doane chapter 02
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Data Collection
Data VocabularyLevel of Measurement
Time Series and Cross-sectional Data
Sampling Concepts
Sampling Methods
Data Sources
Survey Research
Chapter
2
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Data Vocabulary
Data is the plural form of the Latin datum(a givenfact).
McGraw-Hill/Irwin 2007 The McGraw-Hill Companies, Inc. All rights reserved.
In scientific research, data arise
from experiments whose resultsare recorded systematically.
Important decisions may depend on data.
In business, data usually arise from
accounting transactions ormanagement processes.
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Data Vocabulary
Sub jects , Variables, Data Sets
We will refer to Data as plural and data setas aparticular collection of data as a whole.
Observation each data value.
Subject(orindividual) an item for study (e.g., anemployee in your company).
Variable a characteristic about the subject orindividual (e.g., employees income).
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Data Vocabulary
Sub jects , Variables, Data Sets Three types of data sets:
Data Set Variables Typical Tasks
Univariate One Histograms, descriptivestatistics, frequency tallies
Bivariate Two Scatter plots, correlations,simple regression
Multivariate More thantwo
Multiple regression, datamining, econometric modeling
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Data Vocabulary
Sub jects , Variables, Data SetsConsider the multivariate data set with
5 variables 8 subjects 5 x 8 = 40 observations
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Data Vocabulary
Data Types A data set may have a mixture ofdata types.
Types of Data
Attribute(qualitative)
Numerical(quantitative)
Verbal LabelX= economics
(your major)
CodedX= 3
(i.e., economics)
DiscreteX= 2
(your siblings)
ContinuousX= 3.15
(your GPA)
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Data Vocabulary
Attr ibu te 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).
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Data Vocabulary
Data Coding Codingrefers to using numbers to represent
categories to facilitate statistical analysis.
Coding an attribute as a number does notmakethe data numerical.
For example,
1 = Bachelors, 2 = Masters, 3 = Doctorate Rankings may exist, for example,
1 = Liberal, 2 = Moderate, 3 = Conservative
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Data Vocabulary
B inary 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 employed1 = married, 0 = not married
1 = male, 0 = female1 = female, 0 = male
The coding itself has no numerical value so binaryvariables are attribute data.
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Data Vocabulary
Numerical Data Numericalorquantitative data arise from counting
or some kind of mathematical operation.
For example,- Number of auto insurance claims filed inMarch (e.g.,X= 114 claims).
- Ratio of profit to sales for last quarter
(e.g.,X= 0.0447). Can be broken down into two typesdiscrete or
continuous data.
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Data Vocabulary
Disc rete 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 OHare (e.g.,X= 37).
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Data Vocabulary
Cont inuous 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
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Data Vocabulary
Rounding Ambiguity is introduced when continuous data are
rounded to whole numbers.
Underlying measurement scale is continuous. Precision of measurement depends on instrument.
Sometimes discrete data are treated as
continuous when the range is very large (e.g., SATscores) and small differences (e.g., 604 or 605)arent of much importance.
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Level of Measurement
Fou r levels of measu rement 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 hasmeaning
Temperature (57oCelsius)
Ratio Meaningful zeroexists
Accounts payable ($21.7million)
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Level of Measurement
Nom inal Measu rement 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.
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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 and3, or between 3 and 4 lacks meaning).
Many useful statistical tests exist for ordinal data.Especially useful in social science, marketing andhuman resource research.
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Level of Measurement
In terval Measu rement Data can not only be ranked, but also have
meaningful intervals between scale points
(e.g., difference between 60
F and 70
F is sameas difference between 20F and 30F). Since intervals between numbers represent
distances, mathematical operations can be
performed (e.g., average). Zero point of interval scales is arbitrary, so ratiosare not meaningful (e.g., 60F is nottwice aswarm as 30F).
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Level of Measurement
L ikert Scales A special case of interval data frequently used in
survey research.
The coarseness of a Likert scale refers to thenumber of scale points (typically 5 or 7).
College-bound high school students should be required to study aforeign language. (check one)
StronglyAgree
SomewhatAgree
NeitherAgree
NorDisagree
SomewhatDisagree
StronglyDisagree
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Level of Measurement
L ikert Scales A neutral midpoint(Neither Agree Nor Disagree)
is allowed if an oddnumber of scale points is usedor omitted to force the respondent to lean one
way or the other.
