Factor Analysis Intro (Marketing Research Module 5, Video 6)

so in the next set of videos we'll be looking at a factor analysis which is a different technique that's similar to cluster analysis and spirit but it serves a very different purpose let's take a look at what that is so in cluster analysis we had some data which i'm representing here and we had a bunch of respondents so each line was one participant one respondent and each column was a bunch of different variables in cluster analysis we tried to reduce the number of variables on this y-axis so instead of having 100 unique individuals we would say we had three separate clusters of individuals factor analysis does something very similar but it does it on the other dimension what we try to accomplish is we say that rather than having say 20 different unique variables that we're measuring perhaps those variables represent some subset of constructs or some ideas and so factor analysis will allow us to statistically take a large number of factors lots of dimensions and reduce them into a smaller set of dimensions so factor analysis is a technique that combines questions or variables to create new factors the idea is that if you've got lots of different questions they might sum up into these individual factors and the purpose is two-fold one is data reduction to reduce the number of variables to a more manageable set of factors and two much more importantly is substantive interpretation it's to identify underlying constructs in the data and this makes it easier to understand the data because if we're trying to understand a set of individuals on 50 different dimensions that's very very hard to do but if those 50 dimensions really only represent a couple of underlying factors that's a whole lot easier for us to be able to perform factor analysis there needs to be some sort of relatedness or correlation between underlying variables so imagine for a second that we have a set of data such that there are only two attributes and there's four different responses a b c and d if the responses are arranged in the way that they are in this graph in the bottom left it's impossible to do a factor analysis because the level of attribute one does not depend on the level of attribute two there's no relatedness and so we'd say these four dimensions capture four unique traits that we're trying to measure on the other hand if we observe something like this now this is obviously an extreme perfect correlation it doesn't have to be quite so perfect but if we observe any kind of relationship we might say well hold on a second it looks like as you move up in attribute 4 you also move up in attribute 3. so maybe it's the case that attribute 4 and 3 are actually measuring something very similar if not the same thing and if there is any of this correlation we could perform factor analysis so what i want to do is before we get to spss i want to show you what factor analysis does under the hood that way you can understand what's going on when we actually do the analysis in spss so imagine there was a survey conducted by best buy and best buy identified nine different attributes of their retail stores and their service that influence consumer store choice in other words where they shop best buy wants to know do consumers think in more general evaluative terms which are in fact composites of these nine specific attributes in other is it really that there are nine unique different things that people care about or those nine things represent something smaller some smaller subset of dimensions and if that's the case best buy can use those broader dimensions to define areas of planning and action which is great that's what we want we want to help facilitate decision making and so factor analysis is going to help us identify these broad dimensions or factors as we call them from data on detailed consumer evaluations so imagine that best buy asked nine questions about their store and their service they were measured on very good very poor so the questions were things like how good is the price level the store personnel the return policy and so on and you might argue that these nine things are totally unique but what factor analysis is going to do is it's going to statistically check and see if some of these are related to one another sufficiently to allow us to pool them into underlying factors so what it's going to do is something like this it's going to create a correlation table we've seen these already and it's going to pull out values that are particularly large so i know that's hard to see here so i'll circle them for you what this is basically saying is something like a5 product quality is very related a1 price level a3 return policy is very correlated to a2 store personnel and so on if that's the case what we could do is rearrange this table so i'm going to leave the exact same correlation coefficients and i'm going to rearrange this so that i group them and what you see here is that it looks like these four questions here a 3 a 8 8 9 and a 2 are all highly correlated with one another and to a lesser extent correlated with other dimensions a6 a7 and a4 are all correlated with one another and two lesser extent with other dimensions and a1 and a5 are correlated with one another and to a lesser extent with other dimensions and so what this tells me is that these four these three and these two questions somehow hang together so let me put this on a different figure for you and what we might do is say well what do these factors actually represent so these four questions return policy in store service store atmosphere store personnel they seem to refer to the in-store experience whereas assortment depth assortment width and product availability seem to be related to something like product offerings finally price level and product quality are related to something like value so instead of having to think about nine different dimensions that best buy customers are evaluating the store on we now only have to think about three and if we found that there's some sort of inadequacy in any one of these three we might be able to act and change that and make our consumers happier so factor analysis assumes that the correlation between a number of variables is due to their all being dependent on the same underlying factor this is an assumption that we're making so for instance perception of in-store experience like we saw a moment ago is a function of these four related concepts and this is what factor analysis is going to allow us to do so what we're going to do is go through a couple of examples in spss in the next set of videos so you can get a feel for how this works with some real data

Channel: Data Demystified

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Keywords: marketing research, tepper, marketing research course, introduction to spss, marketing resarch mba, statistical analysis, 45830, regression, regression analysis, regression and business, regression spss, regression analysis for business, decision making regression, regression case study, cluster analysis, factor analysis, cluster analysis spss, factor analysis spss, difference between cluster and factor analysis

Id: uvRgMnyon2Y

Length: 6min 18sec (378 seconds)

Published: Sun Jan 03 2021