LEARNING TO USE SPSS: SOME COMMON PROBLEMS

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LEARNING TO USE SPSS SOME COMMON PROBLEMS

• Paul Kinnear
• University of Aberdeen

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Introduction

• Colin Gray and I have been teaching our students
  statistics and the use of statistical packages
  for many years.
• We have written several books on SPSS, published
  by Psychology Press.
• The latest is SPSS14 Made Simple (2006). We are
  currently updating it for the recently released
  SPSS15.
• Page numbers in later images relate to the SPSS14
  book.

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Our associates

• Our teaching has benefited hugely from the advice
  and support of John Lemon, our Universitys
  Senior Computing Adviser, and Caroline Green,
  Senior Teaching Fellow in the School of
  Psychology.
• Today, I would like to share with you some of our
  experiences in teaching SPSS to our students.

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Purpose of this talk

• The advent of Windows-based versions of SPSS has
  been a boon to students and teachers alike.
• Certain aspects of SPSS, however, present
  problems for students.
• My purpose today is to identify some of the most
  common difficulties.

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Example of a problem

• Many difficulties arise from users not taking
  time to make a list of what their dependent and
  independent variables are, and what they are
  trying to show in terms of their experimental
  hypothesis (or hypotheses).

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Summary of problem areas

• Not identifying the variables and their types.
• Not understanding the differences between within
  subjects and between subjects designs.
• Not undertaking exploratory data analysis (EDA)
  before launching into statistical analyses.
• Not knowing how to select the appropriate
  analysis.

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Summary of problem areas (continued)

• Not knowing how to edit out unnecessary bits of
  output.
• Misunderstanding the meaning of p-values recorded
  as .0000
• Not knowing how to process frequency data in
  contingency tables.

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Where problems can arise

• Data entry.
• Choosing the right statistics.
• Interpretation of the output.
• These areas are certainly not independent
  problems in one area often arise from problems in
  another.

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Data entry

• Often, what seems to be a problem with data
  entry arises from a lack of clarity about the
  purpose and design of the investigation and
  about the variables involved.

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Purpose and design

• The user must be able to answer the following
  questions
• What was the hypothesis?
• How was the study designed?
• What pattern in the results would confirm the
  hypothesis?

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The variables Some key questions for learners

• Was your investigation an experiment or a
  correlational study?
• If it was an experiment, what were the
  independent and dependent variables?
• Was the experiment a between subjects or a within
  subjects (or possibly a mixed) design?

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Key terms

• To use SPSS, the user must be fully conversant
  with some standard terms in experimental design.
  
• The user needs to know these terms in order to
  complete SPSSs dialog boxes.

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Some common terms

• Dependent (DV) and independent (IV) variables.
• Between subjects versus within subjects (repeated
  measures).
• Independent versus related samples.
• Nominal, ordinal and interval (or scale) data.
• Factors and levels.
• Covariates.

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Between subjects or within subjects?

• Users are often unclear about this distinction.
• A treatment factor is between subjects if there
  is no basis for pairing the data obtained under
  different conditions.
• For example, if the same participant is tested
  twice under different conditions, a pair of
  scores will result.
• Such paired data are likely to be correlated.

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Misunderstood concepts univariate, bivariate and
multivariate statistics

• Univariate statistics relate to analyses with
  just one DV (e.g. driving performance).
• Bivariate statistics relate to analyses such as
  correlation where two variables are measured but
  neither can be considered as an IV (e.g. scores
  in two tests).
• Multivariate statistics relate to analyses with
  more than one DV (e.g. reaction time number of
  errors).

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We discourage the use of MANOVA

• MANOVA is multivariate analysis of variance i.e.
  ANOVA applied to more than one DV.
• In line with Tabachnick Fidells recommendation
  (Using Multivariate Statistics 4th Edition, p.
  323), we discourage students from using MANOVA
  and encourage them instead to use several
  univariate ANOVAs.
• The difficulty is that a MANOVA significant
  result does not tell you which DVs are sensitive
  to the IV(s).

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SPSS data sets

• An SPSS data set in the Data Editor is not in all
  respects like the sort of table one sees in a
  book or a journal article.
• In a published table, the columns may represent
  data gathered on different groups of people.
• In an SPSS data set, each row contains data on
  only one participant.

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Between subjects example (p. 23)

• An experiment has been carried out to investigate
  the effects of a drug upon performance.
• The design is between subjects a Placebo and
  Drug group are compared in their performance on a
  test of skill.

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A between subjects experiment (p.26)
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Grouping variables

• Each participants group membership is indicated
  by a code number 1 Placebo 2 Drug.
• Together, the code numbers make up what is known
  as a grouping variable.
• Now, each row in the data set contains
  information on only one participant.

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Between subjects data in SPSS format (p. 27)
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Variable View of Data Editor for between subjects
design (p. 31)
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Data View of Data Editor for between subjects
design (p. 35)
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Within subjects example (p. 302)

• Participants took part in shooting at three
  shapes of target (circle square triangle) and
  their accuracy scores were recorded.
• Since each participant fired at all three target
  shapes, the design is within subjects. (The
  order of using the media counterbalanced across
  participants).

