Chapter
2
Data Collection
Data Vocabulary
Level of Measurement
Time Series and Cross-sectional Data
Sampling Concepts
Sampling Methods
Data Sources
Survey Research
McGraw-Hill/Irwin
© 2008 The McGraw-Hill Companies, Inc. All rights reserved.
2-2
Data Vocabulary
•
Data is the plural form of the Latin datum (a “given”
fact).
•
In scientific research, data arise
from experiments whose results
are recorded systematically.
•
In business, data usually arise from
accounting transactions or
management processes.
•
Important decisions may depend on data.
2-3
Data Vocabulary
 Subjects, Variables, Data Sets
• We will refer to Data as plural and data set as a
particular collection of data as a whole.
• Observation – each data value.
• Subject (or individual) – an item for study (e.g., an
employee in your company).
• Variable – a characteristic about the subject or
individual (e.g., employee’s income).
2-4
Data Vocabulary
 Subjects, Variables, Data Sets
• Three types of data sets:
Data Set
Variables
Typical Tasks
Univariate
One
Histograms, descriptive
statistics, frequency tallies
Bivariate
Two
Scatter plots, correlations,
simple regression
Multivariate More than
two
Multiple regression, data
mining, econometric modeling
2-5
Data Vocabulary
 Subjects, Variables, Data Sets
Consider the multivariate data set with
5 variables 8 subjects
5 x 8 = 40 observations
2-6
Data Vocabulary
 Data Types
• A data set may have a mixture of data types.
Types of Data
Attribute
(qualitative)
Verbal Label
Coded
X = economics
X=3
(your major) (i.e., economics)
Numerical
(quantitative)
Discrete
X=2
(your siblings)
Continuous
X = 3.15
(your GPA)
2-7
Data Vocabulary
 Attribute Data
• Also called categorical, nominal or qualitative data.
• Values are described by words rather than
numbers.
• For example,
- Automobile style (e.g., X = full, midsize,
compact, subcompact).
- Mutual fund (e.g., X = load, no-load).
2-8
Data Vocabulary
 Data Coding
• Coding refers to using numbers to represent
categories to facilitate statistical analysis.
• Coding an attribute as a number does not make
the data numerical.
• For example,
1 = Bachelor’s, 2 = Master’s, 3 = Doctorate
• Rankings may exist, for example,
1 = Liberal, 2 = Moderate, 3 = Conservative
2-9
Data Vocabulary
 Binary Data
• A binary variable has only two values,
1 = presence, 0 = absence of a characteristic of
interest (codes themselves are arbitrary).
• For example,
1 = employed, 0 = not employed
1 = married, 0 = not married
1 = male, 0 = female
1 = female, 0 = male
• The coding itself has no numerical value so binary
variables are attribute data.
2-10
Data Vocabulary
 Numerical Data
• Numerical or quantitative data arise from counting
or some kind of mathematical operation.
• For example,
- Number of auto insurance claims filed in
March (e.g., X = 114 claims).
- Ratio of profit to sales for last quarter
(e.g., X = 0.0447).
• Can be broken down into two types – discrete or
continuous data.
2-11
Data Vocabulary
 Discrete Data
• A numerical variable with a countable number of
values that can be represented by an integer (no
fractional values).
• For example,
- Number of Medicaid patients (e.g., X = 2).
- Number of takeoffs at O’Hare (e.g., X = 37).
2-12
Data Vocabulary
 Continuous Data
• A numerical variable that can have any value
within an interval (e.g., length, weight, time, sales,
price/earnings ratios).
• Any continuous interval contains infinitely many
possible values (e.g., 426 < X < 428).
2-13
Level of Measurement
 Four levels of measurement for data:
Level of
Measurement
Characteristics
Example
Nominal
Categories only
Eye color (blue, brown,
green, hazel)
Ordinal
Rank has meaning
Bond ratings (Aaa, Aab,
C, D, F, etc.)
Interval
Distance has
meaning
Temperature (57o
Celsius)
Ratio
Meaningful zero
exists
Accounts payable ($21.7
million)
2-14
Level of Measurement
 Nominal Measurement
• Nominal data merely identify a category.
• Nominal data are qualitative, attribute, categorical
or classification data (e.g., Apple, Compaq, Dell,
HP).
• Nominal data are usually coded numerically,
codes are arbitrary (e.g., 1 = Apple, 2 = Compaq,
3 = Dell, 4 = HP).
• Only mathematical operations are counting (e.g.,
frequencies) and simple statistics.
2-15
Level of Measurement
 Ordinal Measurement
• Ordinal data codes can be ranked
(e.g., 1 = Frequently, 2 = Sometimes, 3 = Rarely,
4 = Never).
• Distance between codes is not meaningful
(e.g., distance between 1 and 2, or between 2 and
3, or between 3 and 4 lacks meaning).
• Many useful statistical tests exist for ordinal data.
Especially useful in social science, marketing and
human resource research.
2-16
Level of Measurement
 Interval Measurement
• Data can not only be ranked, but also have
meaningful intervals between scale points
(e.g., difference between 60F and 70F is same
as difference between 20F and 30F).
• Since intervals between numbers represent
distances, mathematical operations can be
performed (e.g., average).
• Zero point of interval scales is arbitrary, so ratios
are not meaningful (e.g., 60F is not twice as
warm as 30F).
2-17
Level of Measurement
 Likert Scales
• A special case of interval data frequently used in
survey research.
• The coarseness of a Likert scale refers to the
number of scale points (typically 5 or 7).
“College-bound high school students should be required to study a
foreign language.” (check one)





