Descriptive analysis of data

Modified on 2015/05/20 10:55 by Sean Zheng — Categorized as: Chapter 4 - Analysis and presentation of gender statistics

The degree of data processing and analysis varies according to the types of statistical products prepared by the national statistical offices. (See box IV.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. Large amounts of data are provided, often as absolute frequencies or counts of observations, making it 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 those cases, the differences between women and men may become more visible.

Gender statistics requires the cross-tabulation of at least two statistical variables: sex and the main characteristic that is studied, such as educational attainment or labour force participation. Ideally, additional variables are used in further cross-tabulation of data (for example, by age group or geographical 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 I and shown in chapter II, 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 the level of educational attainment.

A basic descriptive analysis of data involves the calculation of simple measures of composition and the distribution of variables by sex, and for each sex, that facilitate straightforward gender-focused comparisons between different groups of population. Depending upon 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 the calculation of gender indicators. Indicators, in general, are used to “indicate” how differently one group performs by comparison to a norm or reference group. Gender indicators should show how women perform in comparison to men, and their status relative to men’s status, in areas such as education, formal work, access to resources, health and decision-making. In this regard, gender indicators are important tools for planners and policymakers in monitoring progress towards gender equality.

The sections that follow 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.