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Steps in Data Preparation
Editing
Coding
Entering Data
Data Tabulation
Reviewing Tabulations
Statistically adjusting the data (e.g. weighting)
Editing Carefully checking survey data for
Completeness (no omissions) Non-ambiguous (e.g. two boxes checked instead of one) Right informant (e.g. under age, when all supposed to be over
18) Consistency
e.g. charging something when the person does not own a charge card
Accuracy (e.g. no numbers out of range)
Most important purpose is to eliminate or at least reduce the number of errors in the raw data.
1. Ideally re-interview respondent2. Eliminate all unacceptable surveys (case wise deletion)
(if sample is large and few unacceptable)3. In calculations only the cases with complete responses
are considered (pair wise deletion) (means that some statistics will be based on different sample sizes)
4. Code illegible or missing answers into a a “no valid response” category
5. substitute a neutral value - typically the mean response to the variable, therefore the mean remains unchanged
Solutions
Coding• The process of systematically and consistently
assigning each response a numerical score.• The key to a good coding system is for the coding
categories to be mutually exclusive and the entire system to be collectively exhaustive.
• To be mutually exclusive, every response must fit into only one category.
• To be collectively exhaustive, all possible responses must fit into one of the categories.
• Exhaustive means that you have covered the entire range of the variable with your measurement.
• Coding Missing Numbers: When respondents fail to complete portions of the survey. – Whatever the reason for incomplete surveys, you
must indicate that there was no response provided by the respondent.
– For single digit responses code as “9”, 2 digit code as “99”
Coding
Coding Open-Ended Questions: When open-ended questions are used, you must create categories.
– All responses must fit into a category
– similar responses should fall into the same category.
e.g. Who services your car? ______________
Possible categories: self, garage, husband, wife, friend, relative etc.
• To make it collectively exhaustive add an “other” or “none of the above” category
–Only a few i.e. < 10% should fit into this category
Are you: Male Female How satisfied are you with our product?___Very Satisfied___Somewhat Satisfied___Somewhat Dissatisfied___Very Dissatisfied___No opinion
Are you: (1) Male (2) Female How satisfied are you with our product?_1__Very Satisfied_2__Somewhat Satisfied_3__Somewhat Dissatisfied_4__Very Dissatisfied_5__No opinion
This… Becomes this…
Precoded Questionnaires: Sometimes you can place codes on the actual questionnaire, which simplifies data entry.
1. Are you solely responsible for taking care of your automotive service needs ___ Yes ___ No
2. If No who performs the simple maintenance ___________
3. If scheduled maintenance is done on your automobile, how do you keep track of what has been done
Not tracked� auto dealer records� mental recollection� other�
4. How often is your automobile serviced? Once per month� Once every three months� Once every six months� Once per year� Other _______________�
Col. No
Question No.
Question Des. Range of permissible values
1-3 ID # N/A 001-200 (this also means the surveys themselves should be numbered)
4 1 Responsible for Maintenance
0= No. 1=yes, 9= blank
5 2 perform simple maintenance
0=husband, 1=boyfriend, 2=father, 3=mother, 4=relative, 5=friend, 6=other, 9=blank
5 3 How maintenance tracked
0=not tracked, 1=auto dealer records, 2=personal records, 3=mental recollection, 4=other, 9=blank
6 4 How often maintenance performed
1=Once per month, 2=3 month, 3=6 months , 4=year , 5=other, 9= blank
Code Book
In questions that permit multiple responses, each possible response option should be assigned a separate column
6. Which magazines do you read, choose all that apply.
Time National Geographic Readers Digest Chatelaine MacLean's
Col. No Question No. Question Des. Range of permissible values
15 6 Time 0 =read, 1= not read
16 6 Readers Dig. 0 =read, 1= not read
17 6 MacLean's 0 =read, 1= not read
18 6 National Geo. 0 =read, 1= not read
19 6 Chatelaine 0 =read, 1= not read
For rank order questions, separate columns are also needed
7. Please rank the following brands of toothpaste in order of preference (1-5) with 1 being the most important
Crest Colgate Aquafresh Arm & Hammer Pepsodent
Col.# Q. No.
