Social Statistics

5.

Data Management and Preparation

5.1.

Creating a Dataset

5.1.1.

Data Structure and Organization

5.1.2.

Rectangular Data Format

5.1.3.

Variable Naming Conventions

5.1.4.

Case Identification

5.1.5.

Data Documentation

5.2.

Coding and Data Entry

5.2.1.

Coding Closed-Ended Responses

5.2.2.

Coding Open-Ended Responses

5.2.3.

Content Analysis for Open-Ended Data

5.2.4.

Data Entry Procedures

5.2.5.

Single Entry vs. Double Entry

5.2.6.

Verification Procedures

5.2.7.

Data Entry Software

5.3.

5.3.1.

Purpose and Importance

5.3.2.

Variable Names and Labels

5.3.3.

5.3.4.

Missing Value Codes

5.3.5.

Variable Descriptions

5.3.6.

Data Collection Information

5.3.7.

Maintaining Codebook Updates

5.4.

5.4.1.

Identifying Data Entry Errors

5.4.2.

Identifying Outliers

5.4.2.1.

Statistical Methods for Outlier Detection

5.4.2.2.

Graphical Methods for Outlier Detection

5.4.2.3.

Deciding How to Handle Outliers

5.4.3.

Handling Missing Data

5.4.3.1.

Types of Missing Data

5.4.3.2.

Missing Completely at Random

5.4.3.3.

Missing at Random

5.4.3.4.

Missing Not at Random

5.4.3.5.

Listwise Deletion

5.4.3.6.

Pairwise Deletion

5.4.3.7.

Mean Substitution

5.4.3.8.

Multiple Imputation

5.4.4.

Logical Consistency Checks

5.4.5.

Range and Validity Checks

5.4.6.

Skip Pattern Verification

5.5.

Data Transformation

5.5.1.

Recoding Variables

5.5.1.1.

Collapsing Categories

5.5.1.2.

Creating Dichotomous Variables

5.5.1.3.

5.5.2.

Creating New Variables

5.5.2.1.

Computed Variables

5.5.2.2.

Conditional Variables

5.5.3.

Creating Composite Measures

5.5.3.1.

Additive Indexes

5.5.3.2.

Weighted Indexes

5.5.3.3.

Scale Construction

5.5.3.4.

Assessing Reliability of Composite Measures

5.5.4.

Data Standardization

5.5.4.1.

Z-score Standardization

5.5.4.2.

Min-Max Normalization

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6. Descriptive Statistics: Univariate Analysis