1 / 31

Causality and Experimental Design

2005 Robert Coe, University of Durham. 2. Causal claims. ExamplesA causes B"B is affected by A"A influences B"A improves B"B benefits from A"A results in B"A prevents B"The effect of A on B ". If you do A, B will result (provided X, Y, Z)A must be something you can choose to do (

oneida
Download Presentation

Causality and Experimental Design

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


    1. Causality and Experimental Design Doctor of Education (EdD) Analysing, Interpreting and Using Educational Research (Research Methodology)

    2. © 2005 Robert Coe, University of Durham 2 Causal claims Examples “A causes B” “B is affected by A” “A influences B” “A improves B” “B benefits from A” “A results in B” “A prevents B” “The effect of A on B …” If you do A, B will result (provided X, Y, Z) A must be something you can choose to do (‘manipulable’) B would not result if you didn’t do A (How can you ever know this?)

    3. © 2005 Robert Coe, University of Durham 3 Can we do without ‘causality’? Very hard to avoid (even in ‘interpretative’ writing) No causality ? no predictability You can have it both ways There are general laws, but we can choose Causal laws are probabilistic – better applied to groups than individuals Just because we actively interact with our environment and interpret situations does not mean our behaviour is not (partially) predictable Human behaviour is unpredictable, but also predictable – the trick is to predict the bits you can

    4. © 2005 Robert Coe, University of Durham 4 ImpaCT2: ICT and achievement What the report said: “evidence of a positive relationship between ICT use and achievement” (p2) “pupils characterised as high ICT users outperformed, on average, low ICT users in English and mathematics [at KS2]” (p11) What the press release said: “A new independent research report shows that computers can help to raise standards in schools” Harrison, C. et al (2002) ‘The impact of information and communication technologies on pupil learning and attainment’. DfES/Becta (available at www.becta.org.uk/research) Becta press release 7/11/2002 (www.becta.org.uk/press)Harrison, C. et al (2002) ‘The impact of information and communication technologies on pupil learning and attainment’. DfES/Becta (available at www.becta.org.uk/research) Becta press release 7/11/2002 (www.becta.org.uk/press)

    5. © 2005 Robert Coe, University of Durham 5 Questions What is the difference between (1) “a positive relationship” and (2) “computers can help to raise standards”? How might the first be true and not the second? (List as many possible reasons as you can think of) How could you test (2) even if you knew that (1) was true? (To do this you will need to define exactly what you think (2) means)

    6. © 2005 Robert Coe, University of Durham 6 What is ‘evidence-based’? Intervention, not description Evaluation, not common sense

More Related