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Variable View of Data Editor for within subjects
design (p. 302)
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Data View of Data Editor for within subjects
design
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Not undertaking exploratory data analysis (EDA)
before launching into statistical analyses.

• Learners are often so excited at having
  completed a data file that they do not
• Bother to check whether there have been any
  transcription errors.
• Whether there are outliers needing attention.

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Exploring your data

• Statistics such as the mean, SD and r are very
  useful for summarising some feature of the data.
  But they can all be misleading.
• It is essential to use graphical methods to
  confirm the summaries provided by the statistics.

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Get to know your data first

• Learners must be warned against pressing ahead
  with formal tests before they have first explored
  their data thoroughly.

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Panelled bar charts (p.144)
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Pie Chart
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What to look for in a scatterplot

• The cloud of points should either be elliptical
  or circular.
• An ellipse indicates a linear relationship a
  circular cloud indicates independence.

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Example of an outliers (p. 185)
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More problems

• Failure to label variables and values carefully.
  This can make the output very difficult to read.
• Inability to analyse nominal data.

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Choosing the right statistic

• We use statistical techniques for TWO purposes.
• Summarising and exploring our data.
• Confirming the patterns we find in our data.

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Five common research situations (p. 13)
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Not knowing how to select the appropriate analysis

• Learners find difficulty finding the ANOVA
  analysis because SPSS does not have an item in
  the Analyze drop-down menu called ANOVA.
• They also have difficulty finding the chi-square
  test of association and other statistics for
  analysing contingency tables because they do not
  remember that it is in the Crosstabs item of the
  drop-down menu Descriptive Statistics.

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Yet more problems

• Failure to remove unnecessary items from the SPSS
  output.
• Confusion by expressions such as .0000 in columns
  of p-values.

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Not knowing how to edit out unnecessary bits of
output

• Learners are prone to print all SPSSs output
  without taking the trouble to edit out
  unnecessary bits.
• Editing can include cutting out whole items
  (tables, graphics) or just bits within tables.
• The output for within subjects ANOVA can be
  particularly obscure without appropriate editing.

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Need to edit output too

• Learners are also wary of trying to edit output.
• The result is that they copy large tables with
  unnecessary columns and unnecessary decimal
  places into their documents.

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More extensive outputs

• Lets have a look at some ANOVA outputs and see
  how we can edit them.

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Complete within subjects output
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Within subjects Mauchly output (p. 313)
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Within subjects effects output (p. 314)
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ANOVA summary table after editing
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Edited Bonferroni output
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Profile plot before editing
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Profile plot after editing the differences are
less impressive
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Results of editing output

• Hopefully you will agree that suitable editing of
  the SPSS output has made the material much more
  suitable for inserting into laboratory or
  research reports.
• Learners often think that they are obliged to
  paste everything SPSS outputs into their reports.

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Misunderstanding the meaning of p-values recorded
as .0000

• Learners seem to have difficulty interpreting
  p-values.
• They often do not realise that .0600 is larger
  than .0500 and hence that the statistical test is
  not significant (assuming ? 0.05).
• They are bewildered by decimals with just zeros
  after the point e.g. .0000

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Not knowing how to process frequency data in
contingency tables

• Two problems here
• How to enter the data.
• How to find chi-square and other contingency
  table statistics.
• Learners need reminding that they need two coding
  variables as well as a count variable.

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Contingency table example (p. 380)
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Contingency Table for SPSS

• The table has to be re-arranged into two coding
  variables and a count variable as in the next
  slide.

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Two coding variables and a count variable (p. 383)
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Remember the Weight Cases procedure

• When data are aggregated as in the previous
  table, it is necessary to tell SPSS that the
  numbers in the Count variable are frequencies.
• This is done by means of the Weight Cases item in
  the Data drop-down menu.
• Weight Cases is not needed when each row of the
  data set represents a participant.

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The Weight Cases procedure for getting SPSS to
treat the data in Count as a frequency
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Finding the Chi-square measure of association in
Crosstabs (p. 383)
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The Crosstabs dialog box (p. 384)
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Selecting Chi-square and expected frequencies
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Contingency table with expected counts (p. 386)
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Chi-square output (continued)
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Conclusions

• I have tried to identify the most common
  difficulties we have found for people learning to
  use SPSS.
• Some of these are due to basic statistical
  inexperience but others relate to the way in
  which SPSS operates.
• Learners find difficulties in working out how to
  re-arrange their data to fit in with SPSSs Data
  Editor requirement that rows generally represent
  single participants (or cases) and columns
  represent variables.

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Conclusions (continued)

• Learners need to be clear about what are their
  DVs and what are their IVs. They also need to be
  clear about whether the experiment has
  independent or related samples of scores (i.e.
  between subjects or within subjects designs).
• Prudent editing of output is desirable.
• They need to understand how to re-arrange data
  for the Chi-Square test of association.

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The End

• Many thanks for your attention.