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Kanchana Prapphal, Chulalongkorn University Statistics for Language Teachers Kanchana prapphal May 23, 2002 Kasetsart University.

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Presentation on theme: "Kanchana Prapphal, Chulalongkorn University Statistics for Language Teachers Kanchana prapphal May 23, 2002 Kasetsart University."— Presentation transcript:

1 Kanchana Prapphal, Chulalongkorn University Statistics for Language Teachers Kanchana prapphal May 23, 2002 Kasetsart University

2 Kanchana Prapphal, Chulalongkorn University Contents Descriptive Statistics (Frequency Distributions, Measures of Central Tendency, Measures of Variability) Correlation and Regression Inferential Statistics (t-test, F-test) Non-parametric Statistical Tests (Chi- square Test, Spearman Rank Order Correlation)

3 Kanchana Prapphal, Chulalongkorn University Frequency Distributions Class interval Graphic Presentation of Data (Bar graph, Histogram, Frequency Polygon, Line graph) Percentage

4 Kanchana Prapphal, Chulalongkorn University Measures of Central Tendency Mode Median Arithmetic mean (X = sum X/N)

5 Kanchana Prapphal, Chulalongkorn University Measures of Variability Range Variance Standard deviation The normal distribution

6 Kanchana Prapphal, Chulalongkorn University Correlation Relationship between 2 variables Interpretation: +.95, +.93, +.87, +.85 = high positive correlation +.23, +.20, +.18, +.17 = low positive correlation +.02, +.01,.00, -.03 = no systematic correlation -.21, -.22, -.17, -.19 = low negative correlation -.92, -.89, -.90, -.93 = high negative correlation

7 Kanchana Prapphal, Chulalongkorn University Pearson Correlation Matrix ________________________________ ___________ Tests 1 2 3 ________________________________ ___________ 1. Vocab 1.000.38.66 2. Grammar 1.00.60 3. Sound Perception 1.00 ________________________________ ___________

8 Kanchana Prapphal, Chulalongkorn University Regression (Bivariate) Prediction of the relationship between 2 variables y = a + bx y = the predicted college GPA a = constant or the point at which the regression line intersects the y axis b = the slope of the regression line,I.e. the amount of y is increasing for each increase of one unit in x x = the x value used to predict y

9 Kanchana Prapphal, Chulalongkorn University Regression (Multiple Variables) Multiple regression prediction equation y = a + bx1 + bx2 + bx3 y = the predicted college GPA x1 = the high school GPA x2 = the score on the entrance exam x3 = the absence rate in high school y = 2.80 = He would be predicted to obtain a B- average in his first quarter of college work.

10 Kanchana Prapphal, Chulalongkorn University Inferential Statistics T-test (independent samples, correlated samples) F-test One-way analysis of variance (ANOVA) Factorial analysis of variance -two-way ANOVA -three-way ANOVA -factorial design

11 Kanchana Prapphal, Chulalongkorn University T-test (for one factor with 2 groups) A. Independent samples e.g. An experiment between a control group and an experimental group B. Dependent or correlated samples e.g. The difference between the pre- test and the post-test

12 Kanchana Prapphal, Chulalongkorn University F-test One-way ANOVA (with more than two groups) The ANOVA Summary Table Source df SS MS F Test formats 2 16 8 4* Within groups 15 30 2 Total 17 46 *p <.05 The three groups differed in terms of the test form they received.

13 Kanchana Prapphal, Chulalongkorn University Two-Way ANOVA 3 Fs 2 main effects (two factors or two independent variables) 1 interaction (the effect the dependent variable of the two independent variables operating together) Example: an experiment of two methods of teaching English

14 Kanchana Prapphal, Chulalongkorn University Three-Way ANOVA 7Fs 3 main effects 3 first-order interactions (AxB, AxC, BxC) 1 second-order interaction (AxBxC) Example: an experiment on three methods of teaching English

15 Kanchana Prapphal, Chulalongkorn University Factorial Design More than one factor Two main effects and one interaction Example: Factors = Time limit (Yes, No) Item order (syllabus, backward, random) 2*3 ANOVA

16 Kanchana Prapphal, Chulalongkorn University Non-parametric Statistical Tests Chi-square Test frequency, category, nominal data Spearman Rank Order Correlation rank, N < 30, ordinal data

17 Kanchana Prapphal, Chulalongkorn University Practice tests mean % sd items structure 31.57 (42.09) 15.05 75 listening 19.33 (38.66) 8.43 50 CU-TEP 44.54 (44.54) 16.36 100 Which is the easiest test? Which is the most difficult test? What do you learn from the standard deviations of the 3 tests?

18 Kanchana Prapphal, Chulalongkorn University Practice (continued) Interpret the following correlation coefficients. Structure Listening CU-TEP Spelling Structure.723**.560 * -.300* Listening.840 ** -.010 Spelling *p<.05 **p<.01

19 Kanchana Prapphal, Chulalongkorn University Practice (continued) Read the following table. Criterion variables R Aptitude Aptitude+Affective F Reading.792.810 6.094** Listening.723.740 3.200** Writing.570.608 5.111** Speaking.578.624 6.182** **p<.01

20 Kanchana Prapphal, Chulalongkorn University Practice (continued) Source df MS F Instructional methods (A) 1 439.35 4.85* Subject matters (B) 1 67.33 Science interest levels (C) 1 1.13 A x B 1 1116.94 12.34** A x C 1 111.83 B x C 1 225.92 A x B x C 1 760.03 8.39***

21 Kanchana Prapphal, Chulalongkorn University Research Questions Is there a significant relationship between X and Y? Do A, B, and C have any effect on Y? Which method (A or B) is better for first-year Arts students? Can field trips, case studies and mini- theses predict career success of graduate students?

22 Kanchana Prapphal, Chulalongkorn University


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