Question Des. Range of permissible values
20 7 Crest rank 0 =blank, 1 = most important, 2 =2nd most important, 3 =third, 4=fourth, 5= fifth
21 7 Colgate rank 0 =blank, 1 = most important, 2 =2nd most important, 3 =third, 4=fourth, 5= fifth
22 7 Acquafresh rank 0 =blank, 1 = most important, 2 =2nd most important, 3 =third, 4=fourth, 5= fifth
23 7 A & H rank 0 =blank, 1 = most important, 2 =2nd most important, 3 =third, 4=fourth, 5= fifth
25 7 Pepsodent rank 0 =blank, 1 = most important, 2 =2nd most important, 3 =third, 4=fourth, 5= fifth
Preparing the Data for Analysis
Variable Re-specification• Existing data modified to create new variables
• Large number of variables collapsed into fewer variables
• E.g. If 10 reasons for purchasing a car are given they might be collapsed into four categories e.g. performance, price, appearance, and service
• Creates variables that are consistent with research questions
Entering Data• Problems can occur during data entry, such as
transposing numbers and inputting an infeasible code(e.g out of range) – E.g. Score on range of 1-5 then 0, 6, 7, and 8 are
unacceptable or out of range (might be due to transcription error)
• Always check the data-entry work.
Five types of statistical analysis
Descriptive
Inferential
Differences
Associative
Predictive
What are the characteristics of the respondents?
What are the characteristics ofthe population?
Are two or more groups the sameor different?
Are two or more variables relatedin a systematic way?
Can we predict one variable if we know one or more other variables?
Summarization of a collection of data in a clear and understandable way
the most basic form of statistics lays the foundation for all statistical
knowledge
Descriptive Statistics
Measures of central tendency (mean, median, mode)
Measures of dispersion (range, standard deviation, and coefficient of variation)
Measures of shape (skewness and kurtosis)
The tradeoff in descriptive statistics
• If you use fewer statistics to describe the distribution of a variable, you lose information but gain clarity.
• When should one use fewer statistics?– When dropping the number of statistics would leave more
information per remaining statistic.
– When the information you drop is unimportant to one’s research question.
Type ofMeasurement
Nominal
Twocategories
More thantwo categories
Frequency tableProportion (percentage)
Frequency tableCategory proportions
(percentages)Mode
Type of descriptive analysis
Ratio means
Type ofMeasurement
Type of descriptive analysis
Ordinal Rank orderMedian
Interval Arithmetic mean
Data Tabulation• Tabulation: The organized arrangement of data in a
table format that is easy to read and understand.– Tabulate the data to count the number of responses to each
question.
• Simple Tabulation: tabulating of results of only one variable informs you how often each response was given.
• Frequency Distribution: A distribution of data that summarizes the number of times a certain value of a variable occurs expressed in terms of percentages.
The arrangement of statistical data in a row-and-column format that exhibits the count of responses or observations for each category assigned to a variable• How many of certain brand users can be called loyal?• What percentage of the market are heavy users and
light users?• How many consumers are aware of a new product?• What brand is the “Top of Mind” of the market?
Frequency Tables
More on relative frequency distributions
• Rules for relative frequency distributions:– Make sure each observation is in one and only one category.
– Use categories of equal width.
– Choose an appealing number of categories.
– Provide labels
– Double-check your graph.
• Definitions:– A histogram is a relative frequency distribution of a
quantitative variable
– A bar graph is a relative frequency distribution of a qualitative variable
643 Netw orking213 print ad179 Online recruitment site112 Placement f irm18 Temporary agency
How did you find your last job?
7006005004003002001000
Netw orking
print ad
Online recruitment site
Placement f irm
Temporary agency
55.2 %
18.3 %
15.4 %
9.6 %
1.5 %
WebSurveyor Bar Chart
How many times per week do you use mouthwash ?
1__ 2__ 3__ 4__ 5__ 6__ 7__
1 1 2 2 2 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 6 6 6 7 7
1 2
2 3
3 5
4 7
5 5
6 3
7 2
0
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Normal Distributions Curve is basically bell shaped from - to
symmetric with scores concentrated in the middle (i.e. on the mean) than in the tails.
Mean, medium and mode coincide
They differ in how spread out they are.
The area under each curve is 1.
The height of a normal distribution can be specified mathematically in terms of two parameters: the mean () and the standard deviation ().
Occur when one tail of the distribution is longer than the other.
