of eligible children enrolled in primary educationRatio of female to male students enrolled secondary education (if genders are treated equitably, the ratio = 1)Percent of eligible children enrolled in tertiary (post-high school) educationOur research questions in this chapter are:
● How did life expectancy vary around the world in 2016?
● How did each of the variables listed in the table above vary in 2016?
● How do we best describe the co-variation between each factor and life expectancy?
Applying an Analytic Framework
In any statistical analysis, it is essential to be clear-minded about the research questions and about the nature of the data that we will be analyzing. We have mentioned these before, and this is a good time to review them in the context of a larger research exercise.
Data Source and Structure
We know these indicators are published by the World Bank for the purpose of monitoring economic development, identifying challenges, and assessing policy interventions. The figures are determined by analysts at the World Bank using estimates gathered and provided by governmental agencies from each country. Each of the annual indicators is best described as observational data, as opposed to experimental or survey.
In this data table, we have 42 variables, or data series, observed for 215 countries for each year from 1990 through 2018. Hence, we have 29 years of repeated observations for 215 countries, giving us 6,235 rows. Many cells of the table are empty, reflecting difference in national statistical infrastructure or, in some cases, simply reflecting periods before the World Bank began monitoring a development indicator. We have relatively few observations for 2018.
Recall the difference between cross-sectional data (many observations selected at one time from a population or process), and time series or longitudinal data (regularly repeated observations of a single observational unit). In this table, we have a combination: we have 29 sets of repeated cross-sectional samples.
Observational Units
Each row in the data table represents a country in a particular year. We will be dealing with aggregated totals and rates rather than with individual people.
Variable Definitions and Data Types
Before we dive into analysis, it is critical to understand what each variable represents, as well as the type of each variable. We need to understand the measurements in order to interpret them. We need to know the data types because that guides the choice of summary or descriptive techniques that are applicable.
Table 5.2: Data Types and Definitions for All Variables
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