Higher Education Academy Psychology Learning and Teaching Conference, Bath, July 2008
Download
1 / 18

Importance of Statistics in Psychology - PowerPoint PPT Presentation


  • 523 Views
  • Updated On :

Higher Education Academy Psychology Learning and Teaching Conference, Bath, July 2008 Development of an interactive visual workspace to aid the intuitive understanding of ANOVA (Analysis of Variance) Richard Stephens & Sol Nte School of Psychology. Importance of Statistics in Psychology.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Importance of Statistics in Psychology' - Roberta


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
Slide1 l.jpg

Higher Education Academy Psychology Learning and Teaching Conference, Bath, July 2008Development of an interactive visual workspace to aid the intuitive understanding of ANOVA (Analysis of Variance)Richard Stephens & Sol NteSchool of Psychology


Importance of statistics in psychology l.jpg
Importance of Statistics in Psychology Conference, Bath, July 2008

  • People are the subjects in Psychology research

  • People vary e.g. cleverness, speediness, attention to detail, etc.

  • Statistics offer a counter-argument to: “What if all the brainy people were in the experimental group?”


Psych students statistics l.jpg
Psych students + statistics = Conference, Bath, July 2008

  • “Surface learning" (Marton & Saljo, 1984) v.“Deep learning" (e.g. Richardson, 2005)

  • How to encourage psych students to process statistics at a deep(er) level?


A specific example l.jpg
A specific example Conference, Bath, July 2008

  • ANOVA (Analysis of Variance)

  • Used where a study includes groups and we want to know whether group means are different

  • I think you need to grasp 6 concepts to understand ANOVA properly (to integrate info and process it more deeply)…

  • Histograms/ distributions

  • Variance

  • The F ratio

  • “Significance”

  • Type I error

  • Underlying assumptions

  • Q: How can these be taught in a more integrated fashion?


Our idea l.jpg
Our idea Conference, Bath, July 2008

  • A software applet and tutorial package presenting a medium rendering ANOVA and its assumptions visually and dynamically

  • Aimed to demonstrate key concepts, so facilitating in students a more intuitive grasp of ANOVA and its assumptions

  • Mills (2002; Journal of Statistics Education) recommends computer simulation methods for teaching statistics (but notes absence of empirical evaluation studies)

  • Existing web-based ANOVA demonstration applets (see links on final slide) criticised:

    • lack an intuitive interface

    • omit assumptions of ANOVA

    • not evaluated

  • We tried to rectify these problems and we included an empirical evaluation study


Literature review l.jpg
Literature review Conference, Bath, July 2008

  • We could find no formal evaluations of comparable software applets

  • But there are reports of positive student evaluations of other demonstrative teaching methods applied to ANOVA...

  • Software that graphically presented ANOVA designs (Rasmussen, 1996);

  • A demonstration of ANOVA sources of variance using cardboard boxes of different weights (Sciutto, 2000)

  • A classroom exercise demonstrating the effects of violations of ANOVA assumptions (Refinetti, 1996)

  • Conclusion: there is pedagogic merit in developing and empirically evaluating a novel software applet for teaching ANOVA and its assumptions


Slide7 l.jpg

  • Two normal distributions Conference, Bath, July 2008

  • Right is moveable, morphable and can be viewed as a curve or histogram

  • The distributions are generated algorithmically in real-time (i.e. NOT animations)

  • Generated using the highly skewable log-normal distribution, given by the formula

  • Controls vary the location (), shape () and scale (m), adjusting the core properties of the blue distribution

  • Added an algorithm allowing adjustment of N to explore sample size and power


Anova demo l.jpg
ANOVA Demo Conference, Bath, July 2008

  • Learning outcomes:

    • How ANOVA works: F = between grps / within grps variance

    • Homogeneity of variance assumption

    • Normality assumption & kurtosis

    • Normality assumption & skewness

    • Relationship between sample size and statistical power

  • Disk

  • www


Evaluation l.jpg
Evaluation Conference, Bath, July 2008

  • A classroom comparison study in Y1 Psyc research methods module

  • Aimed to assess, empirically, the dynamic interactive aspect, so

    • 59 experimental group participants used the software online (moveable)

    • 52 control participants studied paper copies (static)

  • 10-item MCQ class test applied twice: immediately and after a 1 hour delay

  • An 8-item qualitative feedback questionnaire inbetween


Evaluation10 l.jpg
Evaluation Conference, Bath, July 2008

Results

  • P’pants answered 6.54 items on the class test correctly (standard deviation 2.3), but..

