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- 1. CORRELATIONAL RESEARCH: LANGUAGE LEARNING / TEACHING ATTITUDES GROUP 3 PREMALATHA P. CHELLADORAI PGP110028 NORAZLINA BINTI RAFI AHMAD PGP110020 SITI AISHAH BINTI SAHAIRI PGP110013Research In Second Language Acquisition(PBGS 6113)
- 2. What isCORRELATIONAL RESEARCH
- 3. DEFINITION OFCORRELATIONAL RESEARCHAccording To J.D. Brown & T.S Rodgers (2009), Second Language Research, How things fit together, how things are related
- 4. For example :Are big kids really fast runners?- The relationship between students height and their speed.
- 5. ANALYZING CORRELATIONAL DATA Collect data Compile data Calculate a statistic called CORRELATION COEFFICIENT
- 6. CORRELATION COEFFICIENTThe degree of relationship between twosets of numbers represented as the ratioof go – togetherness to total scorevariation
- 7. CORRELATION COEFFICIENTCORRELATION SignMagnitude - Indicates the direction of the- Tells the degree of relationship relationship between (positive/negative) the two sets of numbers (0.00 – 1.00)
- 8. CORRELATION COEFFICIENTSTUDENT SET A SET B Marie 9 8 Example 1: Jose 8 7Jeanne 7 6 The number of words spelledHachiko 6 5 correctly in a spelling test of ten items (TEST 1)Raphael 5 4 Yuka 4 3 Correlation : 1.00Hossein 3 2 - Magnitude : Perfect relationshipTamara 2 1 - Sign : Positive Hans 1 0
- 9. CORRELATION COEFFICIENTSTUDENT SET A SET B Marie 9 1 Example 2 : Jose 8 2Jeanne 7 3 The number of words spelledHachiko 6 4 correctly in a spelling test of ten items (TEST 2)Raphael 5 5 Yuka 4 6 Correlation : -1.00Hossein 3 7 - Magnitude : Perfect relationshipTamara 2 8 - Sign : Negative Hans 1 9
- 10. STEPS IN CORRELATIONAL RESEARCHSTEP 1Figure out what kind of scales you aredealing withSTEP 2Deciding on the appropriate correlationcoefficient to calculateSTEP 3Calculate the appropriate correlationcoefficient
- 11. STEP 1Figure out the types of scaleIn a language studies, there are THREEkinds of scales1) Rank – ordered scales2) Continuous scales3) Categorical
- 12. RANKED ORDER SCALESScales that arrange or sort the valuesaccording to orderFor example : 1st, 2nd, 3rd
- 13. CONTINUOUS SCALEInstead of ranking order, we usenumber values to organize dataFor example : 100, 90, 80, 70
- 14. CATEGORICAL SCALEScales that organize the data intocategory / groupsFor example : MARKS CATEGORY 90 – 100 Excellent 80 – 89 Very good 70 – 79 Good
- 15. THE COMBINATION OF THE THREE SCALES NAME MARKS RANKS GROUPSAmber 100 1 HighBernard 94 2 HighCassey 89 3 High Dania 86 4 Middle Eric 78 5 Middle Fay 76 6 MiddleGeorgia 64 7 LowHashim 61 8 Low Indra 55 9 Low
- 16. STEP 2 & 3Decide and calculate correlation coefficient There are THREE types of correlational coefficient 1) Spearman (rho, or ρ) - Analyzing 2 sets of numbers if they are both rank ordered scales
- 17. 2) Phi (Φ) - Is appropriate if the 2 sets of are numbers are categorical scales3) Pearson / Product – moment (r) correlation coefficient - Is appropriate if the 2 sets of numbers are continuous scales
- 18. TYPES OF CORRELATION COEFFICIENT AND SCALESTYPE OF CORRELATION WHAT SCALES CAN IT COEFFICIENT ANALYZE?Spearman (rho, or ρ) Two sets of rank – ordered dataPhi (Φ) Two sets of categorical dataPearson / product – Two sets of continuousmoment (r) data
- 19. SPEARMAN (rho, or ρ)It is conceptually the easiest tounderstandIt is designed to estimate the degree ofrelationship between two sets of rank-order dataAlso simply called as SPEARMAN RHO
- 20. SPEARMAN (rho, or ρ)The equation : 2 6 D 1 2 N N 1where ρ = Spearman rho correlation D = the differences between the ranks N = the number of cases
- 21. SPEARMAN (rho, or ρ)For example :Two teachers’ rankings of overall courseperformance for one group of 11students
- 22. SPEARMAN (rho, or ρ)STUDENT TEACHER A TEACHER B DIFFERENCE D² Maria 1 4 -3 9Juanita 2 3 -1 1 Toshi 3 1 2 4 Raul 4 2 2 4 Anna 5 5 0 0Jaime 6 6 0 0 Hans 7 8 -1 1Hachiko 8 9 -1 1 Tanya 9 7 2 4Jacques 10 11 -1 1 Serge 11 10 1 1 TOTAL : 0 TOTAL : 26
- 23. SPEARMAN 6 D 2 1(rho, or ρ) 2 NN 1 D² = 26 / N = 11 6 26 ρ = 1 11(121 1) 156 The result based on = 1 the ranks is high 1320 The rankings of both teachers are highly related = 1 .1181818 = .8818182 .88
- 24. PHI COEFFICENT (φ)It is designed to estimate the degree ofrelationship between two categoricalvariables with two possible possibilitieseach.
