Approaches to the Analysis of Survey Data

Topics: Data analysis, Quantitative research, Qualitative research Pages: 39 (9401 words) Published: October 12, 2013
Approaches to the
Analysis of Survey Data
March 2001

The University of Reading
Statistical Services Centre
Biometrics Advisory and
Support Service to DFID

© 2001 Statistical Services Centre, The University of Reading, UK

Contents
1. Preparing for the Analysis

5

1.1

Introduction

5

1.2

Data Types

6

1.3

Data Structure

7

1.4

Stages of Analysis

9

1.5

Population Description as the Major Objective

11

1.6

Comparison as the Major Objective

12

1.7

When Weighting Matters

13

1.8

Coding

14

1.9

Ranking & Scoring

15

2. Doing the Analysis

17

2.1

Approaches

17

2.2

One-Way Tables

18

2.3

Cross-Tabulation: Two-Way & Higher-Way Tables

18

2.4

Tabulation & the Assessment of Accuracy

19

2.5

Multiple Response Data

20

2.6

Profiles

21

2.7

Looking for Respondent Groups

22

2.8

Indicators

23

2.9

Validity

25

2.10

Summary

26

2.11

Next Steps

26

© SSC 2001 – Approaches to the Analysis of Survey Data

3

4

© SSC 2001 – Approaches to the Analysis of Survey Data

1. Preparing for the Analysis
1.1 Introduction
This guide is concerned with some fundamental ideas of analysis of data from surveys. The discussion is at a statistically simple level; other more sophisticated statistical approaches are outlined in our guide Modern Methods of Analysis. Our aim here is to clarify the ideas that successful data analysts usually need to consider to complete a survey analysis task purposefully.

An ill-thought-out analysis process can produce incompatible outputs and many results that never get discussed or used. It can overlook key findings and fail to pull out the subsets of the sample where clear findings are evident. Our brief discussion is intended to assist the research team in working systematically; it is no substitute for clear-sighted and thorough work by researchers. We do not aim to show a totally naïve analyst exactly how to tackle a particular set of survey data. However, we believe that where readers can undertake basic survey analysis, our recommendations will help and encourage them to do so better.

Chapter 1 outlines a series of themes, after an introductory example. Different data types are distinguished in section 1.2. Section 1.3 looks at data structures; simple if there is one type of sampling unit involved, and hierarchical with e.g. communities, households and individuals. In section 1.4 we separate out three stages of survey data handling – exploration, analysis and archiving – which help to define expectations and procedures for different parts of the overall process. We contrast the research objectives of description or estimation (section 1.5), and of comparison (section 1.6) and what these imply for analysis. Section 1.7 considers when results should be weighted to represent the population – depending on the extent to which a numerical value is or is not central to the interpretation of survey results. In section 1.8 we outline the coding of non-numerical responses. The use of ranked data is discussed in brief in section 1.9.

In Chapter 2 we look at the ways in which researchers usually analyse survey data. We focus primarily on tabular methods, for reasons explained in section 2.1. Simple one-way tables are often useful as explained in section 2.2. Cross-tabulations (section 2.3) can take many forms and we need to think which are appropriate. Section 2.4 discusses issues about ‘accuracy’ in relation to two- and multi-way tables. In section 2.5 we briefly discuss what to do when several responses can be selected in response to one question.

© SSC 2001 – Approaches to the Analysis of Survey Data

5

Cross-tabulations can look at many respondents, but only at a small number of questions, and we discuss profiling in section 2.6, cluster analysis in section 2.7, and indicators in sections 2.8 and 2.9.

1.2 Data Types...
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