Positive Skew Distributions have a long tail in the positive direction. sometimes called "skewed to the right" more common than distributions with negative skews E.g. distribution of income. Most people make under $40,000 a year, but some make quite a bit more with a small number making many millions of dollars per year The positive tail therefore extends out quite a long way
Negative Skew Distributionshave a long tail in the negative direction. called "skewed to the left." negative tail stops at zero
Skewed Distributions
• Kurtosis: how peaked a distribution is. A zero indicates normal distribution, positive numbers indicate a peak, negative numbers indicate a flatter distribution)
Peakeddistribution
Flat distribution
Thanks, Scott!
Summary statistics
–central tendency
–Dispersion or variabilityA quantitative measure of the degree to which scores in a distribution are spread out or are clustered together;
Descriptive Analysis: Measures of Central Tendency
• Mode: the number that occurs most often in a string (nominal data)
• Median: half of the responses fall above this point, half fall below this point (ordinal data)
• Mean: the average (interval/ratio data)
Mode the most frequent category
users 25%non-users 75%
Advantages: • meaning is obvious• the only measure of central tendency that can be used with nominal data.
Disadvantages• many distributions have more than one mode, i.e. are "multimodal• greatly subject to sample fluctuations • therefore not recommended to be used as the only measure of central tendency.
Medianthe middle observation of the data
number times per week consumers use mouthwash
1 1 2 2 2 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 6 6 6 7 7
Frequency distribution of Mouthwash use per week
Heavy userLight user Mode
Median
Mean
The Mean (average value)
sum of all the scores divided by the number of scores.
a good measure of central tendency for roughly symmetric distributions
can be misleading in skewed distributions since it can be greatly influenced by extreme scores in which case other statistics such as the median may be more informative
formula = X/N (population)
X = xi/n (sample)
where /X is the population/sample mean
and N/n is the number of scores.
¯
¯
• Minimum, Maximum, and Range (Highest value minus the lowest value)
• Variance • Standard Deviation (A measure’s distance
from the mean)
Measures of Dispersion or Variability
Distribution of Final Course Grades in MGMT 3220Y
0
5
10
15
20
25
Grade
Fre
qu
ency
Frequency 3 10 20 23 12
F D C B A
RANGE
- 1 SD
+ 1 SD
Variance• The difference between an observed value and the mean is called the deviation from the mean
• The variance is the mean squared deviation from the mean
• i.e. you subtract each value from the mean, square each result and then take the average.
• Because it is squared it can never be negative
2 = (x- xi)2/n¯
• The standard deviation is the square root of the variance
• Thus the standard deviation is expressed in the same units as the variables
• Helps us to understand how clustered or spread the distribution is around the mean value.
Standard Deviation
S = (x- xi)2/n¯
Measures of DispersionSuppose we are testing the new flavor of a fruit punch
Dislike 1 2 3 4 5 Like Data
1. 3
2. 5
3. 3
4. 5
5. 3
6. 5
x
x
x
x
x
x
X= 42= 1S = 1
2 = (x- xi)2/n¯ S = (x- xi)2/n¯
Measures of Dispersion
Dislike 1 2 3 4 5 Like Data
1. 5
2. 4
3. 5
4. 5
5. 5
6. 4
x
x
x
x
x
xX = 4.672=0.22S = 0.47
2 = (x- xi)2/n¯ S = (x- xi)2/n¯
¯
Measures of Dispersion
Dislike 1 2 3 4 5 Like Data
1. 1
2. 5
3. 1
4. 5
5. 1
6. 5
x
x
x
x
x
x
X= 32=4S = 2
2 = (x- xi)2/n¯ S = (x- xi)2/n¯
¯
• A statistical technique that involves tabulating the results of two or more variables simultaneously
• informs you how often each response was given
• Shows relationships among and between variables
• frequency distribution for each subgroup compared to the frequency distribution for the total sample
• must be nominally scaled
Cross Tabulation
Cross-tabulation• Helps answer questions about whether two
or more variables of interest are linked:– Is the type of mouthwash user (heavy or
light) related to gender?– Is the preference for a certain flavor (cherry
or lemon) related to the geographic region (north, south, east, west)?
– Is income level associated with gender?
• Cross-tabulation determines association not causality.
• The variable being studied is called the dependent variable or response variable.
• A variable that influences the dependent variable is called independent variable.
Dependent and Independent Variables
Cross-tabulation
• Cross-tabulation of two or more variables is
possible if the variables are discrete:– The frequency of one variable is subdivided by the
other variable categories.
