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From |
Urmi Bhattacharya <ub3@indiana.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Exploratory factor analysis using a mix of categorical and continuous variables |

Date |
Fri, 3 Feb 2012 17:33:25 +0530 |

Hi Nick, Thank you for sharing your thoughts on this. It was most helpful. You are right in the sense that I want to use factor analysis as an exploratory tool. The objective is to see if the 16 variables I am using to proxy what I call "school quality" are measuring the same aspect of school quality. In that case I can use factor analysis to see if the job can be done by a fewer number of variables. Best Urmi Bhattacharya On Fri, Feb 3, 2012 at 3:22 PM, Nick Cox <njcoxstata@gmail.com> wrote: > I don't see that this can be easily classified as correct or incorrect. > > Some people recommend strongly against such a mix, and some people > would argue that the pragmatic defence is whether it provides > interesting or useful results. > > As you yourself have labelled the exercise "exploratory" the acid test > is surely what do you learn from the data by examining the results. > > This is a cross-disciplinary list and we can only report our own > perspectives. In what is nominally my own discipline, geography, > factor analysis in the sense of an exploratory exercise throwing all > the data into one pot, stirring and seeing what you got, was probably > the most popular technique of all in the late 1960s and early 1970s. I > met several people whose one statistical idea was to read everything > into SPSS and do a factor analysis. I even met some people who did not > know that there were simpler statistical techniques. In geography this > fashion faded rapidly as too many people did not understand what they > were doing or found no useful new results. However, I am now touching > on quite different stories. > > In terms of your question, my only guess is that from your variable > names you have a ragbag here and you won't find much interesting or > useful structure. It is better to decide what are your response or > outcome variables that you most want to explain or predict and think > how those might be modelled. The basic problem is not soluble by using > a slightly different multivariate command. Having a mix of predictor > types, dummies, categorical and continuous variables, is of course a > soluble problem. > > Nick > > On Fri, Feb 3, 2012 at 9:32 AM, Urmi Bhattacharya <ub3@indiana.edu> wrote: > >> I am using exploratory factor analysis to generate factor loadings and >> the corresponding uniqueness values using 16 variables. I have a mix >> of dummy variables (taking values 1 or 0), categorical variables >> (positive integers), and continuous variables. I am using the >> following command: >> >> factor govt_school chais_desk_s schl_toilet_s schl_water_s >> hrs_electric_s num_classoutdoor_s num_mixedgrade_s reg_fee_gen_s >> tuit_fee_ gen_s pupil_teach_s inservice_training_s library_s >> computer_use_s playgrnd_s formal_teach_eval_s distance_primaryschool, >> ipf factor(1). >> >> My question is whether this is the correct procedure to use when I >> have variables that are not continuous? If not, is there a command in >> Stata that better handles this? > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Exploratory factor analysis using a mix of categorical and continuous variables***From:*Urmi Bhattacharya <ub3@indiana.edu>

**Re: st: Exploratory factor analysis using a mix of categorical and continuous variables***From:*Nick Cox <njcoxstata@gmail.com>

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