Strongly
Agree
Somewhat
Agree
Neither
Agree
Nor
Disagree
Somewhat
Disagree
Strongly
Disagree
2-18
Level of Measurement
 Likert Scales
• A neutral midpoint (“Neither Agree Nor Disagree”)
is allowed if an odd number of scale points is used
or omitted to force the respondent to “lean” one
way or the other.
• Likert data are
coded numerically
(e.g., 1 to 5) but any
equally spaced
values will work.
Likert coding:
1 to 5 scale
Likert coding:
-2 to +2 scale
5 = Help a lot
4 = Help a little
3 = No effect
2 = Hurt a little
1 = Hurt a lot
+2 = Help a lot
+1 = Help a little
0 = No effect
1 = Hurt a little
2 = Hurt a lot
2-19
Level of Measurement
 Likert Scales
• Careful choice of verbal anchors results in
measurable intervals (e.g., the distance from 1 to
2 is “the same” as the interval, say, from 3 to 4).
• Ratios are not meaningful (e.g., here 4 is not
twice 2).
• Many statistical calculations can be performed
(e.g., averages, correlations, etc.).
2-20
Level of Measurement
 Likert Scales
• More variants of Likert scales:
How would you rate your marketing instructor? (check one)

Terrible

Poor

Adequate

Good

Excellent
How would you rate your marketing instructor? (check one)
Very Bad






Very Good
2-21
Level of Measurement
 Ambiguity
• Grades are usually coded numerically
(A = 4, B = 3, C = 2, D = 1, F = 0) and are used to
calculate a mean GPA.
• Is the interval from 3.0 to 4.0 really the same as
the interval from 1.0 to 2.0?
• What is the underlying reality ranging from 0 to 4
that we are measuring?
• Best to be conservative and limit statistical tests to
those for ordinal data.
2-22
Level of Measurement
 Ratio Measurement
• Ratio data have all properties of nominal, ordinal
and interval data types and also possess a
meaningful zero (absence of quantity being
measured).
• Because of this zero point, ratios of data values
are meaningful (e.g., $20 million profit is twice as
much as $10 million).
• Zero does not have to be observable in the data,
it is an absolute reference point.
2-23
Level of Measurement
 Use the following procedure to
recognize data types:
Question
If “Yes”
Q1. Is there a
meaningful zero point?
Ratio data (all statistical operations are
allowed)
Q2. Are intervals
between scale points
meaningful?
Interval data (common statistics allowed,
e.g., means and standard deviations)
Q3. Do scale points
represent rankings?
Ordinal data (restricted to certain types
of nonparametric statistical tests)
Q4. Are there discrete
categories?
Nominal data (only counting allowed,
e.g. finding the mode)
2-24
Level of Measurement
 Changing Data by Recoding
• In order to simplify data or when exact data
magnitude is of little interest, ratio data can be
recoded downward into ordinal or nominal
measurements (but not conversely).
• For example, recode systolic blood pressure as
“normal” (under 130), “elevated” (130 to 140), or
“high” (over 140).
• The above recoded data are ordinal (ranking is
preserved) but intervals are unequal and some
information is lost.
2-25
Time Series and Cross-sectional Data
 Time Series Data
• Each observation in the sample represents a
different equally spaced point in time (e.g., years,
months, days).
• Periodicity may be annual, quarterly, monthly,
weekly, daily, hourly, etc.
• We are interested in trends and patterns over time
(e.g., annual growth in
consumer debit card use
from 1999 to 2006).
2-26
Time Series and Cross-sectional Data
 Cross-sectional Data
• Each observation represents a different individual
unit (e.g., person) at the same point in time
(e.g., monthly VISA balances).
• We are interested in
- variation among observations or in
- relationships.
• We can combine the two data types to get pooled
cross-sectional and time series data.
2-27
Sampling Concepts
 Sample or Census?
• A sample involves looking only at some items
selected from the population.
• A census is an examination of all items in a
defined population.
• Why can’t the United States Census survey every
person in the population?
- Mobility
- Illegal immigrants
- Budget constraints
- Incomplete responses or nonresponses
2-28
Sampling Concepts
Situations Where A Sample May Be Preferred:
Infinite Population
No census is possible if the population is infinite or of indefinite size
(an assembly line can keep producing bolts, a doctor can keep
seeing more patients).
Destructive Testing
The act of sampling may destroy or devalue the item (measuring
battery life, testing auto crashworthiness, or testing aircraft turbofan
engine life).
Timely Results
Sampling may yield more timely results than a census (checking
wheat samples for moisture and protein content, checking peanut
butter for aflatoxin contamination).
2-29
Sampling Concepts
Situations Where A Sample May Be Preferred:
Accuracy
Sample estimates can be more accurate than a census. Instead of
spreading limited resources thinly to attempt a census, our budget
of time and money might be better spent to hire experienced staff,
improve training of field interviewers, and improve data safeguards.
Cost
