How to Investigate the Use of Medicines by Consumers
(2004; 98 pages) View the PDF document
Table of Contents
View the documentAcknowledgements
View the documentPreface
Open this folder and view contents1. Why study medicines use by consumers
Open this folder and view contents2. What influences medicines use by consumers
Open this folder and view contents3. How to study medicines use in communities
Open this folder and view contents4. Prioritizing and analysing community medicines use problems
Open this folder and view contents5. Sampling
Close this folder6. Data analysis
View the document6.1 Introduction
View the document6.2 Sorting and ordering data
View the document6.3 Making quality control checks
View the document6.4 Processing qualitative data
View the document6.5 Analysing qualitative data
View the document6.6 Processing quantitative data
View the document6.7 Analysing quantitative data
View the document6.8 Conclusion
Open this folder and view contents7. Monitoring and evaluating rational medicines use interventions in the community
View the documentBack cover
 

6.1 Introduction

Before conducting a study, a plan for data processing and analysis should be prepared. Such a plan helps the researcher ensure that at the end of the study:

• all the necessary information has been collected
• unnecessary data that will never be analysed are not collected.


This means that the plan for data processing and analysis must be closely linked to the study objectives and research questions, as well as a list of relevant variables.

The procedures for analysis of data collected through qualitative and quantitative techniques are quite different. For qualitative data, it is a matter of expanding notes from interviews and/or transcribing tapes, and then ordering, describing, summarizing, and interpreting data obtained for each study unit or for each group of study units. Here the researcher starts analysing while collecting the data, so that questions that remain unanswered (or new questions that come up) can be addressed before data collection is over.

For quantitative data, the variables have been defined prior to the study. Variables are characteristics of persons, objects or phenomena that can take on different values. The values of variables can be expressed as numbers (for example age, expressed in years); such variables are called numerical variables. Or, they can be expressed in categories (for example, ‘source of advice’; the categories for this variable are: no advice; family; health worker; pharmacist; and others). Such variables are ‘categorical variables’. If you develop a problem analysis diagram in preparation for a field study, you identify factors that influence the core problem, see table 4.

Note that in the table, waiting time is easy to operationalize as a numerical variable. It can be measured in minutes. The other variables can be made operational as categorical variables. Operationalizing variables means making them measurable. To measure knowledge, you could, for example, ask five questions. 0-2 correct answers can be categorized as poor knowledge, 3 as reasonable, and 4 to 5 as good knowledge. Availability of antibiotics can be measured by using a list of five different antibiotics that every health facility should have as a minimum. Likewise, availability of only 0-2 of these antibiotics could be categorized as poor, 3 as reasonable, and 4 to 5 as good.

Table 4. Factors rephrased as variables

FACTORS AS PRESENTED IN A PROBLEM ANALYSIS DIAGRAM

REPHRASED AS VARIABLES

   

Long waiting time

Waiting time

Absence of antibiotics

Availability of antibiotics

Inadequate dispensing of antibiotics

Appropriateness of antibiotic dispensing

Lack of knowledge on how to use antibiotics

Knowledge of antibiotic use

Preparation of a plan for data processing and analysis will provide you with better insight into the feasibility of the analysis to be performed, as well as the resources that are required. It also provides an important review of the appropriateness of your data-collection tools. When developing the plan it is very helpful to prepare dummy tables and charts of data.

Note: The plan for processing and analysis of data must be prepared before the data are collected in the field, so that it is still possible to make changes in the list of variables or the data-collection tools. This chapter gives you an overview of what you should consider when preparing such a plan.

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