Descriptive analysis of data

Modified on 2013/05/20 17:01 by Haoyi Chen — Categorized as: Chapter 4 - Analysis and presentation of gender statistics

The degree of data processing and analysis varies by type of statistical products prepared by the national statistical offices. (See Box 4.1 for types of statistical products that may include gender statistics.) Typically, tables constructed to disseminate data collected in censuses or surveys involve minimum data processing and analysis. A large amount of data is provided, often as absolute frequencies or counts of observations, making difficult to discern the main differences between women and men. Additional processing and analysis are developed when more analytical reports or articles focused on specific topics are prepared. In this case, the differences between women and men have a chance of becoming more visible.

Gender statistics require at least two statistical variables cross-tabulated: sex and the main characteristic that is studied, such as educational attainment or labour force participation. Ideally, additional variables are used in further crosstabulation of data (for example, by age group or geographic areas) in three- or multiple-way tables. Although statistics on individuals have been traditionally disseminated as totals with no further information on women and men, data are increasingly disaggregated by sex in dissemination materials. Still, one limitation in producing gender statistics persists. Sex is often used as only one of the breakdown variables for the data presented. As explained in chapter 1 and shown in chapter 2, gender statistics and a meaningful gender analysis commonly require disaggregation by sex and other characteristics at the same time. For example, gender segregation in the labour market is partially determined by the gender gap in education, therefore data on occupations should be further disaggregated by level of educational attainment.

Basic descriptive analysis of data involves calculation of simple measures of composition and distribution of variables by sex and for each sex that facilitate straightforward gender-focused comparisons between different groups of population. Depending on the type of data, these measures may be proportions, rates, ratios or averages, for example. Furthermore, when necessary, such as in the case of sample surveys, measures of association between variables can be used to decide whether the differences observed for women and men are statistically significant or not.

Percentages, ratios, rates or averages are the basis for calculation of gender indicators. Indicators, in general, are used to “indicate” how differently one group performs by comparison to a norm or a reference group. Gender indicators should show how women perform by comparison to men, what is their status relative to men’s status, in areas such as education, formal work, access to resources, health or decision-making. In this regard, gender indicators are important tools for planners and policy makers in monitoring progress toward gender equality.

The sections following present the type of data involved in gender statistics, measures of composition and distribution used in gender statistics, and the types of gender indicators that can be constructed using those measures.