Likert data arecoded numerically
(e.g., 1 to 5) but anyequally spacedvalues will work.
Likert coding:
1 to 5 scaleLikert coding:
-2 to +2 scale
5 = Help a lot
4 = Help a little3 = No effect2 = Hurt a little1 = Hurt a lot
+2 = Help a lot
+1 = Help a little0 = No effect1 = Hurt a little2 = Hurt a lot
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Level of Measurement
L ikert Scales Careful choice of verbal anchors results in
measurable intervals (e.g., the distance from 1 to2 is the same as the interval, say, from 3 to 4).
Ratios are not meaningful (e.g., here 4 is nottwice 2).
Many statistical calculations can be performed(e.g., averages, correlations, etc.).
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Level of Measurement
L ikert 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
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Level of Measurement
Ambigu i ty Grades are usually coded numerically
(A = 4, B = 3, C= 2, D = 1, F= 0) and are used tocalculate a mean GPA.
Is the intervalfrom 3.0 to 4.0 really the same asthe 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.
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Level of Measurement
Ratio Measu rement Ratio data have all properties of nominal, ordinal
and interval data types and also possess ameaningful zero (absence of quantity beingmeasured).
Because of this zero point, ratios of data valuesare 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.
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Level of Measurement
Use the fol low ing p rocedu re torecognize data types:
Question If Yes
Q1. Is there ameaningful zero point?
Ratio data (all statistical operations areallowed)
Q2. Are intervalsbetween scale points
meaningful?
Interval data (common statistics allowed,e.g., means and standard deviations)
Q3. Do scale pointsrepresent rankings?
Ordinal data (restricted to certain typesof nonparametric statistical tests)
Q4. Are there discrete
categories?
Nominal data (only counting allowed,
e g finding the mode)
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Level of Measurement
Chang ing Data by Recoding In order to simplify data or when exact data
magnitude is of little interest, ratio data can berecoded downwardinto ordinal or nominalmeasurements (but not conversely).
For example, recode systolic blood pressure asnormal (under 130), elevated (130 to 140), or
high (over 140). The above recoded data are ordinal (ranking is
preserved) but intervals are unequal and someinformation is lost.
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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).
Periodicitymay be annual, quarterly, monthly,weekly, daily, hourly, etc.
We are interested in trends and patterns over time(e.g., annual growth inconsumer debit card usefrom 1999 to 2006).
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Time Series and Cross-sectional Data
Cross-sect ional 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 getpooledcross-sectional and time series data.
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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 adefined population.
- Mobility- Illegal immigrants- Budget constraints- Incomplete responses or nonresponses
Why cant the United States Census survey every
person in the population?
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Sampling Concepts
Situations Where A Sample May Be Preferred:
Infinite PopulationNo 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 TestingThe act of sampling may destroy or devalue the item (measuringbattery life, testing auto crashworthiness, or testing aircraft turbofan
engine life).Timely Results
Sampling may yield more timely results than a census (checkingwheat samples for moisture and protein content, checking peanutbutter for aflatoxin contamination).
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Sampling Concepts
Situations Where A Sample May Be Preferred:
AccuracySample estimates can be more accurate than a census. Instead ofspreading 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.
CostEven if it is feasible to take a census, the cost, either in time ormoney, may exceed our budget.
Sensitive InformationSome kinds of information are better captured by a well-designedsample, rather than attempting a census. Confidentiality may alsobe improved in a carefully-done sample.
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Sampling Concepts
Situations Where A Census May Be Preferred
Small PopulationIf the population is small, there is little reason to sample, for the effort ofdata collection may be only a small part of the total cost.
Large Sample SizeIf the required sample size approaches the population size, we might aswell go ahead and take a census.
Legal RequirementsBanks must count allthe cash in bank teller drawers at the end of eachbusiness day. The U.S. Congress forbade sampling in the 2000 decennial
l ti
Database Exists
If the data are on disk we can examine 100% of the cases. But auditing orvalidating data against physical records may raise the cost.
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Sampling Concepts
Parameters and Stat ist ics Statistics are computed from a sample ofn items,
chosen from a population ofNitems.
Statistics can be used as estimates ofparametersfound in the population.
