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Setting the scene

Setting the scene. (Session 01). Learning Objectives. At the end of this session, you will be able to recognise situations where statistical modelling in relevant understand the purpose of modelling

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Setting the scene

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  1. Setting the scene (Session 01)

  2. Learning Objectives At the end of this session, you will be able to • recognise situations where statistical modelling in relevant • understand the purpose of modelling • for a given scenario, be able to identify the key response variable of interest and potential factors that may affect the variation in the key response

  3. Session Contents In this session you will be • provided with examples of situations where modelling is relevant to answer questions of importance in policy decisions • given the opportunity to explore examples in order to develop some insight into modelling ideas • introduced to the associated terminology

  4. Examples where modelling is relevant Two examples will be discussed initially… • Child malnutrition and feeding practices in Malawi, in Food and Nutrition Bulletin, Volume 18, No. 2, 1997. United Nations University Press, Tokyo, Japan • Gender-sensitive education statistics and indicators, in UNESCO Training Materials for workshops on Education Statistics and Indicators in Ghana (1996), Côte d’Ivoire (1997).

  5. Example 1 - Nutrition: The data come from the Malawi Demographic and Health Survey, 1992. Primary interest was in identifying factors affecting malnutrition. The factors were: • gender, age, birth size, type of breast feeding, maternal education & area of residence amongst 4-11 month olds infants • age, birth size, preceding and succeeding birth interval, if still breast feeding, no. of days with diarrhoea in past 2 weeks and other household characteristics amongst 12-59 month old children

  6. Example 2 - Education: A cross-country study to determine factors which hinder gender equality in education. One outcome variables was a gender-equity sensitive indicator (GESI). Some factors studied were: • Total fertility rate • GNP per capita • % female teachers in primary education • Male & female enrolment ratios at primary and secondary education

  7. Identifying response and regressor (explanatory) variables In each of the above examples, there was a key response of interest. This is called the dependent variable, usually denoted by y. Factors identified as possibly influencing the variability in y are called explanatory, or regressor variables. They form the x’s in the model. In statistical modelling, we assume they are measured without error. What are the y and x’s in previous examples?

  8. What is a statistical model? A model is a simple equation which relates a key response (y) of interest to one or more other variables (x1, x2, …) which are believed to contribute to the variability in the key response. For example,y = 38.1 – 1.91x, where y is perinatal mortality per 1000 live births and x the number of health centres per 1000 HHs. This describes the relationship between mortality and availability of health facilities.

  9. Purpose of Modelling • To determine a simple summary of the way that a key response (y) relates to a set of x’s • To understand factors (x’s) affecting y • To use the model equation to make predictions about y • To determine which values of the x’s will optimise y in some way

  10. Types of key response In the simplest type of statistical modelling, the key response is a quantitative measurement, assumed to follow a normal distribution. This module focuses on such responses. However, there are other types of key responses. Often have binary variables, e.g. whether or not a household is below the poverty line, whether contraceptives are used or not, person is HIV positive or not.

  11. Example 3: a binary response See Impact of HIV on tuberculosis in Zambia: a cross-sectional study, in British Medical Journal, 1990, Vol.301, pp.412-5 This includes studying the relationship of HIV-1 antibody state (yes/no) to • years of full-time education • housing (no. of people sharing bedroom) • marital state (married, single, other) • history of treatment for sexually transmitted diseases (yes/no)

  12. Example 4: a multinomial response See Patterns of Tobacco Use in the Early Epidemic Stages: Malawi and Zambia, 2000-2002, in American J of Public Health, 2005, Vol. 95, No. 6, pp. 1009-1015. This was a study relating tobacco use (none, light smoker, heavy smoker) to • age, education, occupation, religion, and • residence (rural/urban), and • marital status (married, single, other)

  13. Types of regressor variables In above examples, the explanatory (regressor) variables can be: • quantitative measurements, e.g years of education; • ordered categorical variables, e.g. extent of smoking (low, medium, high) • nominal (type of occupation); • binary (possess a specific asset or not). Quantitative x’s will be considered in sessions 1-10, and other types in later sessions.

  14. Practical work follows to ensure learning objectives are achieved…

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