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Using statistics in small-scale language education research Jean Turner © Taylor & Francis 2014.

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Presentation on theme: "Using statistics in small-scale language education research Jean Turner © Taylor & Francis 2014."— Presentation transcript:

1 Using statistics in small-scale language education research Jean Turner © Taylor & Francis 2014

2  Tests and other data collection tools must measure accurately and appropriately given the nature of the construct.  Test validity is associated with the extent to which: ◦ a tool measures the intended construct ◦ the tool scores/outcomes mean what they are intended to mean ◦ the tool scores/outcomes are useful for their intended purpose(s) © Taylor & Francis 2014

3  Test validity has an impact on both internal research study validity and external research study validity. © Taylor & Francis 2014

4  There are different perspectives and techniques associated with investigations of test validity.  Historically, these different perspectives and techniques were referred to as different types of validity.  (Though they aren’t really different types.) © Taylor & Francis 2014

5  Construct validity  Content validity  Criterion-related validities ◦ Concurrent validity ◦ Predictive validity  Face validity © Taylor & Francis 2014

6  Construct validity—the extent to which the constructs measured by a test or data collection tools are clearly and appropriately defined and measured ◦ (1) Are the definitions of the constructs clear and useful? ◦ (2) Does the data collection tool really tap these skills? ◦ (3) Is there convincing evidence supporting points 1 and 2? © Taylor & Francis 2014

7  Content validity—the items or tasks measure the constructs completely and without measuring other, unrelated knowledge, skills, or abilities. ◦ Does the test measure all aspects of the construct? ◦ Is there very little measured by that test that’s unrelated to the construct? © Taylor & Francis 2014

8  There are two criterion-related approaches to investigating validity. Both involve investigating the relationship between the data collection tool in question and another tool. ◦ Concurrent validity ◦ Predictive validity © Taylor & Francis 2014

9  The new tool is administered to a group of people— who also completed a well-established tool tapping the same construct.  If the new tool taps what it’s designed to measure, the correlation between the two sets of scores will be high.  If the correlation is high, the concurrent validity is considered good—evidence that the test measures the intended construct. © Taylor & Francis 2014

10  Does a new test of Business English ability really measure that construct? ◦ Give the new test to a large number of examinees; also give the same examinees the English BULATS test (a recognized measure of Business English ability). ◦ Calculate the correlation between scores on the two tests. A high correlation serves as evidence that the new test measures Business English, because it relates well to the recognized measure of Business English.  This approach is called concurrent validity because the two tests are taken concurrently.  This approach is only as useful as the comparison measure is sound! © Taylor & Francis 2014

11  Admissions tests must have good predictive validity.  Ways to collect evidence of predictive validity: ◦ G ive the test to a number of people starting a program of study. ◦ At the end of the term, collect information on their final exam or final GPA. ◦ Find the correlation between the initial scores and the later measure of success. ◦ A high correlation is evidence of high predictive validity. © Taylor & Francis 2014

12  In the past, all students in a particular MATESOL/TFL had to take the GRE (though it wasn’t used for admission).  The correlation between GRE performance and students’ score on their comprehensive examination at the end of their studies was found to be very low.  The GRE doesn't seem to have good predictive validity for students in this program. © Taylor & Francis 2014

13  Face validity is the extent to which research study participants and other users of a data collection tool outcome believe the tool is useful and the outcomes are good indicators of the intended construct.  A data collection tool’s face validity varies according to individuals’ background and experiences, thus it’s impressionistic.  Though impressionistic, it’s important because participant performance may be affected by face validity! © Taylor & Francis 2014

14  Correlational evidence ◦ Two tests (concurrent validity) ◦ A test and a future measure (predictive validity) © Taylor & Francis 2014

15  Experimental evidence ◦ Intervention study ◦ Differential group study © Taylor & Francis 2014

16  Expert review of content, format, processes. ◦ Language testing experts ◦ Teachers ◦ Employers ◦ Learners © Taylor & Francis 2014

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