Symbols are used to represent populationparameters and sample statistics.
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Sampling Concepts
Parameters and Stat ist ics
Statistic Any measurement computed from a sample. Usually,the statistic is regarded as an estimate of a populationparameter. Sample statistics are often (but notalways) represented by Roman letters.
Parameter or Statistic?
Parameter Any measurement that describes an entirepopulation.
Usually, the parameter value is unknown since werarely can observe the entire population. Parametersare often (but not always) represented by Greekletters.
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Sampling Concepts
Parameters and Stat ist ics The population must be carefully specified and the
sample must be drawn scientifically so that thesample is representative.
The target population is the population we areinterested in (e.g., U.S. gasoline prices).
Target Populat ion
The sampling frame is the group from which wetake the sample (e.g., 115,000 stations). The frame should not differ from the target
population.
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N n
Finite or In f in i te? 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 Nis at least 20 times n (i.e., whenN/n > 20)
Sampling Concepts
Here,N/n > 20
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Sampling Methods
Probability Samples
Simple RandomSample
Use random numbers to select itemsfrom a list (e.g., VISA cardholders).
Systematic Sample Select every kth item from a list orsequence (e.g., restaurant customers).
Stratified Sample Select randomly within defined strata(e.g., by age, occupation, gender).
Cluster Sample Like stratified sampling except strataare geographical areas (e.g., zipcodes).
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Sampling Methods
Nonprobability Samples
JudgmentSample
Use expert knowledge to choosetypical items (e.g., which employees
to interview).
ConvenienceSample
Use a sample that happens to beavailable (e.g., ask co-worker opinionsat lunch).
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Sampling Methods
Simple Random Sample Every item in the population ofNitems has the
same chance of being chosen in the sample ofnitems.
We rely on randomnumbers to select aname.
=RANDBETWEEN(1,48)
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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.
Sett ing 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 and875, we define any consistent rule for movingthrough the random number table.
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Sampling Methods
Sett ing Up a Rule Randomly point at the table to choose a starting
point.
Choose the first three digits of the selected five-digit block, move to the right one column, downone 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.
82134 14458 66716 54269 31928 46241 03052 00260 32367 25783
Table of 1,000 Random DigitsStart Here
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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
S li M th d
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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 smallerthan N.
If we do not allow duplicates when sampling, thenwe are sampling without replacement.
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Sampling Methods
Computer Methods
These arepseudo-random generators because even the bestalgorithms eventually repeat themselves.
Excel - Option A Enter the Excel function =RANDBETWEEN(1,875)into 10 spread-sheet cells. Press F9 to get a newsample.
Excel - Option B Enter the function =INT(1+875*RAND()) into 10spreadsheet cells. Press F9 to get a new sample.
Internet The web site www.random.org will give you manykinds of excellent random numbers (integers,decimals, etc).
Minitab Use Minitabs Random Data menu with the Integeroption.
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Using MINITAB to generate random numbers.
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Sampling Methods
Row
Column Data A rrays When the data are arranged in a rectangular array,
an item can be chosen at random by selecting arow and column.
For example, in the 4 x 3 array, select a randomcolumn between 1 and 3 and a random rowbetween 1 and 4.
This way, each item has an equal chance of beingselected.
Sampling Methods
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Dillard's K-Mart Saks
Dollar General Kohl's Sears Roebuck
Federated DeptStores
May Dept Stores Target
J. C Penney Nordstrom Wal-Mart Stores
Sampling Methods
Row
Column Data A rrays Use =RANDBETWEEN function to choose row 3
and column 3 (Target).
Sampling Methods
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Sampling Methods
Random izing a List In Excel, use function =RAND() beside each row
to create a column of random numbers between0 and 1.
Copy and paste these numbers into the samecolumn using Paste Special | Values (to paste
only the values and not the formulas).
Sort the spreadsheet on the random numbercolumn.
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Sampling Methods
The first n itemsare a random
sample of theentire list (theyare as likely asany others).
Random izing a List
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Sampling Methods
Systemat ic Sampl ing
For example, starting at item 2, we sample everyk= 4 items to obtain a sample ofn = 20 items froma list ofN= 78 items.
Note that N/n = 78/20 4.
Sample by choosing every kth item from a list,starting from a randomly chosen entry on the list.
