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Module II: Quantitative Research
March 15 - April 16, 2015

People’s aspirations for improved wellbeing and their call for social justice require evidence-based practice to guide policies and programs. Quantitative research provides information to build evidence. Collecting, analyzing and synthesizing quantitative data makes information relevant to decision making. Quantitative research has developed tremendously over the years and constitutes the cornerstone wellbeing.


This module aims to cover techniques of quantitative research, interpretation and reporting of research findings. It reviews quantitative data collection and analysis as used in the fields of epidemiology, statistics and demography, applied in social, population and health sciences. Special emphasis is placed on equity measures and computer-assisted applications. It also introduces secondary analysis using statistical analysis computer packages, as well as producing scientific article.

Learning Objectives

At the end of Module II participants are expected to understand:

  • The basics of quantitative research
  • The quantitative research designs
  • The sampling methods and sample size calculation
  • The data collection techniques
  • The data processing approach
  • The methods of describing and analyzing data
  • The interpretation of research results
Performance Objectives

Participants are expected to be capable of:

  • Designing data collection tool
  • Data description and analyses
  • Interpretation of research results
  • Practicing secondary analysis
  • Developing and presenting scientific articles

Participants are expected to develop computer skills using:

  • EPI-INFO (for data entry)
  • SPSS (for data analysis)
  • STATA (for measuring inequities)

The module covers the following parts:

Part One: Sources of Data

This section of module II aids trainees in the development of their understanding of the various sources of data, as well as the availability of data sets for the Arab countries. Participants are introduced to the various sources of data for research. They are also introduced to the international data sets.

Part Two: Research Designs

Trainees are introduced to the various epidemiologic study designs, as well as their strengths and limitations. They are taught the potential errors in research; as well as how to prevent them at the research planning stage and control them during the data analysis and interpretation of results.

Part Three: Study Population

Trainees will develop an understanding of the methods needed to choose target population, as well as selecting the study population. Furthermore, participants are introduced to population-based and sample-based research. Lectures cover a range of topics including sampling techniques (probability and non-probability sampling), sampling hidden and heard-to-reach populations, sampling frames, sample size calculation and sampling error.

Part Four: Data Processing

This section introduces the methods of data processing, as well as quality control techniques. Participants are introduced to the types of variables, data collection techniques, data management techniques, the weighing of data prior to analysis, the data transformation process and the data presentation methods. Participants will apply concepts in group work and computer applications.

Part Five: Data Analysis

This section covers summary statistics, indices of population and health, as well as statistical inference. Participants will be expected to become familiar with the following summary statistics:

  • Descriptive statistics (measures of central tendency and dispersion)

  • Tools for summarizing qualitative data (count, ratio, proportion, rate)

  • Measures of health (cumulative incidence, incidence density, attack rate, secondary attack rate, point prevalence, and period prevalence)

  • Measures of population composition (age-child ratio, population momentum, masculinity proportion, sex ratio excess/deficit male proportion)

  • Measures of years of life lost and years of life lived with disability

  • Measures of association (relative risk, odds ratio, attributable risk, population attributable risk)

  • Measures of equity for 2 groups (rate ratio, rate difference, low-to-high ratio, shortfall)

  • Measures of equity for more than 2 groups (slope index, concentration index, index of dissimilarity)

  • Measures of correlation (pearson’s spearman’s, kendall tau, phi, cramér, kappa)

  • Inter-individual measures of equity (Gini coefficient and Lorenzo curve)


Participants are expected to become familiar with the following indices:

  • General indices (crude rate, specific rate, direct standard rate, indirect standard rate, cause-specific mortality rate, case-fatality rate, proportional mortality, proportional mortality ratio)

  • Indices of maternal and child health (maternal mortality ratio, maternal mortality rate, fetal death rate, perinatal mortality rate, neonatal mortality rate, infant mortality rate, child mortality rate, under-five mortality rate)

  • Indices of fertility (crude birth rate, general fertility rate, age-specific fertility rate, total fertility rate, gross reproduction rate, net reproduction rate, )

  • Indices of human development (human development index, human poverty index, gender-related development index, gender empowerment index)


Finally, participants are educated on the requirements for a statistical inference; these include a research hypothesis, type I error, Type II error, probability level for a, and finally, evaluating the role of chance. Relevant statistical inferences are listed below:

  • Confidence interval (for means, proportions, relative risk and odds ratio)

  • Tests of statistical significance for question problem of estimation (for a single mean, for a single proportion)

  • Tests of statistical significance for question problem of comparison (between 2 or more means, two or more proportions for independent and paired samples, for trend in proportions)

  • Tests of statistical significance for question problem of correlation (for different correlation coefficients)

  • Regression (simple linear, multiple linear, logistic)

  • Decomposition of concentration index

Part Six: Secondary Analysis

Participants will apply the knowledge above in order to write a scientific article after a secondary analysis on a social concern of their interest with an equity lens based on available data set. Participants will also benefit from the required computer application sessions.

Specific Requirements

1. Written material that includes

  • Written exercises
  • Assignments
  • Secondary analysis of data
  • Scientific article

2. Formal oral professional presentations


3. Computer application exercises