Even if it is feasible to take a census, the cost, either in time or
money, may exceed our budget.
Sensitive Information
Some kinds of information are better captured by a well-designed
sample, rather than attempting a census. Confidentiality may also
be improved in a carefully-done sample.
2-30
Sampling Concepts
Situations Where A Census May Be Preferred
Small Population
If the population is small, there is little reason to sample, for the effort of
data collection may be only a small part of the total cost.
Large Sample Size
If the required sample size approaches the population size, we might as
well go ahead and take a census.
Database Exists
If the data are on disk we can examine 100% of the cases. But auditing or
validating data against physical records may raise the cost.
Legal Requirements
Banks must count all the cash in bank teller drawers at the end of each
business day. The U.S. Congress forbade sampling in the 2000 decennial
population census.
2-31
Sampling Concepts
 Parameters and Statistics
• Statistics are computed from a sample of n items,
chosen from a population of N items.
• Statistics can be used as estimates of parameters
found in the population.
• Symbols are used to represent population
parameters and sample statistics.
2-32
Sampling Concepts
 Parameters and Statistics
Parameter or Statistic?
Parameter
Any measurement that describes an entire population.
Usually, the parameter value is unknown since we
rarely can observe the entire population. Parameters
are often (but not always) represented by Greek
letters.
Statistic
Any measurement computed from a sample. Usually,
the statistic is regarded as an estimate of a population
parameter. Sample statistics are often (but not
always) represented by Roman letters.
2-33
Sampling Concepts
 Parameters and Statistics
• The population must be carefully specified and the
sample must be drawn scientifically so that the
sample is representative.
 Target Population
• The target population is the population we are
interested in (e.g., U.S. gasoline prices).
• The sampling frame is the group from which we
take the sample (e.g., 115,000 stations).
• The frame should not differ from the target
population.
2-34
Sampling Concepts
 Finite or Infinite?
• A population is finite if it has a definite size, even if
its size is unknown.
• A population is infinite if it is of arbitrarily large
size.
• Rule of Thumb: A population may be treated as
infinite when N is at least 20 times n (i.e., when
N/n > 20)
N
n
Here,
N/n > 20
2-35
Sampling Methods
Probability Samples
Simple Random
Sample
Use random numbers to select items
from a list (e.g., VISA cardholders).
Systematic Sample
Select every kth item from a list or
sequence (e.g., restaurant customers).
Stratified Sample
Select randomly within defined strata
(e.g., by age, occupation, gender).
Cluster Sample
Like stratified sampling except strata
are geographical areas (e.g., zip
codes).
2-36
Sampling Methods
Nonprobability Samples
Judgment
Sample
Use expert knowledge to choose
“typical” items (e.g., which employees
to interview).
Convenience
Sample
Use a sample that happens to be
available (e.g., ask co-worker opinions
at lunch).
2-37
Sampling Methods
 Simple Random Sample
• Every item in the population of N items has the
same chance of being chosen in the sample of n
items.
• We rely on random
numbers to select a
name.
=RANDBETWEEN(1,48)
2-38
Sampling Methods
 Random Number Tables
• A table of random digits used to select random
numbers between 1 and N.
• Each digit 0 through 9 is equally likely to be
chosen.
 Setting Up a Rule
• For example, NilCo wants to award cash prizes to
10 of its 875 loyal customers.
• To get 10 three-digit numbers between 001 and
875, we define any consistent rule for moving
through the random number table.
2-39
Sampling Methods
 Setting Up a Rule
• Randomly point at the table to choose a starting
point.
• Choose the first three digits of the selected fivedigit block, move to the right one column, down
one row, and repeat.
• When we reach the end of a line, wrap around to
the other side of the table and continue.
• Discard any number greater than 875 and any
duplicates.
2-40
Start Here
Table of 1,000 Random Digits
82134
14458
66716
54269
31928
46241
03052
00260
32367
25783
07139
16829
76768
11913
42434
91961
92934
18229
15595
02566
45056
43939
31188
43272
11332
99494
19348
97076
95605
28010
10244
19093
51678
63463
85568
70034
82811
23261
48794
63984
12940
84434
50087
20189
58009
66972
05764
10421
36875
64964
84438
45828
40353
28925
11911
53502
24640
96880
93166
68409
98681
67871
71735
64113
90139
33466
65312
90655
75444
30845
43290
96753
18799
49713
39227
15955
46167
63853
03633
19990
96893
85410
88233
22094
30605
79024
01791
38839
85531
94576
75403
41227
00192
16814
47054
16814
81349
92264
01028
29071
78064
92111
51541
76563
69027
67718
06499
71938
17354
12680
26246
71746
94019
93165
96713
03316
75912
86209
12081
57817
98766
67312
96358
21351
86448
31828
86113
78868
67243
06763
37895
51055
11929
44443
15995
72935
99631
18190
85877
31309
27988
81163
52212
25102
61798
28670
01358
60354
