This document discusses preliminary data analysis techniques. It begins by explaining that data analysis is done to make sense of collected data. The basic steps of preliminary analysis are editing, coding, and tabulating data. Editing involves checking for errors and inconsistencies. Coding transforms raw data into numerical codes for analysis. Tabulation involves counting how many cases fall into each coded category. Examples of tabulations like simple counts and cross-tabulations are provided to show relationships between variables. Preliminary analysis helps detect errors and develop hypotheses for further statistical testing.

1. DATA
ANALYSIS
Dr. Jayesh Patidar
www.drjayeshpatidar.blogspot.com

2. Introduction to Data Analysis

Why do we analyze data?
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Make sense of data we have collected
Basic steps in preliminary data analysis
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Editing
Coding
Tabulating
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3. Introduction to Data Analysis

Editing of data

Impose minimal quality standards on the raw data

Field Edit -- preliminary edit, used to detect glaring
omissions and inaccuracies (often involves respondent
follow up)

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Completeness
Legibility
Comprehensibility
Consistency
Uniformity
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4. Introduction to Data Analysis

Central office edit

More complete and exacting edit


Best performed by a number of editors, each looking at
one part of the data
Decision on how to handle item non-response and other
omissions need to be made

List-wise deletion (drop for all analyses) vs. case-wise
deletion (drop only for present analysis)
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5. Introduction to Data Analysis

Coding -- transforming raw data into symbols
(usually numbers) for tabulating, counting,
and analyzing

Must determine categories



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Completely exhaustive
Mutually exclusive
Assign numbers to categories
Make sure to code an ID number for each
completed instrument
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6. Introduction to Data Analysis

Tabulation -- counting the number of cases
that fall into each category


Initial tabulations should be preformed for each
item
One-way tabulations

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Determines degree of item non-response
Locates errors
Locates outliers
Determines the data distribution
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7. Preliminary Data Analysis

Tabulation


Simple Counts
For example
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Number of
Cars
1
74 families in the study
own 1 car
2 families own 3
Missing data (9)


1 Family did not report
Not useful for further
analysis
Number of
Families
75
2
23
3
9
2
1
Total
101
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8. Preliminary Data Analysis

Tabulation

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
Compute Percentages
Eliminate non-responses
Note – Report without
missing data
Number of
Cars
1
Number of
Families
75%
2
23%
3
Total
2%
100
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9. Preliminary Data Analysis

Cross Tabulation

Simultaneous count of two
or more items


Note marginal totals are
equal to frequency totals
Allows researcher to
determine if a relationship
exists between two
variables


Number
of Cars
Lower
Income
Higher
Income
1
48
27
75
2 or
More
6
19
25
Total 54
46
Total
100
Used a final analysis step in
majority of real-world
applications
Investigates the relationship
between two ordinal-scaled
variables
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10. Preliminary Data Analysis
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To analyze the data

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Calculate percentages in
the direction of the
“causal variable”
Does number of cars
“cause” income level?
Lower
Income
Higher
Income
Total
1
64%
36%
100%
2 or
More
24%
76%
100%
Total 54%
Cross Tabulation
46%
100%
Num
ber
of
Cars
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11. Preliminary Data Analysis

Cross Tabulation

To analyze the data


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Does income level
“cause” number of cars?
Seem like this is the
case.
In the direction of
income – thus, income
marginal totals should be
100%
Lower
Income
Higher
Income
1
89%
59%
75%
2 or
More
11%
41%
25%
Num
ber
of
Cars
Total
Total 100% 100% 100%
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12. Preliminary Data Analysis

Cross Tabulation allows the development of
hypotheses

Develop by comparing percentages across
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Lower income more likely to have one car (89%) than
the higher income group (59%)
Higher income more likely to have multiple cars (41%)
than the lower income group (11%)
Are results statistically significant?

To test must employ chi-square analysis
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13. Measurement Scales & Types of Data
Types of Data
Discrete
Continuous
Nominal
Ordinal
Interval
Ratio
The Assignment
of Numbers for
Classification
Purposes;
Categorical
Data
Quantitative Values
Providing a
Classification
According to Order
or Magnitude
Classification According
to a Continuum With
Interval Equality &
Subdivision Sensibility
Interval Data
With An
Absolute
Value of 0
Eg: Temp.
Eg: Height;
weight
Eg: VAS; SE Status
E.g. Sex, Blood
Gr
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14. Statistical Tests: Overview
Type of
data
Kind of
comparison
distribution
two
samples
Comparison
of two
one
test,
groups
sample
Data
Qualitative
Quantitative
Normal distribution
Any
2-test,
t-Test , Z test
Z test
(n>30)
for proportion
sign-test,
one sample
Mc.Nemar-test t-Test
Wilcoxon;MannWhitney-test
Chi Square
signone-sample Wilcoxon-test
Comparison independ. 2-test
one-way analysis
KruskalWallis-test
of more
samples
of variance
than two
one
Cochran’s
two-way analysis Friedman-test
groups
sample
Q-test
of variance
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