Title: STEP WISDOM
Course: UGQuants (SSPS) > Section: Multiple Regression

STEP WISDOM

When entering variables in multiple regression, SPSS allows you to do this in a number of ways.  With the enter method, variables are inserted into the model regardless of their 'performance' in terms of improving the model fit.  This is almost always the best option to choose.  It can be done all at once, or we can insert the variables in successive blocks, in what is known as hierarchical inclusion.  This means that we could decide the order in which we include the various independent variables that we are interested in, allowing us to see what additional contribution to explanation each variable (or group of variables) makes beyond what has already been included.  The order can be decided for theoretical reasons, or to allow particular focus on certain variables.

As an alternative SPSS can use the significance values corresponding to the t statistic to select variables from a list for inclusion in a model.

Forward | Selection starts with what SPSS considers to be the best performing variable and then the next best and so on until the addition of subsequent variables does not improve the model significantly.
Backward | Selection does the reverse: SPSS puts all the variables in, and then the worst and then next worst and so on are removed.
Stepwise | Selection, probably the most commonly used procedure, alternates between forward and backward, considering whether to remove any of the existing variables each time it adds a new one.

If you look at journal articles published using regression analyses you will soon discover that stepwise is a very popular procedure.  However, just because something is popular does not mean it is good.  The trouble with stepwise is that it uses purely statistical criteria to consider the importance of the variables.  This may produce a sensible model, but it may equally well produce nonsense that is purely the effect of chance patterns of random measurement error in the data.  As we have continually stressed in this course, good hypotheses, and therefore good models, have their origin not in the empirical data but in our heads: in our theories.

Using statistics to construct a model, rather than to test it, is a bit like imagining that 'the facts', sufficiently laboriously and comprehensively collected and measured, will 'speak for themselves.'  Critical social scientists should stay in control of their models and, unless they have very good reasons for doing otherwise, choose to enter variables in a model for a reason, and avoid stepwise procedures.

There is, of course, a further reason why we should avoid significance-based variable selection in the particular example we are considering here: our data do not come from a sample but from a population.  We have not drawn any random sample of the countries of the world, nor would it have made any sense for us to do so.  This is a small enough population for us to be able to measure all its members.  In such a situation, the significance statistics and standard error values that SPSS produces automatically (because it always assumes that the data it is processing comes from a sample) have no meaning.