1. Data Preparation

2. Steps in Data Preparation
Editing
Coding
Entering Data
Data Tabulation
Reviewing Tabulations
Statistically adjusting the data

3. Editing Carefully checking survey data for
Completeness (no omissions)
Legibility (non-ambiguous)
Right informant
Consistency
e.g. charging something when the person does not own a charge card
Accuracy.
Most important purpose is to eliminate or at least reduce the number of errors in the raw data.

4. Solutions Ideally re-interview respondent
Eliminate all unacceptable surveys (case wise deletion) (if sample is large and few unacceptable)
In calculations only the cases with complete responses are considered (pair wise deletion) (means that some statistics will be based on different sample sizes)
Code illegible or missing answers into a a “no valid response” category
substitute a neutral value - typically the mean response to the variable, therefore the mean remains unchanged

5. 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.

6. Coding
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”

7. 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

8. Precoded Questionnaires: Sometimes you can place codes on the actual questionnaire, which simplifies data entry.
This…
Becomes this…
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

9. Are you solely responsible for taking care of your automotive service needs ___ Yes ___ No
If No who performs the simple maintenance ___________
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
How often is your automobile serviced?
 Once per month
 Once every three months
 Once every six months
 Once per year
 Other _______________

10. Code Book Col. No Question No. Question Des.
Range of permissible values
1-3
ID #
N/A 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 3
How maintenance tracked
0=not tracked, 1=auto dealer records, 2=personal records, 3=mental recollection, 4=other, 9=blank 6
How often maintenance performed
Once per 0=month, 1= 3 months, 2= 6 months, 3= year, 4= other 9=blank 7 4
Other for how often

11. 6. Which magazines do you read, choose all that apply.
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
Readers Dig. 17
MacLean's 18
National Geo. 19
Chatelaine

12. For rank order questions, separate columns are also needed
7. Please rank the following brands of toothpaste in order of preference (1-5)
 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
Colgate rank 22
Acquafresh rank 23
A & H rank 25
Pepsodent rank

13. 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

14. 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.

15. Descriptive Statistics

16. Five types of statistical analysis
Descriptive
What are the characteristics of the respondents?
Inferential
What are the characteristics of the population?
Differences
Are two or more groups the same or different?
Associative
Are two or more variables related in a systematic way?
Predictive
Can we predict one variable if we know one or more other variables?

17. Descriptive Statistics
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

18. 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.

19. Proportion (percentage)
Type of
Measurement
Type of
descriptive analysis
Frequency table
Proportion (percentage)
Category proportions
(percentages)
Mode
Two
categories
Nominal
More than
two categories

20. Type of
Measurement
Type of
descriptive analysis
Ordinal
Rank order
Median
Interval
Arithmetic mean
Ratio
means

21. 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: The 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 and is expressed in terms of percentages.

22. Frequency Tables
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?

23. 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

24. WebSurveyor Bar Chart

26. How many times per week do you use mouthwash ?
1__ 2__ 3__ 4__ 5__ 6__ 7__ 1 2 3 5 4 7 6

27. Normal Distribution 
-    a b

28. 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 (m) and the standard deviation (s).
Normal Distributions

29. Skewed Distributions
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 Distributions
have a long tail in the negative direction.
called "skewed to the left."
negative tail stops at zero

30. Kurtosis: how peaked a distribution is
Kurtosis: how peaked a distribution is. A zero indicates normal distribution, positive numbers indicate a peak, negative numbers indicate a flatter distribution)
Peaked
distribution
Flat distribution
Thanks, Scott!

31. Dispersion or variability
Summary statistics
central tendency
Dispersion or variability
A quantitative measure of the degree to which scores in a distribution are spread out or are clustered together;

32. 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)

33. the most frequent category
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.

34. number times per week consumers use mouthwash
Median
the middle observation of the data
number times per week consumers use mouthwash
Frequency distribution of
Mouthwash use per week
Heavy user
Light user
Mode
Median
Mean

35. 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 m = SX/N (population)
X = xi/n (sample)
where m/X is the population/sample mean
and N/n is the number of scores.

36. Normal Distributions with different Mean
-   1 2

37. Measures of Dispersion or Variability
Minimum, Maximum, and Range (Highest value minus the lowest value)
Variance
Standard Deviation (A measure’s distance from the mean)

38. - 1 SD
+ 1 SD
RANGE

39. Variance 2 = (x- xi)2/n ¯
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

40. Standard Deviation S =  (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.
S =  (x- xi)2/n

41. Measures of Dispersion
Suppose we are testing the new flavor of a fruit punch
Dislike Like Data x
X= 4
2= 1
S = 1
2 = (x- xi)2/n
S =  (x- xi)2/n

42. Measures of Dispersion
Dislike Like Data x
X = 4.6
2=0.26
S = 0.52
2 = (x- xi)2/n
S =  (x- xi)2/n

43. Measures of Dispersion
Dislike Like Data x
X= 3
2=4
S = 2
2 = (x- xi)2/n
S =  (x- xi)2/n

44. Normal Distributions with different SD
-   1 2 3

45. Cross Tabulation
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

46. 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.

47. Dependent and Independent Variables
The variable being studied is called the dependent variable or response variable.
A variable that influences the dependent variable is called independent variable.

48. 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. .

49. Contingency Table
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:
Observed cases
Total cases P=

50. Cross tabulation

51. Chi-square Test for Independence
The Chi-square test for independence determines whether two variables are associated or not.
H0: Two variables are independent
H1: Two variables are not independent
Chi-square test results are unstable if cell count is lower than 5