Quantitative research methodologies correlational research

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Quantitative research methodologies correlational research

  1. 1. CORRELATIONAL RESEARCH QUANTITATIVE RESEARCH METHODOLOGIES Presented By: Manoj Patel Asst. Professor JHUNJHUNWALA BUSINESS SCHOOL
  2. 2. THE NATURE OF CORRELATIONAL RESEARCH • Sometimes called associational research • It investigates the possibility of relationships between only two variables • Also sometimes referred to as a form of descriptive research • Describes the degree to which two or more quantitative variables are related
  3. 3. PURPOSES OF CORRELATIONAL RESEARCH • Two basic purposes 1. Help explain important human behaviors (Explanatory Studies) 2. Predict likely outcomes (Prediction Studies)
  4. 4. EXPLANOTARY STUDIES • Researchers often investigate a number of variables they believe are related to a more complex variable. • Unrelated variables dropped from further consideration • Most researchers most probably trying to gain some ideas about cause and effect • However it does not establish cause and effect
  5. 5. PREDICTION STUDIES • Predict a score on one variable if a score on the other variable is known • Determine the predictive validity of measuring instruments • Predictor Variable; variable that is used to make the prediction • Criterion Variable; variable about which the prediction is made
  6. 6. Using Scatter plots to Predict a Score • We can use the scatter plots to find a correlation between the variables • correlational research.pptx
  7. 7. A simple Prediction Equation • Used to express the regression line • We gain confidence in using the prediction equation to make future predictions if there is a close similarity between two results 'Y
  8. 8. MORE COMPLEX CORRELATIONAL TECHNIQUES 1. Multiple Regressions; technique that enables researchers to determine a correlation between a criterion variable • The best combination of two or more predictor variables
  9. 9. 2. The Coefficient of Multiple Correlation • Symbolized by R; indicates the strength of the correlation between the combination of the predictor variables and the criterion variables. • multiple correlation.jpg • The higher R is, the more reliable a prediction will be
  10. 10. 3. The Coefficient of Determination • The square of the correlation between a predictor and a criterion variable • Indicates the percentage of the variability among the criterion scores that can be attributed to differences in the scores on the predictor variable
  11. 11. 4. Discriminant Function Analysis • Technique used when the technique of multiple regression cannot be used when the criterion variable is categorical 5. Factor Analysis • Technique that allows a researcher to determine if many variables can be described by a few factors.
  12. 12. BASIC STEPS IN CORRELATIONAL RESEARCH 1. Problem Selection • Three major types of problems; a. is variable X related to variable Y? b. how well does variable P predict variable C? c. What are the relationship among a large number of variables and what predictions can be made?
  13. 13. 2. Sample • Should be selected carefully, and if possible, randomly. • Not less than 30. 3. Instruments • Most correlational studies involve the administration of some types of instruments (tests, questionnaire, and so on).
  14. 14. 4. Design and Procedures • Design used quite straightforward. 5. Data Collection • Data on both variables will usually be collected in a short time. • Instruments used are administered in a single session or two sessions
  15. 15. THREATS TO INTERNAL VALIDITY • There are some threats identified in conducting correlational research 1. Subject Characteristics • Individuals or groups have two or more characteristics; might be a cause of variation in the other two variables.
  16. 16. 2. Location • Location is different for different subject • One location may be more comfortable compared to others 3. Instrumentation • Instrument decay; care must be taken to ensure the observers don’t become tired, bored or inattentive • Data collector characteristics; different gender, age or ethnicity may affect specific response
  17. 17. 4. Testing • Experience of responding to the first instrument may influence subject responses to the second instrument 5. Mortality • Loss of subjects may make a relationship more (or less) likely in the remaining data
  18. 18. EVALUATING THREATS TO INTERNAL VALIDITY • Follows a procedure similar to the experimental research. 1. Subject Characteristics • Four of many possible characteristics a. Severity of disability b. Socioeconomic level of parents c. Physical strength and coordination d. Physical appearance
  19. 19. 2. Mortality • Loss of subjects can be expected to reduce magnitude of correlation 3. Location • Threats could be controlled by independently assessing the job-site environments.
  20. 20. 4. Instrumentation • Instrument decay; observations should scheduled • Data collector characteristics; interaction of data collectors and supervisors is a necessary parts • Data collector bias; observers should have no knowledge of job ratings

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