Conference september 2013
This presentation is the property of its rightful owner.
Sponsored Links
1 / 35

Conference September 2013 PowerPoint PPT Presentation


  • 60 Views
  • Uploaded on
  • Presentation posted in: General

Conference September 2013. Text analysis software needs more common sense and less intelligence! John S. Lemon, University of Aberdeen. Open Day 2013. IT Services . John S. Lemon. S tudent Liaison Officer. Introduction. History – setting the scene

Download Presentation

Conference September 2013

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Conference september 2013

Conference September 2013

Text analysis software needs more common sense and less intelligence!

John S. Lemon, University of Aberdeen


Conference september 2013

Open Day 2013

IT Services

John S. Lemon

Student Liaison Officer


Conference september 2013

Introduction

  • History – setting the scene

  • Problem – move from quantitative to qualitative

  • Etc.


Introduction

Introduction

  • History – setting the scene

  • Problem – move from quantitative to qualitative

  • How - Analysis / reporting

  • Quantity – increases each year

  • Constraints

    • Reports required earlier each year

    • Very limited budget


Disclaimer

Disclaimer

  • I am not a statistician – I just have to present reports

  • When I started at university in 1975 almost all data was numeric / quantitative

  • For the purposes of this paper I emulated a naive user

  • To carry out the analysis there is no budget for:

    • Software

    • Training


History

History

  • IT Services ( formerly DISS & DIT ) runs an annual survey to:

    • Staff

    • Students

  • Purpose is to identify satisfaction with facilities and service

  • Originally on paper and scanned – almost entirely tick boxes

  • Moved to web but retained ‘tick box’ format


History1

History

  • Converted to WebHost around 2008/9

  • Still retained the mainly quantitative original


History2

History

  • SNAP had been used to create Student Course Evaluation Forms ( SCEF )

  • On paper since 1999 – two sides of Likert scales

  • Only one free text box

  • 60,000 forms scanned / year

  • In 2010 deemed to be ‘not green’ / ecological

  • Move to special web based software

  • Move to free text comments


History3

History

  • This is the 2007 paper form

  • As SCEF forms had changed approach it was decided the annual survey would do the same

  • Fewer tick boxes


History4

History

  • From 2011 some check boxes but more free text options.


Problem quantitative to qualitative

Problem - quantitative to qualitative

  • Report generation could no longer rely on

    • charts

    • tables.

  • No thought given to how to cope with free text

  • First year one person (me)

    • ‘skimmed’ the responses

    • Subdivided according to which area of service was commented on

    • Passed to section heads for action and responses


Problem quantitative to qualitative1

Problem - quantitative to qualitative

  • Second year – manual coding

  • Excel file of case number and free text comments

  • Plus extra columns for coding comments / categorisation

  • Code values were “Positive”, “Negative” or “Ambiguous”

  • Limited number of categories

  • Needed consistency so one person coded all


Problem quantitative to qualitative2

Problem - quantitative to qualitative

  • Once coded loaded into SPSS

  • Merged with original file

  • Produced tables and charts combining demographic data and coded values

  • Extremely labour intensive

  • Needed an iterative approach for accuracy

    • Categories were too broad or too detailed

    • Codes were too restrictive


Problem quantitative to qualitative3

Problem - quantitative to qualitative

  • This year attempted a new approach

  • Use software

  • New / updated versions of:

    • SNAP (11)

    • Nvivo (10)

    • STAFS - SPSS Text analysis For Surveys (4)

  • Also consider use of concordance software


Problem quantitative to qualitative4

Problem - quantitative to qualitative

  • Why choose these four products ?

    • SNAP

      • Already had so no extra cost

      • Had SNAP format files so no translating / transforming the data

    • NVivo

      • Like SNAP already had on site

      • Claims that it would meet all requirements

      • Takes data from many sources


Problem quantitative to qualitative5

Problem - quantitative to qualitative

  • Why choose these four products ?

    • SPSS Text Analysis For Surveys

      • Reads SPSS files which SNAP would create

      • Export coded categories back to SPSS

      • Being considered for site licence

    • Concordance

      • Language / literature department recommendation

      • Cheap

      • Appeared easy to use.