74015
18556
19216
53008
44498
19262
12196
93947
90162
76337
12646
26838
28078
86729
69438
24235
35208
48957
53529
76297
41741
54735
34455
61363
93711
68038
75960
16327
95716
66964
28634
65015
53510
90412
70438
45932
57815
75144
52472
61817
41562
42084
30658
18894
88208
97867
30737
94985
18235
02178
39728
66398
2-41
Sampling Methods
 With or Without Replacement
• If we allow duplicates when sampling, then we are
sampling with replacement.
• Duplicates are unlikely when n is much smaller
than N.
• If we do not allow duplicates when sampling, then
we are sampling without replacement.
2-42
Sampling Methods
 Computer Methods
Excel - Option A
Enter the Excel function =RANDBETWEEN(1,875)
into 10 spread-sheet cells. Press F9 to get a new
sample.
Excel - Option B
Enter the function =INT(1+875*RAND()) into 10
spreadsheet cells. Press F9 to get a new sample.
Internet
The web site www.random.org will give you many
kinds of excellent random numbers (integers,
decimals, etc).
Minitab
Use Minitab’s Random Data menu with the Integer
option.
These are pseudo-random generators because even the best
algorithms eventually repeat themselves.
2-43
Sampling Methods
 Randomizing a List
• In Excel, use function =RAND() beside each row
to create a column of random numbers between
0 and 1.
• Copy and paste these numbers into the same
column using “Paste Special | Values” (to paste
only the values and not the formulas).
• Sort the spreadsheet on the random number
column.
2-44
Sampling Methods
 Randomizing a List
• The first n items
are a random
sample of the
entire list (they
are as likely as
any others).
2-45
Sampling Methods
 Systematic Sampling
• Sample by choosing every kth item from a list,
starting from a randomly chosen entry on the list.
• For example, starting at item 2, we sample every
k = 4 items to obtain a sample of n = 20 items from
a list of N = 78 items.
• Note that N/n = 78/20  4.
2-46
Sampling Methods
 Systematic Sampling
• A systematic sample of n items from a population
of N items requires that periodicity k be
approximately N/n.
• Systematic sampling should yield acceptable
results unless patterns in the population happen to
recur at periodicity k.
• Can be used with unlistable or infinite populations.
• Systematic samples are well-suited to linearly
organized physical populations.
2-47
Sampling Methods
 Systematic Sampling
• For example, out of 501 companies, we want to
obtain a sample of 25. What should the periodicity
k be?
k = N/n = 501/25  20.
• So, we should choose every 20th company from a
random starting point.
2-48
Sampling Methods
 Stratified Sampling
• Utilizes prior information about the population.
• Applicable when the population can be divided
into relatively homogeneous subgroups of known
size (strata).
• A simple random sample of the desired size is
taken within each stratum.
• For example, from a population containing 55%
males and 45% females, randomly sample 120
males and 80 females (n = 200).
2-49
Sampling Methods
 Stratified Sampling
• Or, take a random sample of the entire population
and then combine individual strata estimates using
appropriate weights.
• For a population with L strata, the population size
N is the sum of the stratum sizes:
N = N1 + N2 + ... + NL
• The weight assigned to stratum j is
wj = Nj / n
• For example, take a random sample of n = 200
and then weight the responses for males by
wM = .55 and for females by wF = .45.
2-50
Sampling Methods
 Cluster Sample
• Strata consist of geographical regions.
• One-stage cluster sampling – sample consists of
all elements in each of k randomly chosen
subregions (clusters).
• Two-stage cluster sampling, first choose k
subregions (clusters), then choose a random
sample of elements within each cluster.
2-51
Sampling Methods
 Cluster Sample
• Here is an
example of 4
elements sampled
from each of 3
randomly chosen
clusters (two-stage
cluster sampling).
2-52
Sampling Methods
 Cluster Sample
• Cluster sampling is useful when
- Population frame and stratum characteristics are
not readily available
- It is too expensive to obtain a simple or stratified
sample
- The cost of obtaining data increases sharply with
distance
- Some loss of reliability is acceptable
2-53
Sampling Methods
 Judgment Sample
• A nonprobability sampling method that relies on
the expertise of the sampler to choose items that
are representative of the population.
• Can be affected by subconscious bias (i.e.,
nonrandomness in the choice).
• Quota sampling is a special kind of judgment
sampling, in which the interviewer chooses a
certain number of people in each category.
2-54
Sampling Methods
 Convenience Sample
• Take advantage of whatever sample is available at
that moment. A quick way to sample.
 Sample Size
• Sample size depends on the inherent variability of
the quantity being measured and on the desired
precision of the estimate.
2-55
Data Sources
 Useful Data Sources
Type of Data
Examples
U.S. general data
Statistical Abstract of the U.S.
U.S. economic data
Economic Report of the President
Almanacs
World Almanac, Time Almanac
