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

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Data Collection in the Field, Response Error, and
Questionnaire Screening
2
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

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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

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Nonsampling Errors Associated With Fieldwork
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Possible Errors in Field Data Collection Field-Wor
ker 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 respondents answers through wording,
  voice inflection, or body language

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Possible Errors in Field Data Collection Field-Wor
ker 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 interviewers 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

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Possible Errors in Field Data Collection Responden
t 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
• to take part in a survey or
• to answer specific survey questions
• Refusals (respondent does not answer any
  questions) vs. Termination (respondent answers at
  least one question then stops)

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Possible Errors in Field Data Collection Responden
t 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

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Possible Errors in Field Data Collection Responden
t 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

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Possible Errors in Field Data Collection Responden
t Errors Unintentionalcont.

• 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 respondents
  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

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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 Unin
tentional Field Worker Errors Interviewer
Characteristics Selection and training of
interviewers Misunderstandings Orientation
sessions and role playing Fatigue Require
breaks and alternate surveys


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How to Control Data Collection Errorscont.
Types of Errors Control Mechanisms
Intentional Respondent Errors Assuring
anonymity and confidentiality Falsehoods Incen
tives Validation checks Third person
technique Assuring anonymity and
confidentiality Nonresponse Incentives Thi
rd person technique


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How to Control Data Collection Errorscont.
Types of Errors Control Mechanisms
Unintentional Respondent Errors
Well-drafted questionnaire Misunderstanding
s Direct Questions Do you understand?
Well-drafted questionnaire Guessing Response
options (e.g., unsure) Attention
loss Reversal of scale endpoints Distractions
Fatigue Prompters




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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)

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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).

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Nonresponse Errorcont.

• 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)

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Nonresponse Errorcont.
CASRO response rate formula (not mathematically
correct)
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Reducing Nonresponse Error

• Mail surveys
• Advance notification
• Monetary incentives
• Follow-up mailings
• Telephone surveys
• Callback attempts

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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) ?

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Unreliable Responses

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

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Determining the Sample Plan
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The Sample Plan is the process followed to select
units from the population to be used in the sample
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Basic Concepts in Samples and Sampling

• Population the entire group under study as
  defined by research objectives. Sometimes called
  the universe.
• Researchers define populations in specific terms
  such as heads of households, individual person
  types, families, types of retail outlets, etc.
• Population geographic location and time of study
  are also considered.

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Basic Concepts in Samples and Samplingcont.

• Sampling error any error that occurs in a survey
  because a sample is used (random error)
• Sample frame a master list of the population
  (total or partial) from which the sample will be
  drawn
• Sample frame error (SFE) the degree to which the
  sample frame fails to account for all of the
  defined units in the population (e.g a telephone
  book listing does not contain unlisted numbers)
  leading to sampling frame error.

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Basic Concepts in Samples and Samplingcont.

• Calculating sample frame error (SFE)
• Subtract the number of items on the sampling
  list from the total number of items in the
  population.
• Take this number and divide it by the total
  population. Multiply this decimal by 100 to
  convert to percent (SFE must be expressed in )
• If the SFE was 40 this would mean that 40 of
  the population was not in the sampling frame

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Reasons for Taking a Sample

• Practical considerations such as cost and
  population size
• Inability of researcher to analyze large
  quantities of data potentially generated by a
  census
• Samples can produce sound results if proper rules
  are followed for the draw

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Basic Sampling Classifications

• Probability samples ones in which members of the
  population have a known chance (probability) of
  being selected
• Non-probability samples instances in which the
  chances (probability) of selecting members from
  the population are unknown

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Probability Sampling Methods Simple Random
Sampling

• Simple random sampling the probability of being
  selected is known and equal for all members of
  the population
• Blind Draw Method (e.g. names placed in a hat
  and then drawn randomly)
• Random Numbers Method (all items in the sampling
  frame given numbers, numbers then drawn using
  table or computer program)
• Advantages
• Known and equal chance of selection
• Easy method when there is an electronic database