- 25. PHI COEFFICENT (φ)The equation : BC AD A B C D A C B D
- 26. PHI COEFFICENT (φ)To calculate, arrange your data in atwo - by - two table like this. A B C D
- 27. PHI COEFFICENT (φ)For example :I like to share things with other people [Y/N](Respondent : several classes of MA levelESL teachers in training at the University ofHawaii)
- 28. PHI COEFFICENT (φ)Convert your data into this tableI like to share things with other people [Y/N] MALE FEMALE A B 2 14 YES C D 11 1 NO
- 29. PHI COEFFICENT BC AD(φ) A B C D A C B D A = 2 / B = 14 / C = 11 / D = 1 (14 11) (2 11)φ = (2 14)(11 1)(2 11)(14 1) 154 2 Relationship in this group of = graduate students between (16)(12)(13)(15) male and female, answering yes or no to the question 152 about sharing is not highly = related. 37440 152 = 193.49 = .7855703 .79
- 30. PEARSON / PRODUCT – MOMENT (r)Is designed to estimate the degree ofrelationship between two sets ofcontinuous scale data.
- 31. PEARSON / PRODUCT – MOMENT (r)The equation : X Mx Y My r NS x S y
- 32. PEARSON / PRODUCT –MOMENT (r) r X Mx Y My NS x S y where : X = the values for the X variable Y = the values for the Y variable Mx = the mean for the X variable My = the mean for the Y variable Sx = the standard deviation for the X variable Sy = the standard deviation for the Y variable N = N the number of paired values for the X and Y variables (often the number of participants)
- 33. PEARSON / PRODUCT – MOMENT (r)For example :One set of questionnaire (Willing, 1988 :116) - This questionnaire results in two different ways: a) Mean answers on each four-point Likert scale item b) Percentage (%) as best for each item
- 34. DoingSecondLanguageResearch,Page 174
- 35. PEARSON / PRODUCT –MOMENT (r) X Mx Y My r NS x S y 149 .76 r = 30 (. 37 )(14 .53 ) 149.76 Shows similarity / high – = 161.283 related / more – less – equivalent = .9285541 .93
- 36. INTERPRETINGCORRELATIONAL RESEARCH Both sets of numbers must be the same. The pair of numbers within a data set must be independent.
- 37. EXAMPLE OFCORRELATIONAL RESEARCH TITLE Motivation and Attitude in Learning English among UiTM Students in the Northern Region of Malaysia. RESEARCHERS Bidin, Samsiah and Jusoff, Kamaruzaman and Abdul Aziz, Nurazila and Mohamad Salleh, Musdiana and Tajudin, Taniza (2009).
- 38. PUBLICATIONEnglish Language Teaching, 2 (2). pp.16-20. ISSN 1916-4742.PURPOSE OF STUDYDescribe the relationship between thestudents’ motivation and attitude; andtheir English Language performance.
- 39. SUBJECTPart two students from three UiTMcampuses in the Northern region.INSTRUMENTATIONQuestionnaire (adopted and adaptedfrom Gardner and Lambert - 1972).
- 40. METHOD- A correlational research design was used : SPEARMAN RHO RANK-ORDER CORRELATION COEFFICIENT- It was used to answer these two questions (QUESTION 1 & QUESTION 2).
- 41. QUESTION 1 To find out whether there exists any correlation between motivation and English language performance. It is found that there is no significant differencebetween motivation and English language performance.
- 42. QUESTION 2 To find out whether there exists a significant correlation between the attitude in learning English and English language performance It is found that the respondents who obtained an A(high achievers) have better attitude in learning English compared to low achievers.
- 43. THE END…

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