Periodicals
Economist, Business Week, Fortune
Indexes
New York Times, Wall Street Journal
Databases
CompuStat, Citibase, U.S. Census
World data
CIA World Factbook
Web
Google, Yahoo, msn
2-56
Survey Research
 Basic Steps of Survey Research
• Step 1: State the goals of the research
• Step 2: Develop the budget (time, money, staff)
• Step 3: Create a research design (target population,
frame, sample size)
• Step 4: Choose a survey type and method of
administration
2-57
Survey Research
 Basic Steps of Survey Research
• Step 5: Design a data collection instrument
(questionnaire)
• Step 6: Pretest the survey instrument and revise as
needed
• Step 7: Administer the survey (follow up if needed)
• Step 8: Code the data and analyze it
2-58
Survey Research
 Survey Types
Type of
Survey
Characteristics
Mail
You need a well-targeted and current mailing list
(people move a lot). Low response rates are typical
and nonresponse bias is expected (nonrespondents
differ from those who respond). Zip code lists (often
costly) are an attractive option to define strata of
similar income, education, and attitudes. To
encourage participation, a cover letter should clearly
explain the uses to which the data will be put. Plan
for follow-up mailings.
2-59
Survey Research
 Survey Types
Type of
Survey
Characteristics
Telephone
Random dialing yields very low response and is
poorly targeted. Purchased phone lists help reach
the target population, though a low response rate
still is typical (disconnected phones, caller
screening, answering machines, work hours, nocall lists). Other sources of nonresponse bias
include the growing number of non-English
speakers and distrust caused by scams and
spams.
2-60
Survey Research
 Survey Types
Type of
Survey
Characteristics
Interviews
Interviewing is expensive and time-consuming, yet
a trade-off between sample size for high-quality
results may still be worth it. Interviews must be
carefully handled so interviewers must be welltrained – an added cost. But you can obtain
information on complex or sensitive topics (e.g.,
gender discrimination in companies, birth control
practices, diet and exercise habits).
2-61
Survey Research
 Survey Types
Type of
Survey
Characteristics
Web
Web surveys are growing in popularity, but are
subject to nonresponse bias because those who
participate may differ from those who feel too busy,
don’t own computers or distrust your motives
(scams and spam are again to blame). This type of
survey works best when targeted to a well-defined
interest group on a question of self-interest (e.g.,
views of CPAs on new proposed accounting rules,
frequent flyer views on airline security).
2-62
Survey Research
 Survey Types
Type of
Survey
Characteristics
Direct
Observation
This can be done in a controlled setting (e.g.,
psychology lab) but requires informed consent,
which can change behavior. Unobtrusive
observation is possible in some nonlab settings
(e.g., what percentage of airline passengers carry
on more than two bags, what percentage of SUVs
carry no passengers, what percentage of drivers
wear seat belts).
2-63
Survey Research
 Survey Guidelines
Plan
What is the purpose of the survey?
Consider staff expertise, needed skills,
degree of precision, budget.
Design
Invest time and money in designing the
survey. Use books and references to
avoid unnecessary errors.
Quality
Take care in preparing a quality survey
so that people will take you seriously.
2-64
Survey Research
 Survey Guidelines
Pilot Test
Buy-in
Pretest on friends or co-workers to make
sure the survey is clear.
Improve response rates by stating the
purpose of the survey, offering a token of
appreciation or paving the way with
endorsements.
Expertise
Work with a consultant early on.
2-65
Survey Research
 Getting Advice
• Consider hiring a consultant in the early stages.
• Many resources are available to help
- The American Statistical Association
- The Research Industry Coalition
- The Council of American Survey Research Organizations
2-66
Survey Research
 Questionnaire Design
• Use a lot of white space in layout.
• Begin with short, clear instructions.
• State the survey purpose.
• Assure anonymity.
• Instruct on how to submit the completed survey.
2-67
Survey Research
 Questionnaire Design
• Break survey into naturally occurring sections.
• Let respondents bypass sections that are not
applicable (e.g., “if you answered no to question 7,
skip directly to Question 15”).
• Pretest and revise as needed.
• Keep as short as possible.
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 Questionnaire Design
Type of Question
Example
Open-ended question
Briefly describe your job goals.
Fill-in-the-blank
How many times did you attend formal
religious services during the last year?
________ times
Check boxes
Which of these statistics packages
have you ever used?
 SAS
 Visual Statistics
 SPSS
 MegaStat
 Systat
 Minitab
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 Questionnaire Design
Type of Question
Example
Ranked choices
“Please evaluate your dining experience”
Excellent
Good
Fair
Poor
Food