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Probability Sampling Methods Simple Random
Sampling

• Disadvantages (Overcome with electronic
  database)
• Complete accounting of population needed
• Cumbersome to provide unique designations to
  every population member
• Very inefficient when applied to skewed
  population distribution (over- and under-sampling
  problems) this is not overcome with the use of
  an electronic database)

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Probability Sampling Methods Systematic Sampling

• Systematic sampling way to select a
  probability-based sample from a directory or
  list. This method is at times more efficient than
  simple random sampling.
• Sampling interval (SI) population list size (N)
  divided by a pre-determined sample size (n)
• How to draw
• calculate SI,
• select a number between 1 and SI randomly,
• go to this number as the starting point and the
  item on the list here is the first in the sample,
  
• add SI to the position number of this item and
  the new position will be the second sampled item,
  
• 5) continue this process until desired sample
  size is reached.

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Probability Sampling Methods Systematic Sampling

• Advantages
• Known and equal chance of any of the SI
  clusters being selected
• Efficiency..do not need to designate (assign a
  number to) every population member, just those
  early on on the list (unless there is a very
  large sampling frame).
• Less expensivefaster than SRS
• Disadvantages
• Small loss in sampling precision
• Potential periodicity problems

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Probability Sampling Methods Cluster Sampling

• Cluster sampling method by which the population
  is divided into groups (clusters), any of which
  can be considered a representative sample.
• These clusters are mini-populations and therefore
  are heterogeneous.
• Once clusters are established a random draw is
  done to select one (or more) clusters to
  represent the population.
• Area and systematic sampling (discussed earlier)
  are two common methods.
• Area sampling

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Probability Sampling Methods Cluster Sampling

• Advantages
• Economic efficiency faster and less expensive
  than SRS
• Does not require a list of all members of the
  universe
• Disadvantage
• Cluster specification errorthe more homogeneous
  the cluster chosen, the more imprecise the sample
  results

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Probability Sampling Methods Cluster Sampling
Area Method

• Drawing the area sample
• Divide the geo area into sectors (sub-areas) and
  give them names/numbers, determine how many
  sectors are to be sampled (typically a judgment
  call), randomly select these sub-areas. Do
  either a census or a systematic draw within each
  area.
• To determine the total geo area estimate add the
  counts in the sub-areas together and multiply
  this number by the ratio of the total number of
  sub-areas divided by number of sub-areas.

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A two-step area cluster sample (sampling several
clusters) is preferable to a one-step (selecting
only one cluster) sample unless the clusters are
homogeneous
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Probability Sampling Methods Stratified Sampling

• This method is used when the population
  distribution of items is skewed.
• It allows us to draw a more representative
  sample.
• Hence if there are more of certain type of item
  in the population the sample has more of this
  type and
• if there are fewer of another type, there are
  fewer in the sample.

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Probability Sampling Methods Stratified Sampling

• Stratified sampling the population is separated
  into homogeneous groups/segments/strata and a
  sample is taken from each. The results are then
  combined to get the picture of the total
  population.
• Sample stratum size determination
• Proportional method (stratum share of total
  sample is stratum share of total population)
• Disproportionate method (variances among strata
  affect sample size for each stratum)

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Probability Sampling Methods Stratified Sampling

• Advantage
• More accurate overall sample of skewed
  populationsee next slide for WHY
• Disadvantage
• More complex sampling plan requiring different
  sample sizes for each stratum

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Why is Stratified Sampling more accurate when
there are skewed populations?

• The less the variance in a group, the smaller the
  sample size it takes to produce a precise answer.
• Why? If 99 of the population (low variance)
  agreed on the choice of brand A, it would be easy
  to make a precise estimate that the population
  preferred brand A even with a small sample size.
• But, if 33 chose brand A, and 23 chose B, and
  so on (high variance) it would be difficult to
  make a precise estimate of the populations
  preferred brandit would take a larger sample
  size.

