Data Analysis PPT

Data Analysis Steps in Data Analysis:  Data Preparation  Data Summary  Choice of Data Analysis Method (s) Techniques  Obtaining Results

Data Preparation Editing: To detect, diagnose and correct fault in a raw data-cooked up data in relevant informationinadequate and inconsistent information Categorizing Information: To classifying the grouping if any, to separate Unstructured and structured responses. Preparation of code book: Preparing the list of all variable and observation, Identifying the limit etc. Preparation of Master Table: Consolidating all variable and observation Information in a tabular format. Summarizing Data  Understanding Each Variable  Nominal Scale Data  Ordinal Scale Data  Interval and Ratio Scale Data  Understanding Relationships

Selection Appropriate Data Analysis Techniques: (i) what kind of analysis is required as per the research, (ii) what kind of analysis is possible on the data collected (to be collected) and, (iii) Is there an appropriate technique which satisfies both (i) and (ii) above.  Data Analysis Requirement  Type of Study objectives (description, causation, association etc.)  Number of variables in the study  Whether there is some pre-specified set of dependent variables Data Analysis Feasibility: Type of measurement / scale are amenable to certain type of manipulation

A Classification for selection of techniques:  No. of variables: One, scale type; any of the four (Univariable Analysis)  No. of variables: Two, whether partitioned or not, scale type any of the four for each variable (Bivariate)  No. of variable: Several (multivariate analysis)  Dependence: number of dependent variables: one or more, scale of each of the dependent / independent variables: any of the four  Interdependence: scale of each of the variables: any of the four

1. Techniques for Univariate Analysis: Appropriate Techniques for Univariate Analysis Type of Scale

Measure of Central Tendency

Nominal

Test of Significance

Dispersion

Mode

Chi-square Percentage response in each category

Ordinal

Median

Interval/Ratio

Arithmetic mean

Percentage response in each category

Rank-Correlation T test / F Test

Variance / Standard deviation

2. Bivariate Analysis: Appropriate Statistical Techniques for Bivariate Analysis Dependent Variable

Independent Variable

Interval

Nominal

Ordinal

Nominal

Chi-Square Contingency

Coefficient of differentiation

Correlation ratio

Ordinal

Coefficient of differentiation

Spearman’s rank correlation

Point multi-serial correlation

Interval

Correlation ratio

Point multi-serial correlation

Regression / correlation analysis

3. Multivariate Analysis: Single Dependent Variable Dependent Variable Analysis Dependent Variable

Independent Variable

Interval

Nominal

Ordinal

Nominal (binary)

Automatic Interaction Detection (AID)

Non-parametric Discriminant Analysis

Two group Discriminant Analysis

Ordinal

Analysis of variance

Cuttman-Lingoes CH-2 Regression

Carrol’s monotone regression

Interval

Analysis of variance, regression (AID)

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Regression Analysis

Multivariate Analysis: More than one dependent variable Canonical correlation and Analysis of Variance and Covariance.

Multivariate Analysis: Interdependence: Factor Analysis, cluster analysis and multi-dimensional scaling

Techniques of Data Analysis 1. Chi-Square Analysis: An Example:

Recall of an Ad Recall

Observed (f)

Expected (F)

Yes

30

50

No

70

50

Total Sample

100

100

2. Multivariable: o Testing Significance

Observed vs Expected Frequencies Recall

Newspaper

Magazine

Total

Yes

30 (40)

30 (20)

60

No

70 (60)

20 (30)

90

Total

100

50

150

o Estimating Association: Contingency Coefficient

Chi-Square= 16 at 5% reject Ho.