Sampling Methods
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Sampling Methods
Systemat ic Sampl ing A systematic sample ofn items from a population
ofNitems requires that periodicity kbeapproximately N/n.
Systematic sampling should yield acceptableresults unless patterns in the population happen torecur at periodicity k.
Can be used with unlistable or infinite populations. Systematic samples are well-suited to linearly
organized physical populations.
Sampling Methods
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Sampling Methods
Systemat ic Sampl ing For example, out of 501 companies, we want to
obtain a sample of 25. What should the periodicitykbe?
k = N/n = 501/25 20.
So, we should choose every 20th company from arandom starting point.
Sampling Methods
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Sampling Methods
Strat i f ied Sampl ing Utilizes prior information about the population.
Applicable when the population can be divided
into relatively homogeneous subgroups of knownsize (strata).
A simple random sample of the desired size istaken within each stratum.
For example, from a population containing 55%males and 45% females, randomly sample 120males and 80 females (n = 200).
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Sampling Methods
Strat i f ied Sampl ing Or, take a random sample of the entire population
and then combine individual strata estimates usingappropriate weights.
For a population with L strata, the population sizeNis the sum of the stratum sizes:
N= N1 + N2+ ... + NL
The weight assigned to stratumjis
wj= Nj / n For example, take a random sample ofn = 200
and then weight the responses for males bywM= .55 and for females by wF= .45.
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Sampling Methods
Cluster Sample Strata consist of geographical regions.
One-stage cluster sampling sample consists of
all elements in each ofkrandomly chosensubregions (clusters).
Two-stage cluster sampling, first choose ksubregions (clusters), then choose a random
sample of elements within each cluster.
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Here is anexample of 4
elements sampledfrom each of 3randomly chosenclusters (two-stage
cluster sampling).
Sampling Methods
Cluster Sample
Sampling Methods
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Sampling Methods
Cluster Sample Cluster sampling is useful when
- Population frame and stratum characteristics arenot readily available
- It is too expensive to obtain a simple or stratifiedsample
- The cost of obtaining data increases sharply withdistance
- Some loss of reliability is acceptable
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Sampling Methods
Judgment Sample A nonprobability sampling method that relies on
the expertise of the sampler to choose items thatare representative of the population.
Can be affected by subconscious bias (i.e.,nonrandomness in the choice).
Quota samplingis a special kind of judgment
sampling, in which the interviewer chooses acertain number of people in each category.
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Sampling Methods
Convenience Sample Take advantage of whatever sample is available at
that moment. A quick way to sample.
Sample size depends on the inherent variability ofthe quantity being measured and on the desiredprecision of the estimate.
Sample Size
Data Sources
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Type of Data Examples
U.S. general data Statistical Abstract of the U.S.
U.S. economic data Economic Report of the PresidentAlmanacs World Almanac, Time Almanac
Periodicals Economist, Business Week, Fortune
Indexes New York Times, Wall Street Journal
Databases CompuStat, Citibase, U.S. CensusWorld data CIA World Factbook
Web Google, Yahoo, msn
Data Sources
Useful Data Sou rces
Survey Research
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Step 1: State the goals of the research
Step 2: Develop the budget (time, money,
staff)
Step 3: Create a research design (targetpopulation,
frame, sample size)
Step 4: Choose a survey type and method ofadministration
Survey Research
Basic Steps o f Survey Research
Survey Research
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Step 5: Design a data collection instrument(questionnaire)
Step 6: Pretest the survey instrument andrevise as
needed
Step 7: Administer the survey (follow up ifneeded)
Step 8: Code the data and analyze it
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Basic Steps o f Survey Research
Survey Research
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Survey TypesType of
SurveyCharacteristics
Mail You need a well-targeted and current mailing list
(people move a lot). Low response rates are typicaland nonresponse bias is expected (nonrespondentsdiffer from those who respond). Zip code lists (oftencostly) are an attractive option to define strata ofsimilar income, education, and attitudes. Toencourage participation, a cover letter should clearlyexplain the uses to which the data will be put. Planfor follow-up mailings.
Survey Research
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Survey TypesType ofSurvey
Characteristics
Telephone Random dialing yields very low response and is
poorly targeted. Purchased phone lists help reachthe target population, though a low response ratestill is typical (disconnected phones, callerscreening, answering machines, work hours, no-
call lists). Other sources of nonresponse biasinclude the growing number of non-Englishspeakers and distrust caused by scams andspams.