• Generally a cross-tabulation table has:– Row percentages– Column percentages– Total percentages
• Which one is better?DEPENDS on which variable is considered as
independent.
• A contingency table shows the conjoint distribution of two discrete variables
• This distribution represents the probability of observing a case in each cell– Probability is calculated as:
Contingency Table
Observed casesTotal cases
P=
10 9 19
52.6% 47.4% 100.0%
55.6% 18.8% 28.8%
15.2% 13.6% 28.8%
5 25 30
16.7% 83.3% 100.0%
27.8% 52.1% 45.5%
7.6% 37.9% 45.5%
3 14 17
17.6% 82.4% 100.0%
16.7% 29.2% 25.8%
4.5% 21.2% 25.8%
18 48 66
27.3% 72.7% 100.0%
100.0% 100.0% 100.0%
27.3% 72.7% 100.0%
Count
% within GROUPINC
% within Gender
% of Total
Count
% within GROUPINC
% within Gender
% of Total
Count
% within GROUPINC
% within Gender
% of Total
Count
% within GROUPINC
% within Gender
% of Total
income <= 5
5>Income<= 10
income >10
GROUPINC
Total
Female Male
Gender
Total
Cross tabulationGROUPINC * Gender Crosstabulation
General Procedure for Hypothesis Test
1. Formulate H0 (null hypothesis) and H1
(alternative hypothesis)2. Select appropriate test3. Choose level of significance4. Calculate the test statistic (SPSS)5. Determine the probability associated with
the statistic.• Determine the critical value of the test
statistic.
General Procedure for Hypothesis Test
6 a) Compare with the level of significance,
b) Determine if the critical value falls in the
rejection region. (check tables)
7 Reject or do not reject H0
8 Draw a conclusion
• The hypothesis the researcher wants to test is called the alternative hypothesis H1.
• The opposite of the alternative hypothesis is the null hypothesis H0 (the status quo)(no difference between the
sample and the population, or between samples).
• The objective is to DISPROVE the null hypothesis.
• The Significance Level is the Critical probability of choosing between the null hypothesis and the alternative hypothesis
1. Formulate H1and H0
• The selection of a proper Test depends on:– Scale of the data
• nominal• interval
– the statistic you seek to compare• Proportions (percentages)• means
– the sampling distribution of such statistic• Normal Distribution• T Distribution2 Distribution
– Number of variables• Univariate• Bivariate• Multivariate
– Type of question to be answered
2. Select Appropriate Test
Example
A tire manufacturer believes that men are more aware of their brand. To find out, a survey is conducted of 100 customers, 65 of whom are men and 35 of whom are women.
The question they are asked is: Are you aware of our brand: Yes or No. 50 of the men were aware and 15 were not whereas 10 of the women were aware and 25 were not.
Are these differences significant?
Aware 50 10 60
Unaware 15 25 40 65 35 100
Men WomenTotal
We want to know whether brand awareness is associated with gender. What are the Hypotheses
1. Formulate H1and H0
H0:
H1:
There is no difference in brand awareness based on gender
There is a difference in brand awareness based on gender
Chi-square test results are unstable if cell count is lower than 5
• Used to discover whether 2 or more groups of one variable (dependent variable) vary significantly from each other with respect to some other variable (independent variable).
• Are the two variables of interest associated:
– Do men and women differ with respect to product usage (heavy, medium, or light)
– Is the preference for a certain flavor (cherry or lemon) related to the geographic region (north, south, east, west)?
H0: Two variables are independent (not associated)
H1: Two variables are not independent (associated)
• Must be nominal level, or, if interval or ratio must be divided into categories
X2 (Chi Square)2. Select Appropriate Test
Aware 50/39 10/21 60
Unaware 15/26 25/14 40 65 35 100
Men Women Total
Awareness of Tire Manufacturer’s Brand
Estimated cell Frequency n
CRE
jiij
Ri = total observed frequency in the ith rowCj = total observed frequency in the jth columnn = sample sizeEij = estimated cell frequency
3. Choose Level of Significance• Whenever we draw inferences about a population, there is a risk that
an incorrect conclusion will be reached• The real question is how strong the evidence in favor of the alternative
hypothesis must be to reject the null hypothesis.• The significance level states the probability of rejecting H0 when in fact
it is true. • In the example a Type I error would be committed if we said that
• This error is commonly known as Type I error, The value of is called the significance level of the testThere is a difference between men and women with respect to brand awareness when in fact there was no difference
• Significance Level selected is typically .05 or .01
• i.e 5% or 1%
•In other words we are willing to accept the risk that 5% (or 1%) of the time the results we get indicate that there is a difference between men and women with respect to brand awareness when in fact there is no difference
3. Choose Level of Significance
• We commit Type error II when we incorrectly accept a null hypothesis when it is false. The probability of committing Type error II is denoted by .
