# methods@manchester: research methods in the social sciences

## Survey-related Methods

• Causal Inference
• Causal inference is concerned with the quantifying the relationship between a particular exposure (the ‘cause’) and an outcome (the ‘effect’). Implicitly or explicitly, causal inference is the primary aim of most empirical investigations, especially in medicine and behavioural science. Find out more..

• Structural Equation Modelling

Structural Equation Models are used to evaluate whether theoretical models are plausible when compared to observed data. SEMs are very general, so for example regression and factor analysis are both just special cases of SEM. Find out more...

• Latent Class Analysis
• The basic idea underlying Latent Class Analysis (LCA) is that there are unobserved subgroups of cases in the data. These unobserved subgroups form the categories of a categorical latent variable. Find out more...

• Event History Analysis
Event History Analysis allows researchers to examine the determinants or factors behind the occurrence of events over time, and is applied to longitudinal data to control for time varying covariates. Find out more...
• Risk Scores
Risk scores help classify and to predict the outcome from a set of predictors, and thus to aid decision making in conditions of uncertainty. Find out more...
• Generalized Method of Moments
Generalized Method of Moments provides a computationally convenient method for estimating the parameters of statistical models based on the information in population moment conditions. Find out more...
• Poverty Mapping
Poverty mapping is a very powerful tool of targeting mechanisms. It provides a detailed description of the spatial distribution of poverty and inequality within a country. Find out more...
• Health Trajectories

Health trajectories are descriptions (lines or curves) of how a person's health changes as they get older. In health, growth curve models are typically used to describe changes in a person's development, health or functioning as they age. Find out more...

• Multilevel Modelling

Multilevel modelling is a quantitative statistical method to investigate variations and relationships for variables of interest, taking into account population structure and dependencies. Find out more...

• Time Series Analysis

Time series analysis covers a wide range of statistical and econometric techniques designed to capture the patterns observed over time in one or more data series. Examples include monthly patterns in average recorded temperature (seasonality), the number of airline passengers since the 1960s (trend) and patterns of growth and recession in national output (business cycles). Find out more...

• Confirmatory Factor Analysis

Historically, factor analysis has been the prime statistical technique for the development of structural theories in social science, such as the hierarchical factor model of human cognitive abilities, or the Five Factor Model of personality. In confirmatory factor analysis the researcher specifies the number of factors which underlie a set of observed variables, together with the relationships between the observed variables and the factors. Find out more...

• Item Response Theory

Why do we believe that questionnaires tell us something meaningful about the inner life of respondents, their knowledge, attitudes and values? What justification do we have for using their responses to claim, for example, that one individual has a higher level of ‘civic duty’ or ‘optimism’ than another? Find out more...

• Latent Structure Analysis
• Sometimes the things we want to study can’t be observed directly, in which case we can call them ‘latent’ variables. The classic example of a latent variable is intelligence, which we infer as the reason why some people do better and some worse at abstract reasoning tests. Find out more...

• Multilevel Structural Equation Modelling

Structural equation modelling is a family of statistical models that encompasses regression-, path- and factor analysis. Find out more...

• Case-Control Study

A case-control study is a type of observational study design that is often used in epidemiology. Two groups of people are compared; one with the condition/disease (‘cases’) and a similar group of people who do not have the condition or disease (‘controls’). Find out more...

• Survey Weights

Survey weights aim to:

• inflate the sample to the level of the target population;
• reduce bias arising from nonresponse when the characteristics of the respondents differ from those not responding;
• increase the precision of estimates by utilizing known auxiliary variables that may be correlated with the survey topics. Find out more...