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Why is Stratified Sampling more accurate when
there are skewed populations? Continued..

• Stratified sampling allows the researcher to
  allocate a larger sample size to strata with more
  variance and smaller sample size to strata with
  less variance. Thus, for the same sample size,
  more precision is achieved.
• This is normally accomplished by disproportionate
  sampling.

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Non-probability Sampling Methods Convenience
Sampling Method

• Convenience samples samples drawn at the
  convenience of the interviewer. People tend to
  make the selection at familiar locations and to
  choose respondents who are like themselves.
• Error occurs
• in the form of members of the population who are
  infrequent or non-users of that location and
• who are not typical in the population

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Nonprobability Sampling Methods Judgment Sampling
Method

• Judgment samples samples that require a
  judgment or an educated guess on the part of
  the interviewer as to who should represent the
  population. Also, judges (informed
  individuals) may be asked to suggest who should
  be in the sample.
• Subjectivity enters in here, and certain members
  of the population will have a smaller or no
  chance of selection compared to others

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Nonprobabilty Sampling Methods Referral and
Quota Sampling Methods

• Referral samples (snowball samples) samples
  which require respondents to provide the names of
  additional respondents
• Members of the population who are less known,
  disliked, or whose opinions conflict with the
  respondent have a low probability of being
  selected.
• Quota samples samples that set a specific
  number of certain types of individuals to be
  interviewed
• Often used to ensure that convenience samples
  will have desired proportion of different
  respondent classes

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Online Sampling Techniques

• Random online intercept sampling relies on a
  random selection of Web site visitors
• Invitation online sampling is when potential
  respondents are alerted that they may fill out a
  questionnaire that is hosted at a specific Web
  site
• Online panel sampling refers to consumer or
  other respondent panels that are set up by
  marketing research companies for the explicit
  purpose of conducting online surveys with
  representative samples

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Developing a Sample Plan

• Sample plan definite sequence of steps that the
  researcher goes through in order to draw and
  ultimately arrive at the final sample

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Developing a Sample Plan Six steps

• Step 1 Define the relevant population.
• Specify the descriptors, geographic locations,
  and time for the sampling units.
• Step 2 Obtain a population list, if possible
  may only be some type of sample frame
• List brokers, government units, customer lists,
  competitors lists, association lists,
  directories, etc.

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Developing a Sample Plan Six steps

• Step 2 (concluded)
• Incidence rate (occurrence of certain types in
  the population, the lower the incidence the
  larger the required list needed to draw sample
  from)

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Developing a Sample Plan Six steps continued

• Step 3 Design the sample method (size and
  method).
• Determine specific sampling method to be used.
  All necessary steps must be specified (sample
  frame, n, recontacts, and replacements)
• Step 4 Draw the sample.
• Select the sample unit and gain the information

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Developing a Sample Plan Six stepsconcluded

• Step 4 (Continued)
• Drop-down substitution
• Oversampling
• Resampling
• Step 5 Assess the sample.
• Sample validation compare sample profile with
  population profile check non-responders
• Step 6 Resample if necessary.

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Determining the Size of a Sample
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Sample Accuracy

• Sample accuracy refers to how close a random
  samples statistic (e.g. mean, variance,
  proportion) is to the populations value it
  represents (mean, variance, proportion)
• Important points
• Sample size is NOT related to representativeness
  you could sample 20,000 persons walking by a
  street corner and the results would still not
  represent the city however, an n of 100 could be
  right on.

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Sample Accuracy

• Important points
• Sample size, however, IS related to accuracy.
  How close the sample statistic is to the actual
  population parameter (e.g. sample mean vs.
  population mean) is a function of sample size.

