TOPIC INFO (UGC NET)
TOPIC INFO – UGC NET (Geography)
SUB-TOPIC INFO – Geographical Techniques (UNIT 9)
CONTENT TYPE – Detailed Notes
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1. Introduction
2. Composite Index: Concept
3. Steps in Constructing Composite Index
4. Dealing with Missing Values and Outliers
5. Methods to Construct Composite Index
5.1. Simple Ranking Method
5.2. Indices Method
5.3. Mean Standardisation Method
5.4. Range Equalisation Method
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Making Indicators Scale Free
UGC NET GEOGRAPHY
Geographical Techniques (UNIT 9)
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Table of Contents
Introduction
- In social sciences research, many a times the complex social and economic issues like child deprivation, food security, human well-being, human development etc. are difficult to measure in terms of single variable. The reason being that such issues have several dimensions and indicators.
- For example, it is difficult to explain the status of development of a district in terms of a single variable because development is reflected in terms of several indicators. Some of such variables/indicators are quantitative type while others are of qualitative nature.
- In such situations Composite index plays an important role to express the single value of several interdependent or dependent variables. Further, the composite index makes it possible to compare the performance among region/states or districts etc.
- Hence, composite indexes are being recognised as useful tool for policy analysis. Composite indexes can also be resorted to make comparison among different regions/sectors where wide range of variables is used.
Composite Index: Concept
- A composite index is an expression in a single score made by combination of different scores to measure a given variable or a group of variables. It expresses quantity or place (position) of multi facet aspects of a concept. The UNDP (2005) has explained that ‘a composite index expresses a quantity or a position on a scale of qualitative multi-faceted aspects….. which is relevant for information of the society’. An index can be a combination of independent indicators, or the average of a set of selected indicators. The index may represent specific concept or highlight specific sector or areas like status of dalit (dalit deprivation index), food situation (food security/insecurity index), status of child (child deprivation/development index), quality of human development (human development index), etc.
- The index that we construct is the outcome of some unidirectional variables or indicators. If an index is constructed by taking positive indicators, the higher value of index implies higher development and lower values imply lower development. For example, in case of index related to child development, if the variables are positive directional, the final index can be termed as ‘child development index’. On the other hand, if the variables are negative directional, it is called ‘Child Deprivation Index’. Let us take an example that child mortality is a negatively directional variable whereas percentage of children immunised is a positively directional variable. Such index is very useful in case of qualitative data. An index is more robust than a single indicator or variable.
- The choice of indicator is a big challenge for researchers. The major issues in identifying measurable indicators are: whether data are available or not, whether we have reliable data, whether the data are cross section or time series, and minimisation of double counting arising from overlap or redundancy. Again if the variables to be chosen are easy to understand, they are more acceptable to a wider audience.
Steps in Constructing Composite Index
The following steps are required to be followed in construction of composite index:
| Steps | Why it is Needed? |
|---|---|
| 1. Theoretical framework A theoretical framework need to be developed because it provides the basis for the selection and combination of variables into a meaningful composite indicator under a fitness-for-purpose principle (involvement of experts and stakeholders is envisaged at this step). | To get clear understanding and definition of the multidimensional phenomenon to be measured. To structure the various sub-groups of the phenomenon (if needed). To compile a list of selection criteria for the underlying variables, e.g., input, output, process. |
| 2. Data selection Indicators should be selected on the analytical soundness, measurability, country coverage, and relevance of the indicators to the phenomenon being measured and relationship to each other. The use of proxy variables should be considered when data are scarce (involvement of experts and stakeholders is envisaged at this stage). | To check the quality of the available indicators. To discuss the strengths and weaknesses of each selected indicator. To create a summary table on data characteristics, e.g., availability (across country, time), source, type (hard, soft or input, output, process). |
| 3. Imputation of missing data Consideration should be given to different approaches for imputing missing values. Extreme values should be examined as they can become unintended benchmarks. | To estimate missing values. To provide a measure of the reliability of each imputed value, so as to assess the impact of the imputation on the composite indicator results. To discuss the presence of outliers in the dataset. |
| 4. Multivariate analysis Should be used to study the overall structure of the data set, assess its suitability, and guide subsequent methodological choices (e.g., weighting, aggregation). | To check the underlying structure of the data along the two main dimensions, namely individual indicators and countries (by means of suitable multivariate methods, e.g., principal components analysis, cluster analysis). To identify groups of indicators or groups of countries that are statistically “similar” and provide an interpretation of the results. To compare the statistically-determined structure of the data set to the theoretical framework and discuss possible differences. |
| 5. Normalisation Should be carried out to render the variables comparable. | To select suitable normalisation procedure(s) that respect both the theoretical framework and the data properties. To discuss the presence of outliers in the datase |
Caution on Choosing Variable:
- Whenever we choose any variable or a particular dimension for the index, we have to justify the inclusion of the variable into the index. This justification should come from empirical evidence or policy based research studies or from theoretical explanations.
- If the variable is not unidirectional, the entire variables used should be converted to unidirectional. For example, in construction of the food security index, two variables like ‘proportion of agricultural worker to total workers’ and per capita value of agricultural output’ is used for index. Here the first variable is a negatively directional whereas the second variable is positively directional. In this case we have to convert the entire variables into either positive direction or negative direction. If we want to convert this to positive direction, the first variable which has a negative direction should be deducted from 100. On the other hand, if we want to convert the all the variables into negative direction, we have to work out the reciprocal of per capita value of agricultural output.