Data Analysis

Anindita C. Rao

Data AnalysisD

A

T

A

C

O

L

L

E

C

T

I

O

N

Getting data ready for analysis•Editing•Handling Blank Reponses•Coding•Categorizing•Creating data file •Programming

Data Analysis

Feel for data1.Mean2.S.D.3.Correlation 4.Frequency distribution

Goodness of Data•Reliability•Validity

Hypothesis Testing•Appropriate statistical tools

Results interpretation

Data Preparation

• Getting data ready for analysis(SPSS)• Editing• Handling Blank Reponses• Coding• Categorizing• Creating data file • Programming

Coding & Categorization

• Usually a number to each response• Coding of questionnaires• If male=1 and if female=2• Negatively worded questionsSome compromises with ethics helps you in

practical life Strongly disagree1-2-3-4-5-6-7Strongly agree

• EXCEL we designate specific columns to specific questions and responses

Descriptive statistics

• Frequency(tabulation)

• Measure of central tendency(mean, median & mode)

• Measure of Dispersion(range, variance, standard deviation & interquartile range)

Inferential Statistics

• Correlation( Direction, Strength & Significance) -1 to +1

• Parametric: Pearson’s correlation

• Non Parametric: Spearman’s Correlation & Kendall’s rank correlation(ordinal)

Data Analysis

• Simple tabulation & Cross tabulation

• ANOVA

• Correlation & Regression

• Discriminant Analysis

• Factor Analysis

• Conjoint Analysis

• Multidimensional Scaling

• Cluster Analysis

Tabulation

Male Female Total

25 35 60

Variables

FrequencyFamily Background

Frequen

cy Percent

Valid

Percent

Cumulative

Percent

Valid Nuclear72 60.0 60.0 60.0

Joint48 40.0 40.0 100.0

Total120 100.0 100.0

Descriptive Statistics

Cross TabulationTwo variable interaction

Qualification * Centrality Crosstabulation

Count

Centrality

Total-2 0 1 2 3 4

Qualification Doctorate4 6 0 18 6 0 34

Post Graduate0 4 3 20 14 8 49

Graduate0 0 5 28 4 0 37

Total4 10 8 66 24 8 120

Variable 1(nominal)

Variable 2(nominal)

Chi square

• To determine the systematic association between two variables

• Null hypothesis: no association

• Expected cell frequencies comparison with actual cell frequencies

• Greater the discrepancies, greater will be chi square statistic

Chi square test

• Two nominal variables

• Cross tabulation

• Non parametric

• SPSS code

1. Analyze from SPSS bar

2.Analyze> Nonparametric test> Chi-square

Exercise• In this case study , we are observing association between educational

background(independent variable) of the PGDBM students and their performance in the terms of grade(dependent variable) secured. We want to test at 90% and 95% confidence level, what is the level of significance of association.(refer to file in SPSS)

Educational Background Code

B.Com 1

B.E. 2

B.Sc. 3

BBA 4

B.A. 5

Grades as follows

Grade Obtained Grade Code

A 1

B 2

C 3

t test

• Significant mean difference between two groups

• Nominal variable on interval or ratio scale

• (smokers & non smokers on extent of well being)

• Sample size less than 30

• Df = N-1

• Mann Whitney U test

ANOVA

• Significant mean difference among multiple groups

• Multiple regression

Variance caused by independent variable on dependent variables simultaneously

Hypothesis Testing

• Errors

• Normal population

• Degrees of freedom

• One tailed or two tailed

• Single Population

• . p value

Univariate Techniques

Metric Data

(interval or ratio)

Nonmetric data

(nominal or ordinal scale)

One sample

•T test

•Z test

Two or More samples

Independent

•Two group t test

•Z test

•One way anova

Related

•Paired t test

One sample

•Frequency

•Chi square

•K-S

•Runs

•Binomial

Two or More Sample

Independent

•Chi square

•Mann whitney

•Median

•K-S

•K-W Anova

Related

•Sign

•Wilcoxon

•Mc Nemar

•Chi Square

Multivariate Techniques

Dependence Techniques

Independence Techniques

One dependent variable

•Cross tabs

•Anova & Covariance

•Multiple regression

•Discriminant

•Conjoint

More than one dependent variable

•Cannonical correlation

•Multiple discriminant

Variable Interdependence

•Factor Analysis

Interobject Similarity

•Cluster Analysis

•MDS

Criterion(dependent)

One Two or More

Nominal

Ordinal Interval Nominal

Ordinal Interval

One Nominal Chi Square

•Sign test•Mann whinney•Krushal Wallis Anova

Analysis of variance

Multiple discriminant analysis

Ordinal •Spearman rank Correlation•Kendall’s rank correlation

Two or More

Interval Anova Regression Analysis

Anova

Multiple Regression Analysis

Nominal Friedman two way analysis

AnovaFactorial Design

Anova

Ordinal

Interval Multiple discriminant analysis

Multiple Regression Analysis

Multiple discriminant analysis

Cannonical Correlation