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Sample Size AXIOMS
To properly understand how to determine sample
size, it helps to understand the following AXIOMS
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Sample Size Axioms

• The only perfectly accurate sample is a census.
• A probability sample will always have some
  inaccuracy (sample error).
• The larger a probability sample is, the more
  accurate it is (less sample error).
• Probability sample accuracy (error) can be
  calculated with a simple formula, and expressed
  as a value.

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Sample Size Axiomscont.

• You can take any finding in the survey, replicate
  the survey with the same probability sample plan
  size, and you will be very likely to find the
  same result within the range of the original
  findings.
• In almost all cases, the accuracy (sample error)
  of a probability sample is independent of the
  size of the population.

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Sample Size Axiomscont.

• A probability sample can be a very tiny
  percentage of the population size and still be
  very accurate (have little sample error).
• The size of the probability sample depends on the
  clients desired accuracy (acceptable sample
  error) balanced against the cost of data
  collection for that sample size.

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There is only one method of determining sample
size that allows the researcher to PREDETERMINE
the accuracy of the sample results

• The Confidence Interval Method of Determining
  Sample Size

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The Confidence Interval Method of Determining
Sample Size Notion of Confidence Interval

• Confidence interval range whose endpoints define
  a certain percentage of the responses to a
  question
• Central limit theorem a theory that holds that
  values taken from repeated samples of a survey
  within a population would look like a normal
  curve. The mean of all sample means is the mean
  of the population.

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The Confidence Interval Method of Determining
Sample Size

• Confidence interval approach applies the
  concepts of accuracy, variability, and confidence
  interval to create a correct sample size
• Two types of error
• Nonsampling error pertains to all sources of
  error other than sample selection method and
  sample size
• Sampling error involves sample selection and
  sample sizethis is the error that we are
  controlling through formulas
• Sample error formula

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The Confidence Interval Method of Determining
Sample Size

• The relationship between sample size and sample
  error

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The Confidence Interval Method of Determining
Sample Size - Proportions Variability

• Variability refers to how similar or dissimilar
  responses are to a given question
• P () share that have or are or will do
  etc.
• Q () 100-P, share of have nots or are
  nots or wont dos etc.
• N.B. The more variability in the population
  being studied, the larger the sample size needed
  to achieve stated accuracy level.

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With Nominal data (i.e. Yes, No), we can
conceptualize answer variability with bar
chartsthe highest variability is 50/50
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The Central Limit Theorem allows us to use the
logic of the Normal Curve Distribution

• Since 95 of samples drawn from a population will
  fall within 1.96 x Sample error
• (this logic is based upon our understanding of
  the normal curve)
• we can make the following statement .

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If we conducted our study over and over,
e.g.1,000 times, we would expect our result to
fall within a known range ( 1.96 s.d.s of the
mean). Based upon this, there are 95 chances in
100 that the true value of the universe statistic
(proportion, share, mean) falls within this
range!
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The Confidence Interval Method of Determining
Sample Size Normal Distribution
1.96 X s.d. defines the endpoints for 95 of the
distribution
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We also know that, given the amount of
variability in the population, the sample size
affects the size of the confidence interval as n
goes down the interval widens (more sloppy)
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So, what have we learned thus far?

• There is a relationship among
• the level of confidence we desire that our
  results be repeated within some known range if we
  were to conduct the study again, and
• the variability (in responses) in the population
  and
• the amount of acceptable sample error (desired
  accuracy) we wish to have and
• the size of the sample.

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Sample Size Formula

• The formula requires that we
• (a.)specify the amount of confidence we wish to
  have,
• (b.) estimate the variance in the population, and
  
• (c.) specify the level of desired accuracy we
  want.
• When we specify the above, the formula tells us
  what sample size we need to use.n

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Sample Size Formula - Proportion

• The sample size formula for estimating a
  proportion (also called a percentage or share)

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Practical Considerations in Sample Size
Determination

• How to estimate variability (p and q shares) in
  the population
• Expect the worst case (p50 q50)
• Estimate variability results of previous
  studies or conduct a pilot study

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Practical Considerations in Sample Size
Determination

• How to determine the amount of desired sample
  error
• Researchers should work with managers to make
  this decision. How much error is the manager
  willing to tolerate (less error more accuracy)?
  
