As some of you will know, there have been many articles and commentaries over
the years decrying the use of Excel for serious statistical analysis. Here
is a presentation that summarizes many of the issues.
http://biostat.mc.vanderbilt.edu/wiki/pub/Main/TheresaScott/StatsInExcel.TAScott.slides.pdf
For an introductory class where one wants to keep things relatively simple
(and cheap), I would suggest using something like JASP instead.
https://jasp-stats.org/current-functionality/
HTH.
bdates wrote
> I'll weigh in briefly. I teach a grad course in research methods and stats
> in which I've used SPSS as a mandate of the program. Recently, the
> program, recognizing that the likelihood of any of the students actually
> doing research in the future was close to nil, has moved to the use of
> Excel. That's good to the extent to which student's have more familiarity
> with Excel and zero with SPSS; so they've not just been forced to learn
> statistics and research methods, but a completely foreign software.
>
>
> With that said, in preparation to move from using SPSS for assignments,
> I'm exporting the datasets from SPSS to Excel and finding that the
> structure of data in SPSS is not conducive to analysis in Excel.
> Furthermore, Excel's structure seems clumsy. For example, to do an
> independent samples t-test in Excel, entering the data ranges requires
> that either two columns/variables be created - one for each group; or if
> there is a group variable (e.g., treatment and control), the ranges
> require that users copy first those values for one group into a range, and
> then for the other group into the second range. The likelihood for error
> with all the copying and pasting is increased quite a bit.
>
>
> Then there's the problem of completeness of analysis. The methods used in
> either the basic correlational analysis or the Analysis Tool Pack gives
> only the correlation, totally devoid of significance, intercept, standard
> error, or confidence interval limits.
>
>
> I'll use Excel because it represents a move the program has now mandated,
> but with all the difficulty inherent in the lack of familiarity with SPSS,
> it probably is more intuitive tool for analysis in the long run.
>
>
> Brian Dates
> ________________________________
> From: SPSSX(r) Discussion <
> SPSSX-L@.UGA
> > on behalf of Reka Solymosi <
> reka.solymosi@.AC
> >
> Sent: Tuesday, June 12, 2018 5:59:39 AM
> To:
> SPSSX-L@.UGA
> Subject: Re: SPSS vs R
>
>
> Dear John,
>
>
>
> Thank you for all the detailed comments. While I never taught in SPSS (so
> I cannot comment on that experience like Juanjo) I did receive all my
> training in SPSS. It works in a university setting great, but once I left
> academia, it was not very useful for me. Licences for SPSS are expensive,
> and are *per PC* from what I remember. I worked at a local council as a
> transport planning analyst, and then later as a crime analyst in London.
> Neither were in well-funded places. I had access to Excel, and MapInfo. I
> self-taught R and it was great not only because of it being free and
> because of all the support and community around the open source ethos of
> it, but because of the flexibility. There are an ever growing number of
> packages available for R for free, which means that it can be used yes for
> graphics, but also for statistical modelling, for network analysis, for
> text analysis, for data mining, as a GIS, to build interactive dashboards,
> to build presentations, and mostly for reproducible data manipulation and
> analysis. I have yet to find any other tool (other than maybe writing SQL
> and more recently Python) that allows such a range in data querying,
> manipulation and cleaning.
>
>
>
> That said, bringing it back to teaching in academia, I agree completely
> with your suggestion that students need an introduction before throwing
> them into R. I teach the first semester course (the one that students
> take before moving on to the R based one) in Excel. I chose Excel over
> SPSS simply due to the fact that no matter where they will go, there will
> be Excel, even in the poorest local council. It gives enough of an
> introduction to data analysis that they can then move on to R. So in that
> sense we are following the suggestion you make, that they start with an
> easier route to contingency tables via excel, and then move on to R. Excel
> can actually be a very powerful tool for data analysis if used right, and
> also an accessible route in to interpreting, understanding, and examining
> data.
>
>
>
> To be honest I think any way we can get students interested in data
> analysis is good. I don’t hugely care if it’s SPSS, Excel, R, STATA, etc,
> I think the most important is the core concept and getting students
> interested in data analysis. I think we manage to achieve that here, we
> even have a few of our graduating students this year applying for a
> masters in data science, which coming from a criminology programme I think
> is somewhat unusual! But I also appreciate that we are standing on the
> shoulders of giants in a way. I for one have made so much use of the work
> and support from those like yourself with numerous years of experience in
> teaching and researching the best ways for teaching quantitative methods.
> I think there is a lot of very valuable material there, and I think that
> it can be applied to any platform. I used many materials and resources
> that showed examples in SPSS, and translated those to Excel or R.
>
>
>
> Ideally some resource for sharing platform agnostic resources could
> actually be compiled and perhaps shared around? My material on using Excel
> is all available here:
https://maczokni.github.io/MSCD/ I know that last
> summer I sent around quite a few requests for help to the quantitative
> methods teaching list, and I would be happy if I could pay back somehow.
> Maybe some central open-source repository of training material that can be
> applied by any one to any platform they choose to use, but that is based
> on all the work everyone is doing, to bring us all together?
>
>
>
> Let me know any thoughts!
>
>
>
> Many thanks,
> Reka
>
>
>
> From: John F Hall [mailto:
> johnfhall@
> ]
> Sent: 12 June 2018 10:24
> To:
> SPSSX-L@.UGA
> Cc: Juan Medina-Ariza; Reka Solymosi
> Subject: SPSS vs R
>
>
>
> As part of the ESRC-Nuffield Q-step initiative
> (
http://www.nuffieldfoundation.org/q-step) to improve quantitative methods
> teaching in undergraduate social science degrees in the UK, a new
> one-semester quantitative criminology course is being taught to
> undergraduates at Manchester using R, mainly because of its graphic
> capabilities.
>
> See
http://jjmedinaariza.github.io/R-for-Criminologists/ for full course
> notes.
>
>
>
> In the accompanying pedagogical rationale Professor Juanjo Medina explains
> why (although he admits that R has problems with crosstabs at which SPSS
> is excellent.)
>
>
>
> It is simple. I was sick to the bone of teaching with SPSS. Why should I
> bother to be a publicist for IBM? . . .But I never quite fell in love with
> SPSS, its ugly graphic system, its patched up inconsistent menu design,
> etc. Its whole architecture, easy in the eye for casual users, seem
> designed to encourage bad habits among future analysts. In the meantime I
> continue using a variety of tools for my own research (STATA, MPlus, etc)
> until I met R and fell in love with it.
>
> See
>
https://rawgit.com/jjmedinaariza/LAWS70821/master/rcommander.html#motivation
>
>
>
> As someone who had to teach and assess Data Management and Analysis (at
> both undergraduate and postgraduate level) within a tight 13-week semester
> I still feel that SPSS is an easier, and better, route to Quantitative
> Methods (via contingency tables rather than multivariate statistics)
> perhaps leading to R at a later stage.
>
>
>
> John F Hall MA (Cantab) Dip Ed (Dunelm)
>
> [Retired academic survey researcher]
>
>
>
> Email:
> johnfhall@
> <mailto:
> johnfhall@
> >
>
> Website: Journeys in Survey
> Research<http://surveyresearch.weebly.com/>
>
> Course: Survey Analysis Workshop
> (SPSS)<http://surveyresearch.weebly.com/1-survey-analysis-workshop-spss.html>
>
> Research: Subjective Social Indicators (Quality of
> Life)<http://surveyresearch.weebly.com/3-subjective-social-indicators-quality-of-life.html>
>
>
>
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-----
--
Bruce Weaver
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