Service




Ambiance




Cleanliness




Overall




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 Questionnaire Design
Type of Question
Example
Pictograms
“What do you think of the President’s
economic policies?” (circle one)
Likert scale
Statistics is a difficult subject.
Strongly
Agree
Slightly
Agree


Neither
Agree Nor Slightly Strongly
Disagree Disagree Disagree



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 Question Wording
• The way a question is asked has a profound
influence on the response. For example,
1. Shall state taxes be cut?
2. Shall state taxes be cut, if it means
reducing highway maintenance?
3. Shall state taxes be cut, it is means firing
teachers and police?
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 Question Wording
• Make sure you have covered all the possibilities.
For example,
Are you married?  Yes  No
• Overlapping classes or How old is your father?
unclear categories are a
 35 – 45
problem. For example,
 45 – 55
 55 – 65
 65 or older
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 Coding and Data Screening
• Responses are usually coded numerically
(e.g., 1 = male 2 = female).
• Missing values are typically denoted by special
characters (e.g., blank, “.” or “*”).
• Discard questionnaires that are flawed or missing
many responses.
• Watch for multiple responses, outrageous or
inconsistent replies or range answers.
• Follow-up if necessary and always document your
data-coding decisions.
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 Sources of Error
Source of Error
Characteristics
Nonresponse bias
Respondents differ from nonrespondents
Selection bias
Self-selected respondents are atypical
Response error
Respondents give false information
Coverage error
Incorrect specification of frame or
population
Interviewer error
Responses influenced by interviewer
Measurement error
Survey instrument wording is biased or
unclear
Sampling error
Random and unavoidable
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 Data File Format
• Enter data into a spreadsheet or database as a
“flat file” (n subjects x m variables matrix).
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 Advice on Copying Data
• Using commas (,), dollar signs ($), or percents (%)
as part of the values may result in your data being
treated as text values.
• A numerical variable may only contain the digits
0-9, a decimal point, and a minus sign.
• To avoid round-off errors, format the data column
as plain numbers with the desired number of
decimal places before you copy the data to a
statistical package.