• Convention is 5
• The more important the decision, the less should
  be the acceptable level of the sample error

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Practical Considerations in Sample Size
Determination

• How to decide on the level of confidence desired
• Researchers should work with managers to make
  this decision. The higher the desired confidence
  level, the larger the sample size needed
• Convention is 95 confidence level (z1.96
  which is 1.96 s.d.s )
• The more important the decision, the more likely
  the manager will want more confidence. For
  example, a 99 confidence level has a z2.58.

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Example Estimating a Percentage (proportion or
share) in the PopulationWhat is the Required
Sample Size?

• Five years ago a survey showed that 42 of
  consumers were aware of the companys brand
  (Consumers were either aware or not aware)
• After an intense ad campaign, management will
  conduct another survey. They want to be 95
  confident (95 chances in 100) that the survey
  estimate will be within 5 of the true share of
  aware consumers in the population.
• What is n?

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Estimating a Percentage What is n?
Z1.96 (95 confidence) p42 (p, q and e must
be in the same units) q100 - p58 e
5 What is n?
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N374 What does this mean?

• It means that if we use a sample size of 374,
  after the survey, we can say the following of
  the results (Assume results show that 55 are
  aware)
• Our most likely estimate of the percentage of
  consumers that are aware of our brand name is
  55. In addition, we are 95 confident that the
  true share of aware customers in the population
  falls between 52.25 and 57.75.
• Note that ( .05 x 55 2.75) !!!!

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Estimating a MeanThis requires a different
formula
Z is determined the same way (1.96 or 2.58) e is
expressed in terms of the units we are
estimating, i.e. if we are measuring attitudes
on a 1-7 scale, we may want our error to be no
more than .5 scale units. If we are estimating
dollars being paid for a product, we may want our
error to be no more than 3.00. S is a little
more difficult to estimate, but must be in same
units as e.
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Estimating s in the Formula to Determine the
Sample Size Required to Estimate a Mean

• Since we are estimating a mean, we can assume
  that our data are either interval or ratio. When
  we have interval or ratio data, the standard
  deviation of the sample, s, may be used as a
  measure of variance.
• How to estimate s?
• Use standard deviation of the sample from a
  previous study on the target population
• Conduct a pilot study of a few members of the
  target population and calculate s

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Example Estimating the Mean of a PopulationWhat
is the required sample size, n?

• Management wants to know customers level of
  satisfaction with their service. They propose
  conducting a survey and asking for satisfaction
  on a scale from 1 to 10 (since there are 10
  possible answers, the range 10).
• Management wants to be 99 confident in the
  results (99 chances in 100 that true value is
  captured) and they do not want the allowed error
  to be more than .5 scale points.
• What is n?

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What is n?

• S 1.7 (from a pilot study), Z 2.58 (99
  confidence), and
• e .5 scale points
• What is n? It is 77. Assume the survey average
  score was 7.3, what does this tell us? A 10 is
  very satisfied and a 1 is not satisfied at all.
• Answer Our most likely estimate of the level of
  consumer satisfaction is 7.3 on a 10-point scale.
  In addition, we are 99 confident that the true
  level of satisfaction in our consumer population
  falls between 6.8 and 7.8 on the scale.

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Other Methods of Sample Size Determination

• Arbitrary percentage rule of thumb sample size
• Arbitrary sample size approaches rely on
  erroneous rules of thumb (e.g. n must be at
  least 5 of the population).
• Arbitrary sample sizes are simple and easy to
  apply, but they are neither efficient nor
  economical. (e.g. Using the 5 percent rule, if
  the universe is 12 million, n 600,000 a very
  large and costly result)

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Other Methods of Sample Size Determinationcont.

