, the chances of getting a
  chi-square value as high as 72 when there is no
  relationship between Internet access and purchase
  of cell phones are less than 1 in 1,000.

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Precautions in Interpreting Cross Tabulation
Results

• Two-way tables cannot show conclusive evidence of
  a causal relationship
• Watch out for small cell sizes
• Increases the risk of drawing erroneous
  inferences when more than two variables are
  involved

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The New Insights Career Bill Tanner Dallas
Morning News / Belo Corp.
IQ EQ Technical Skills Knowledge/ Wisdom
Proven Results
51

• Job Description Director, Marketing Research
• Lead consumer targeting efforts via more focused
  brand positioning efforts - Provides Leadership
  in areas of Trends, Customer Insights that drive
  new product / channel or other growth
  opportunities
• Must Have
• Business Consulting

IQ EQ Technical Skills Knowledge/ Wisdom
Proven Results
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Another Job You Might Want Someday

• Job Description Vice President, Consumer
  Strategy Insights
• Accelerate the companys top line growth rate in
  the marketplace.
• Quickly identify marketplace growth opportunities
  and inspire action.
• Knowledge/ Wisdom
• Is the person others turn to, formally and ad
  hoc, to answer strategic questions.
• A passionate, intellectual curiosity about the
  next big idea and demonstrated ability to own and
  drive it from identification and concept to
  marketplace reality and success.

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Knowledge/ Wisdom Getting From Here To There

• Business Consulting is what corporate research
  has become.
• Research skills just cost of entry and one part
  of the expanding toolkit.

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Knowledge/ Wisdom Broad Toolkit I

• Business consulting requires creative problem
  solving across a broad toolkit
• Business strategy
• Cohort trap Ivory Soap, Cheerios, Oldsmobile,
  Newspapers, Frozen OJ, etc.
• Disruption traps Cost Dell, Internet Amazon,
  Performance Google, etc.
• Innovation
• White space discovery
• Jobs to be done / need states/ barriers reasons
  why.
• Financial Analysis
• The best consumer maximization solution may not
  be the best business decision e.g. loyalty
  reward programs,
• Not knowing the financial impact of research
  recommendations can be fatal.

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Knowledge/ Wisdom Broad Toolkit II

• Business consulting requires creative problem
  solving across a broad toolkit
• Consumer values, motivations and insights gleaned
  from secondary sources.
• Ethnography and anthropology, behavioral
  genetics, biology, sociology, psychology, trends
  analysis, learning labs e.g. MIT, P G,
  history and archaeology, etc.
• Technological knowledge
• Communications skills, written, verbal and
  non-verbal.
• Must constantly sell and influence
• Requires connecting the dots, paint the
  picture, show implications, light the way and
  facilitate execution.

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Earning The Shield