• In our example we commit a type II error when we say that.
there is NO difference between men and women with respect to brand awareness (we accept the null hypothesis) when in fact there is
Accept null Reject null
Null is true
Null is false
Correct-Correct-no errorno error
Type IType Ierrorerror
Type IIType IIerrorerror
Correct-Correct-no errorno error
Type I and Type II Errors
Which is worse?
• Both are serious, but traditionally Type I error has been considered more serious, that’s why the objective of hypothesis testing is to reject H0 only when there is enough evidence that supports it.
• Therefore, we choose to be as small as possible without compromising .
• Increasing the sample size for a given α will decrease β (I.e. accepting the null hypothesis when it is in fact false)
Aware 50/39 10/21 60
Unaware 15/26 25/14 40 65 35 100
Men Women Total
Awareness of Tire Manufacturer’s Brand
Estimated cell Frequency n
CRE
jiij
Ri = total observed frequency in the ith rowCj = total observed frequency in the jth columnn = sample sizeEij = estimated cell frequency
x² = chi-square statisticsOi = observed frequency in the ith cellEi = expected frequency on the ith cell
i
ii )²( ²
E
EOx
n
CRE ji
ij Ri = total observed frequency in the ith rowCj = total observed frequency in the jth columnn = sample sizeEij = estimated cell frequency
Estimated cell Frequency
Chi-Square statistic
Chi-Square Test
Degrees of Freedom
d.f.=(R-1)(C-1)
21
)2110(
39
)3950( 222
X
14
)1425(
26
)2615( 22
161.22
643.8654.4762.5102.32
2
1)12)(12(..
)1)(1(..
fd
CRfd
4. Calculate the Test StatisticChi-Square Test: Differences Among Groups
Chi-square test results are unstable if cell count is lower than 5
While there will be n such squared deviations only (n - 1) of them are free to assume any value whatsoever.
This is because the final squared deviation from the mean must include the one value of X such that the sum of all the Xs divided by n will equal the obtained mean of the sample.
All of the other (n - 1) squared deviations from the mean can, theoretically, have any values whatsoever..
Degrees of Freedom
the number of values in the final calculation of a statistic that are free to vary
For example To calculate the standard deviation of a random sample, we must first calculate the mean of that sample and then compute the sum of the squared deviations from that mean
5. Determine the Probability-value (Critical Value)
•The p-value is the probability of seeing a random sample at least as extreme as the sample observed given that the null hypothesis is true. • given the value of alpha, we use statistical theory to determine the rejection region.• If the sample falls into this region we reject the null hypothesis; otherwise, we accept it• Sample evidence that falls into the rejection region is called statistically significant at the alpha level.
Significance from p-values -- continued
• How small is a “small” p-value? This is largely a matter of semantics but if the
– p-value is less than 0.01, it provides “convincing” evidence that the alternative hypothesis is true;
– p-value is between 0.01 and 0.05, there is “strong” evidence in favor of the alternative hypothesis;
– p-value is between 0.05 and 0.10, it is in a “gray area”;
– p-values greater than 0.10 are interpreted as weak or no evidence in support of the alternative.
Chi-square Test for Independence
Under H0, the probability distribution is approximately distributed by the Chi-square distribution (2).
Chi-square
2
Reject H0 3.84
22.16
2 with 1 d.f. at .05 critical value = 3.84
5. Determine the Probability-value (Critical Value)
6 a) Compare with the level of significance,
b) Determine if the critical value falls in the rejection region. (check tables)
22.16 is greater than 3.84 and falls in the rejection area
In fact it is significant at the .001 level, which means that the chance that our variables are independent, and we just happened to pick an outlying sample, is less than 1/1000
Or, in other words, the chance that we have a Type 1 error is less than .1%
i.e. That there is a .1% chance that we reject the null hypothesis, that there is no difference between men and women with respect to brand awareness, and say that there is, when in fact the null hypothesis is true: there is no difference.
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