• Conventional sample size specification
• Conventional approach follows some convention
  or number believed somehow to be the right sample
  size (e.g. 1,000 1,200 used for national
  opinion polls w/ 3 error)
• Using conventional sample size can result in a
  sample that may be too large or too small.
• Conventional sample sizes ignore the special
  circumstances of the survey at hand.

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Other Methods of Sample Size Determinationcont.

• Statistical analysis requirements of sample size
  specification
• Sometimes the researchers desire to use
  particular statistical technique influences
  sample size. As cross comparisons go up cell
  sizes go up and n goes up.
• Cost basis of sample size specification
• Using the all you can afford method, instead of
  the value of the information to be gained from
  the survey being the primary consideration in
  sample size determination, the sample size is
  based on budget factors.

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Special Sample Size Determination
Situations Sample Size Using Nonprobability
Sampling

• When using nonprobability sampling, sample size
  is unrelated to accuracy, so cost-benefit
  considerations must be used

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Theoretical Framework and Hypothesis Development

• Theoretical framework is a conceptual model
  which shows the relationships between factors
  affecting a phenomenon.
• It is based on previous research that are tested.

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When developing theoretical frameworks

• Determine the relevant variables and define them
• State the relationships between 2 or more
  variables and their directions
• Determine the direction of relationships among
  variables
• Explain why this direction of the relationship is
  expected

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Types of Variables

• Dependent variable
• The main variable that is the main interest of
  the research
• The aim is to explain the change in this variable
• Brand preference, brand loyalty, customer
  satisfaction, evaluation of advertising campaign
• Export performance, Perceived image of Brand X
• Independent variable
• The variable which affects the dependent
  variable, in other words
• Which causes the change in the dependent variable
• Store preference-------- planned shopping
  behaviour
• Adoption of internet banking------- age
• Factors affecting supermarket preference --------
  the importance given to price

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Types of Variables

• Mediating variable
• The variable which is creates the necessary
  condition to have the relationship between the
  dependent and the independent variable
• Emotional attachment ------ consumer-company
  identification---corporate image
• Age------- shopping in supermarkets -------
  frozen food
• Intervening variable
• The variable which emerge during the period in
  which the affect of independent variables impact
  on the dependent variable is assessed

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Hypothesis

• The testable statements which assert the
  relationships that are pre-determined on the
  basis of theoretical framework
•  
• If-then statements
• Directional or non-directional statements
•  
• NULL and ALTERNATIVE HYPOTHESIS
• H0 It states the relationship that we do not
  want to find.
• We expect to reject this hypothesis
• Therefore, we should formulate the statement in
  the NULL hypothesis as something we do not prefer
  to happen.

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Examples

• The firms will launch the product to a certain
  market if the market share is more than 10
•  
• H0 ? ? 0.10
• Ha ? ?0.10
•  
• The new formula of the X product should bring a
  better market share than the existing version of
  the product X
•  
• H0 ?? 0.10 Ha ??0.10
• H0 ? ? ? Ha ? ? ?

97
Alternative Hypotheses Examples 

• Ha  There is a relationship between internet
  banking and prior experience about technological
  products.
•  
• Ha  There is a relationship between usage of
  marketing research in international markets and
  firm size.
•  
• Ha  Status of foreign partnership in capital
  affect technology usage in logistics activities.
•  
• Ha When brand preference is assessed, there is a
  difference between less loyal and more loyal
  consumers groups on brand reputation.
•  
• Ha  Age and gender affect purchase intention.

98
Hypothesis test types

• Two major aims
• Understanding differences
• Understanding relationships
•  
• Univariate tests
• There is only one measurement for an item in a
  sample
• Variables are tested individually
•  
• Multivariate tests
• There are 2 or more measurement for
  observationVariables are tested simultaneously