  • No effect of group, F(1,106) < 1, no effect of delay, F(1,106) = 1.238, p = 0.268, and no group x delay interaction, F(1,106) < 1

  • The control group responded slightly more favourably on the qualitative items (chi-sq p>0.05)

  • 89 subsequent Blackboard (VLE)visits, average visit time 2.5 minutes – the longest average visit time of all course items

  • We did see improvements on the module examination…


Evaluation11 l.jpg
Evaluation Conference, Bath, July 2008


Conclusion l.jpg
Conclusion Conference, Bath, July 2008

  • Likely to be an improvement (exam perf), but could not pinpoint the interactive aspect as necessary

  • Over-stringent control condition???

  • Useful class exercise + in lectures

  • Significant amount of assumed knowledge, e.g. the normal distribution and its depiction in a histogram???


Math s anxiety l.jpg
Math(s) anxiety Conference, Bath, July 2008

  • “A general fear of contact with mathematics, including classes, homework and tests” (Hembree, 1990)

  • Predicted maths performance in a mixed sample of adults (Miller & Bichsel, 2004)

  • In the late 80s classroom interventions (e.g. using microcomputers) were not effective at reducing maths anxiety (Hembree, 1990)

  • But…


3 principles l.jpg
3 principles Conference, Bath, July 2008

  • Appearance should be the antithesis of anxiety; cuteness (Marcus, 2002)

  • NOT abstract; metaphor of data drifting down from the real world to the statistics world

  • Incorporated a game mode to address the need to achieve “deep learning”by allowing students to “learn by doing”


Demonstration of the normal distribution histograms l.jpg
Demonstration of the normal distribution/ histograms Conference, Bath, July 2008

  • Learning outcomes – to explain:

    • What the normal distribution is;

    • How it is depicted with a histogram;

    • How to produce a histogram;

    • Properties of the normal distribution that make it useful in statistics (e.g. 68% of values fall within 1 standard deviation of the mean; the mean is at the centre, etc.)

Disk

www


Using computer graphics to illustrate key concepts underlying basic statistics l.jpg
Using computer graphics to illustrate key concepts underlying basic statistics

  • People seem to think it’s a good idea even if there’s not much empirical support

  • Attractive to funders

  • Don’t be too stringent in your choice of control when doing a first evaluation

  • Can be creative – good to be so

  • Need a good programmer!


Slide18 l.jpg

References underlying basic statistics

  • Dancey, C.P. & Reidy, J. (2002). Statistics without maths for psychology. 2nd Edition. Harlow: Prentice Hall.

  • Hembree, R. (1990). The nature, effects, and relief of mathematics anxiety. Journal For Research In Mathematics Education, 21, 33-46.

  • Marcus, A. (2002). The cult of cute: the challenge of user experience. Interactions 9(6), 29 - 34. 

  • Marton, F. & Saljo, R. (1984). Approaches to Learning. In Marton, F., Hounsell, D. and Entwistle, N.J. (eds), The Experience of Learning: Implications for Teaching and Studying in Higher Education, 2nd ed, Edinburgh: Scottish Academic Press.

  • Miller, H. & Bichsel, J. (2004). Anxiety, working memory, gender, and math performance. Personality and Individual Differences, 37, 591-606.

  • Mills, J.D. (2002). Using computer simulation methods to teach statistics: A review of the literature. Journal of Statistics Education [Online], 10(1).(http://www.amstat.org/publications/jse/v10n1/mills.html)

  • Rasmussen, J.L. (1996). ANOVA MultiMedia: A program for teaching ANOVA designs. Teaching of Psychology, 23, 55-56.

  • Refinetti, R. (1996). Demonstrating the consequences of violations of assumptions in between-subjects analysis of variance. Teaching of Psychology, 23, 51-54.

  • Richardson, J.T.E. (2005). Students’ approaches to learning and teachers’ approaches to teaching in higher education. Educational Psychology, 25, 673-680.

  • Sciutto, M.J. (2000).Demonstration of factors affecting the F ratio. Teaching of Psychology, 27, 52-53.

    Link to our ANOVA demo

  • http://www.psychology.heacademy.ac.uk/miniprojects/anova/anova1.html

    Link to our Normal Distribution demo

  • http://www.keele.ac.uk/depts/ps/RSStat/index.html

    Links to other online statistics demos

  • http://www.ruf.rice.edu/~lane/stat_sim/one_way/index.html

  • http://www.psych.utah.edu/stat/introstats/anovaflash.html

  • http://www.csustan.edu/ppa/llg/stat_demos.htm


ad