Survey Research
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Survey TypesType ofSurvey
Characteristics
Interviews Interviewing is expensive and time-consuming, yeta trade-off between sample size for high-qualityresults may still be worth it. Interviews must becarefully handled so interviewers must be well-trained an added cost. But you can obtain
information on complex or sensitive topics (e.g.,gender discrimination in companies, birth controlpractices, diet and exercise habits).
Survey Research
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Survey TypesType ofSurvey
Characteristics
Web Web surveys are growing in popularity, but are
subject to nonresponse bias because those whoparticipate may differ from those who feel too busy,dont own computers or distrust your motives
(scams and spam are again to blame). This type ofsurvey works best when targeted to a well-definedinterest group on a question of self-interest (e.g.,views of CPAs on new proposed accounting rules,frequent flyer views on airline security).
Survey Research
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Survey TypesType ofSurvey
Characteristics
Direct
Observation
This can be done in a controlled setting (e.g.,
psychology lab) but requires informed consent,which can change behavior. Unobtrusiveobservation is possible in some nonlab settings(e.g., what percentage of airline passengers carryon more than two bags, what percentage of SUVs
carry no passengers, what percentage of driverswear seat belts).
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Plan What is the purpose of the survey?Consider staff expertise, needed skills,degree of precision, budget.
Design Invest time and money in designing thesurvey. Use books and references to
avoid unnecessary errors.Quality Take care in preparing a quality survey
so that people will take you seriously.
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Survey Guidel ines
Survey Research
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Pilot Test Pretest on friends or co-workers to makesure the survey is clear.
Buy-in Improve response rates by stating thepurpose of the survey, offering a token ofappreciation or paving the way withendorsements.
Expertise Work with a consultant early on.
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Survey Guidel ines
Survey Research
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Gett ing 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
Survey Research
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Use a lot of white space in layout. Questionnaire Design
Begin with short, clear instructions.
State the survey purpose.
Assure anonymity.
Instruct on how to submit the completed survey.
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Questionnaire Design Break survey into naturally occurring sections.
Let respondents bypass sections that are notapplicable (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 DesignType of Question Example
Open-ended question Briefly describe your job goals.
Fill-in-the-blank How many times did you attend formalreligious services during the last year?________ times
Check boxes Which of these statistics packageshave you ever used? SAS Visual Statistics SPSS MegaStat Systat Minitab
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Type of Question Example
Questionnaire Design
Ranked choices Please evaluate your dining experience
Excellent Good Fair Poor
Food
Service
Ambiance Cleanliness
Overall
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Type of Question Example
Pictograms What do you think of the Presidentseconomic policies? (circle one)
Questionnaire Design
Likert scale Statistics is a difficult subject.Neither
Strongly Slightly Agree Nor Slightly StronglyAgree Agree Disagree Disagree Disagree
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The way a question is asked has a profoundinfluence on the response. For example,
1. Shall state taxes be cut?
2. Shall state taxes be cut, if it meansreducing highway maintenance?
3. Shall state taxes be cut, it is means firingteachers and police?
Quest ion Wording
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Make sure you have covered all the possibilities.For example,
Are you married? Yes No
Overlapping classes orunclear categories are aproblem. For example,
How old is your father? 35 45 45 55
55 65 65 or older
Quest ion Wording
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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 orinconsistent replies or range answers.
Follow-up if necessary and always document yourdata-coding decisions.
Cod ing and Data Screening
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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 orpopulation
Interviewer error Responses influenced by interviewerMeasurement error Survey instrument wording is biased or
unclear
Sampling error Random and unavoidable
Sources of Error
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Enter data into a spreadsheet or database as aflat file (n subjects x m variables matrix).
Data File Format
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Using commas (,), dollar signs ($), or percents (%)as part of the values may result in your data beingtreated as text values.
A numerical variable may only contain the digits0-9, a decimal point, and a minus sign.
To avoid round-off errors, format the data column
as plain numbers with the desired number ofdecimal places before you copy the data to astatistical package.
Advice on Copy ing Data
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Applied Statistics inBusiness and Economics
End of Chapter 2