Measuring poverty Flashcards
What are indicators in research, and why are they important?
Indicators = measures used to represent more complex or abstract phenomena.
They provide a quantifiable and standardized way to assess and compare concepts that are not directly observable.
Explain the concept of operationalization and provide an example.
Operationalization = process of turning abstract concepts into measurable observations. It involves defining a concept in terms of specific, observable, and measurable characteristics.
For example, ‘academic performance’ can be operationalized using indicators like grade point average or attendance record.
What is the Multidimensional Poverty Index (MPI), and what are its three main dimensions?
The MPI = multidimensional measure of poverty that goes beyond income-based thresholds.
- 3 main dimensions: Health, Education, and Living Standards
A person is identified as multidimensionally poor if they are deprived in at least one-third of the weighted indicators.
List three advantages of using the MPI to measure poverty.
Advantages of the MPI include:
- standardized methodology allowing for cross-country and regional comparisons,
- ability to capture the multifaceted nature of poverty,
- identifying specific areas of deprivation.
Describe two methods for measuring poverty besides the MPI.
Two alternatives:
- poverty thresholds, which define a monetary income level below which individuals or households are considered poor,
- subjective well-being (SWB) surveys, which measure individuals’ self-perceived quality of life and well-being.
Explain the difference between a concept, a variable, and an indicator.
Concept = abstract idea or phenomenon being studied, such as ‘poverty.’
Variable = specific property or characteristic of the concept that can vary, such as ‘income level.’
Indicator = measurable way to quantify the variable, like ‘annual income in dollars.’
What are the potential disadvantages of relying solely on operationalization in research?
Over-reliance on operationalization can lead to oversimplification of complex concepts, potentially missing nuances and subjective experiences.
What is the difference between exploratory and confirmatory data analysis?
Exploratory data analysis seeks to identify patterns, trends, and relationships in data without preconceived hypotheses.
Confirmatory data analysis tests specific hypotheses and uses statistical methods to determine the strength and significance of relationships between variables.
What is a null hypothesis, and how is it used in statistical testing?
A null hypothesis is a statement that assumes there is no relationship or effect between the variables being studied.
Rejecting the null hypothesis suggests evidence for the alternative hypothesis, indicating a potential relationship between variables.
Briefly explain the purpose and application of a t-test.
A t-test is used to compare the means of two different groups and determine if the difference is statistically significant.
It examines whether the observed difference in means is likely due to chance or a genuine difference between the populations. For example, a t-test could be used to assess if there’s a significant difference in average income between men and women in a given population.
