The monitoring and evaluation process in project development is an attempt to collect information about variables of interest and assess changes in those variables as a function of the internal and external environment.
A variable, in statistics, is defined as an attribute or a characteristic of a case, or an object of study. Variables are different for different cases. The variability of variables is generally marked on the measurement scale, which can vary depending on the type of scale used.
Variables can be classified into three categories depending on typology:
- Independent Variable
An independent variable can take values on its own at a particular point of time. It can also be described as a factor that is selected and manipulated or controlled by the researcher.
- Dependent Variable
A variable whose value is dependent upon one or more independent variables or on other dependent variables is called a dependent variable.
- Extraneous Variables
Extraneous variables are variables that may influence the outcome of a study but are not directly related to the study.
There are four types of measurement scales that are generally used in the measurement of variables: nominal, ordinal, interval, and ratio scales.
List of recommended resources #
For a broad overview #
Data Collection and Analysis Methods in Impact Evaluation
This methodological brief guide written by Greet Peersman provides an overview of various data collection and analysis methods. It describes how a researcher should plan for data collection and analysis and outlines the importance of good data management practices.
Types of Variables in Research & Statistics
The blog post provides an overview of the different types of variables used in statistical research, along with basic definitions and examples.
Video: Data Collection & Analysis
This short video by UNICEF provides a basic overview of the process of data collection and analysis, highlighting the issues involved in choosing and using data collection and analysis methods for impact evaluation.
For in depth understanding #
This module developed by the International Program for Development Evaluation Training (IPDET) gives a detailed explanation of data collection methods, its various strategies and rules, different types of data, along with examples of some common data collection approaches.
Case study #
Fossil Fuel Prices and Air Pollution: Evidence from a Panel of 133 Countries
This paper by Kentaro Mayr and Jun Rentschler analyzes the impact of gasoline, diesel, and coal prices on air pollution in 133 countries over a 19-year period. The dataset combines variables like prices, consumption, other country-specific variables, and annual average fine particulate matter concentrations in each country’s capital city.
Infected and Stressed by Climate Variability: New Empirical Evidence from Bangladesh
The report by Iffat Mahmud, Wameeq Raza and Rafi Hossain uses primary data from a national representative sample of around 3,600 households surveyed during the monsoon and dry seasons in Bangladesh. The study links weather variables, the incidence of selected diseases, and health conditions in Bangladesh to ensure that the findings are, as much as possible, based on precise climate and health data.