Conference september 2013

SNAP

  • Survey had been done in SNAP so tried first

  • New features are:

    • word ‘cloud’

    • Auto coding of text / words

  • Can combine all the free text questions into one new ‘derived’ / auto-recoded variable


Conference september 2013

SNAP

  • Not very helpful

  • Is there a difference between ‘computer’ and‘computers’ ?


Conference september 2013

SNAP

  • Not only ‘computer(s)’ presented problems

  • But all the different terms students use for the wireless network.

  • These are the more obviousspellings – ignoring themiss-spellings.

  • Not ideal as did not allow for synonyms


Snap limitations

SNAP - limitations

  • Has a ‘Stop’ list – words to exclude

  • No equivalent list to create synonyms

  • Would like to be able to do:{wifi,wi-fi,eduroam,resnet,wireless}={wireless}

  • Not just a limitation of SNAP word cloud

  • In the time available could not find how to export auto-coded variables to SPSS


Concordance

Concordance

  • Cheaper but very limited

  • No ability to easily export the results

  • Positive point is it shows need for synonyms !!


Nvivo

NVivo

  • Very powerful

  • Accepts data from a wide variety of sources:

    • Text

    • Video

    • Pictures

    • Web

    • Social media

    • Etc.


Nvivo1

NVivo

  • Data needed some pre-preparation before input

  • Some of the concepts weren’t obvious

  • Took a number of attempts to get the data into the correct format

  • It will combine terms

    • But may not be exactly what you want

    • Some of the words for ‘connect’ are quite imaginative to say the least.


Nvivo2

NVivo


Nvivo3

NVivo

  • Depending on how ‘tight’ or ‘loose’ the word associations were made could end up with entirely different results / word clouds


Nvivo4

NVivo

  • Found difficulty in:

    • Trying to get the data categorised

    • Exporting the results to merge back to SPSS

    • Alternatively try and produce tables and charts linked to demographic data within NVivo

  • Problems with all the different software were:

    • Time to learn all idiosyncrasies

    • Impatient line managers

    • Nomenclature


Stafs

STAFS

  • Appears to be very powerful and comprehensive

  • Very large manual

  • Like Nvivio has different nomenclature for the aspects of analysis

  • Will read data from SPSS files

    • Providing the text fields are less than 4000 characters in length

  • Looked the most promising to solve the problem


Stafs1

STAFS

  • Foolishly left it until last for evaluation

  • Very little time left to get to grips with yet another set of concepts

  • The deadline for the report was approaching so not a lot of time

  • Also trial version which lasted 14 days

  • Appears to have a bit more intelligence in matching words together


Stafs2

STAFS


Stafs3

STAFS

  • Has the ability to indicate “good” and “bad” phrases in green, and red

  • It also highlights the context inamber


Stafs4

STAFS

  • Problem is that the file that ‘drives’ this appears to be rather general in approach

  • To really be useful in future it needs tailoring

  • Ran out of time to really develop expertise in this

  • Potential to apply a level of ‘common sense’

  • Not easy to actually do in the time available.

  • Export back to merge with SPSS appeared OK

  • But had to abandon any further experiments


What was used finally

What was used finally

  • Time for testing / experimentation had run out

  • Only one course of action

    • By hand

    • One person – me

  • Scale of problem

    • When loaded into Word as single spaced, normal margins, 12 pt Calibri

    • Just under 500 pages

  • A ream of paper


Next year

Next year

  • Try and get a longer trial period for STAFS

  • Experiment with this years data to provide coding file

  • Use STAFS from the start


Conclusion

Conclusion

  • Don’t try and learn a lot of new software when there are deadlines from “management”

  • Word clouds don’t help much

  • A concordance really only highlights speeling idiosyncrasies

  • Care must be taken when allowing software to make choices in coding


Conclusion1

Conclusion

  • Does text analysis software have intelligence ?

  • Up to a point

  • Does it have common sense

  • Of the four tried only one does BUT

  • It needs teaching “common sense” and that takes time

  • Just like a child !!


  • Login