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Task of Correlation Research Questions
 

Task of Correlation Research Questions

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    Task of Correlation Research Questions Task of Correlation Research Questions Presentation Transcript

    • Task of Correlation Research Questions Prepared by HATS
    • Example 1
      • Question : There is a relationship between families’ income and public examination performance of students. (In Hong Kong, or in other countries)
      • Null hypothesis : There is no relationship between families’ income and students’ performance in public examinations.
    • Data collection
      • Questionnaires can be sent either randomly or selectively to samples of students within the population, asking them about their families’ income and their results in public examinations.
      • The data collected are analyzed and the correlation coefficient (r) is computed.
    • Analysis
      • The coefficient can be from -1 (negative correlation) to +1 (positive correlation).
      • Check the critical value for the number of subjects included. If the absolute value of r exceeds the critical value, then the null hypothesis is rejected, and the alternative hypothesis is accepted.
    • Significance
      • A positive correlation means wealthy students generally perform better. Further research can be done to see what types of resources, such as private tutoring, reference books or electronic resources, enhance students’ performance more.
      • Subsequent to these findings, educators can allocate resources to help students in need, such as hiring teaching assistants, stocking the libraries with more books and improving the IT infrastructures of schools.
    • Significance
      • If a negative correlation is found, further research can be done to see why such a correlation exists.
    • Example 2
      • Question : There is a relationship between Form 1 students’ knowledge of the English phonetic symbols and their motivation in learning English.
      • Null hypothesis : There is no relationship between Form 1 students’ knowledge of the English phonetic symbols and their motivation in learning English.
    • Data collection
      • Tests are given to students to see how well they know the phonetic symbols. Interviews are given to see how motivated they are in learning English. Results of the interviews are converted into scores by statistical analysis.
      • Correlation coefficient (r) is then computed based on tests scores and interviews scores.
      • Analysis : Same as in example 1
    • Significance
      • A positive correlation means that knowledge of the phonetic symbols enhances motivation in learning English. Therefore, teaching of phonetic symbols should be a mandate in the primary school curriculum.
    • Example 3
      • Research Question
        • Is university undergraduate student academic performance related with their admission type ?
    • Correlated Variable
      • Correlated Variable
          • Academic Result:
            • Cumulative Grade Point Average (CGA)
          • Intake Type:
            • JUPAS (EMI, CMI school), EAS (early admission scheme), International student, mainland student, direct entry (Higher Diploma, Associate Degree, etc)
    • Data Collection
      • Data Collection
        • University's Admissions, Registration and Records Office (ARR) – CGA results
        • Questionnaire to student about their entry qualification results
          • Direct entry: higher diploma/associate degree with Distinction, Credit or Pass
          • International student: GCE A/O Level results, IB results
          • Mainland student: university admission results
          • JUPAS: HKALE/HKCEE results
    • Testing Hypothesis
      • H 0 :
        • Null hypothesis mean that student academic result is not related with their admission type
    • Analysis
      • After data collection, correlation coefficient “r” will be computed
      • r = 0 mean that there is no direct relationship between student academic result and their admission type
      • r ≠ 0 mean that there is a direct relationship between student academic result and their admission type
    • Analysis
      • If correlation coefficient belongs to latter case, we will do further analysis and set an alternative hypothesis
        • H A : students have the best academic performance if their admission type is EAS (Early Admission Scheme)
      • Further analysis will be conducted if necessary:
        • For example: rank student academic performance based on different admission types
    • Significance
      • This research result is very useful for our planning to recruit students and we could adjust the quota for different groups of admission type
      • For example, recruit more EAS and mainland student if it is find that their performance are the best two groups. Consequently, less quota will be applied to JUPAS student
    • Example 4
      • Research Question
        • Will students who have taken computer studies or IT related subjects in HKALE/HKCEE be the best student group in the department of computer science ?
    • Correlated Variable
      • Correlated Variable
          • Academic Result:
            • Overall Cumulative Grade Point Average (CGA)
            • Core Computer science subject CGA
          • Intake student’s IT related subjects:
            • Grade of computer studies or IT related subjects in their HKCEE & HKALE
    • Data Collection
      • Data Collection
        • From University's Admissions, Registration and Records Office (ARR) – CGA results
        • Questionnaire to student whether they have taken IT related subjects (and the results) in HKCEE/HKALE
    • Testing Hypothesis
      • H A :
        • Alternative hypothesis mean that student who has taken HKALE/HKCEE computer studies or IT related subject are the best group (highest CGA) in the Department of Computer Science
    • Analysis
      • After data collection, correlation coefficient “r” will be computed
      • r > 0 and close to 1 mean that student who has taken IT related subjects in HKALE or HKCEE are the best group in the department of Computer Science
      • If coefficient “r” is not significant (e.g. it is just greater 0 but not close to 1), further analysis will be conducted.
    • Analysis
      • Testing hypothesis will be adjusted as:
        • H A : Alternative hypothesis mean that student who has taken HKALE/HKCEE computer studies or IT related subject with credit or above are the best group (highest CGA) in the Department of Computer Science
    • Significance
      • If the above hypothesis is true, we may allow the computer science department adjust the significant of IT subjects in the admission formula.
      • Develop the best group student (elite students) and allow them to take more in depth research subjects in their undergraduate studies.
    • Thank You Prepared by HATS