University of Warwick, Department of Sociology, 2014/15 SO 201: SSAASS (Surveys and Statistics) (Richard Lampard) Week 3 Multivariate analysis

Multivariate analysis Introductory modules covering quantitative methods often focus on univariate measures and then on bivariate (i.e. two-way) relationships. However, inter-relationships and phenomena in the social world are usually more complex than a bivariate analysis allows for. Multivariate analyses are thus commonly used in quantitative social research, as a reflection of this complexity. Later in the term we will look at multiple (linear) regression, logistic regression and (hierarchical) log-linear models, all of which are types of multivariate analysis. However, this week we consider the rationale for multivariate analysis in broad terms, and have a look at simple cross-tabular techniques for conducting this form of analysis.

Multivariate analysis de Vaus (1996: 198) suggests that we can use multivariate analysis to elaborate bivariate relationships, in order to answer the following questions: 1.Why does the relationship [between two variables] exist? What are the mechanisms and processes by which one variable is linked to another? 2.What is the nature of the relationship? Is it causal or non-causal? 3.How general is the relationship? Does it hold for people in general, or is it specific to certain subgroups? This is because multivariate analysis enables the identification of: Spurious relationships Intervening variables The replication of relationships The specification of relationships

Age Height Reading ability Spurious relationship Spurious relationships A spurious relationship exists where two variables are not related but a relationship between them is generated by their relationships with a third variable. For example:

Intervening variables Sometimes, although there is a real (non-spurious) relationship between two variables, we want to establish why that relationship exists. For example, if we discover that there is a relationship between risk of unemployment and ethnicity, we want to know why that is the case. One possibility is that some ethnic groups have lower educational levels and that this has implications for their ability to get work. In this case education would be an intervening variable. Intervening variables enable us to answer questions about the bivariate relationship between two variables – suggesting that (in this case) the relationship between ethnicity and unemployment is not direct but (at least in part) occurs via educational levels. EducationEthnicityUnemployment

Is it spurious or intervening? When we do statistical tests we will find similar results for a spurious variable and an intervening variable: In both cases the effect of the independent variable on the dependent variable will be moderated by the third variable. So how do we know whether this third variable provides evidence of a spurious relationship or is an intervening variable? –There is no hard-and-fast statistical rule for deciding this. –But if we are suggesting that a variable is intervening, the logic of the process must make sense – i.e. you must have a cogent theoretical reason for thinking that your independent variable affects the intervening variable which in turn affects the dependent variable. –This kind of causal process is easiest to argue for when the timing supports it, i.e. when the intervening variable can be seen to occur in between the independent and dependent variables (e.g. education in the earlier example of the relationship between ethnicity and unemployment).

Replication Sometimes when we have found a basic (‘zero-order’) relationship between two variables (e.g. ethnicity and unemployment), we want to demonstrate that this relationship exists within different subgroups of the population (e.g. for both men and women; for those of different ages…). Where the relationship IS replicated we can rule out the possibility that it is produced by the variable in question, either as an intervening variable or in a spurious way.

Specification Sometimes a particular variable only has an effect in specific situations. The variable that determines these situations is said to ‘interact’ with the independent variable. For example, an example in de Vaus’s book suggests that going to a religious school makes boys more religious but has little or no effect on girls. In this case type of school interacts with gender: religious education only affects students’ religiosity in combination with being male.

Specification (interactions) Not at allVery How religious was your education? Religiousness high low boys girls Not at allVery How religious was your education? Religiousness high low boys girls Interaction between No interaction sex and religiousness of school Graphical representation of the relationship between religious education and religiousness, controlling for sex:

Using Cramér’s V to classify a multivariate situation If the Cramér’s V values for the layers are all similar, then we have a situation of replication. If the Cramér’s V values are smaller for the layered cross-tabulation than the value for the original cross- tabulation, then we either have a situation where the third variable is acting as an intervening variable, or one where it is inducing a spurious relationship between the original two variables. Deciding between these two options involves reflecting on whether the third variable makes sense conceptually as part of some causal mechanism linking the original two variables. If we use SPSS to produce a cross-tabulation of two variables, then we can elaborate this relationship by introducing a third variable as a layer variable. Examining the Cramér’s V values for the original cross-tabulation and for the layers of the elaborated cross-tabulation tells us what kind of situation we are looking at:

If the Cramér’s V values for the layered cross-tabulation vary in size, perhaps with some being smaller than the original value and some being as large or larger than it, then the situation is one of specification. However, if one or more of the Cramér’s V values is larger than the original value, then a failure to take account of the third variable in the first instance may also have been suppressing an underlying relationship between the two variables. This latter situation is a variation on the theme of spuriousness: in this case, the absence of a bivariate relationship is spurious rather than the presence of one!) Using Cramér’s V to classify a multivariate situation (continued)

Multivariate analyses can utilise a variety of techniques (depending on the form of the data, research questions to be addressed, etc. – we will be looking at multiple (linear) regression, logistic regression and log-linear models), in order to determine whether the relationship between two variables persists or is altered when we ‘control for’ a third (or fourth, or fifth...) variable. Multivariate analysis can also enable us to establish which variable(s) has/have the greatest impact on a dependent variable – e.g. Is sex more important than ‘race’ in determining income? It is often important for a multivariate analysis to check for interactions between the effects of independent variables, as discussed earlier under the heading of specification. More generally…