Variable Lecture


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Variable Lecture

  1. 1. An Introduction to the Use and Misuse of Research Variables “ A judicious man uses statistics, not to get knowledge, but to save himself from having ignorance foisted upon him.” --Thomas Carlyle
  2. 2. Variable Defined Any entity that can take on different values. Age is a variable because it can take different values for different people at different times.
  3. 3. Examples <ul><li>Blood Pressure </li></ul><ul><li>Sex </li></ul><ul><li>Gender </li></ul><ul><li>Age </li></ul><ul><li>Extraversion </li></ul><ul><li>Patient Satisfaction </li></ul><ul><li>Heart rate </li></ul><ul><li>Political Party </li></ul><ul><li>Time </li></ul><ul><li>Weight </li></ul><ul><li>Height </li></ul><ul><li>Anxiety </li></ul><ul><li>Pleasure </li></ul><ul><li>Fear </li></ul><ul><li>Aggression </li></ul><ul><li>Attractiveness </li></ul>
  4. 4. The Aspects of Variables
  5. 5. Variables Attributes Values Relationship
  6. 6. 1. Attributes The specific value of a variable.
  7. 7. Examples of Attributes <ul><li>SEX </li></ul><ul><ul><li>Male </li></ul></ul><ul><ul><li>Female </li></ul></ul><ul><li>Gender </li></ul><ul><ul><li>Assertive </li></ul></ul><ul><ul><li>Responsive </li></ul></ul><ul><ul><li>Androgynous </li></ul></ul><ul><li>Math makes me anxious </li></ul><ul><ul><li>Agree </li></ul></ul><ul><ul><li>Neutral </li></ul></ul><ul><ul><li>Disagree </li></ul></ul>
  8. 8. Values The numerical aspect directly associated with a specific attribute.
  9. 9. Examples of Values <ul><li>SEX </li></ul><ul><ul><li>Male = 1 </li></ul></ul><ul><ul><li>Female = 2 </li></ul></ul><ul><li>Gender </li></ul><ul><ul><li>Assertive = 81< 120 </li></ul></ul><ul><ul><li>Responsive = 0 < 40 </li></ul></ul><ul><ul><li>Androgynous = 41<80 </li></ul></ul><ul><li>Math makes me anxious </li></ul><ul><ul><li>Agree = 1 </li></ul></ul><ul><ul><li>Neutral = 2 </li></ul></ul><ul><ul><li>Disagree =3 </li></ul></ul>
  10. 10. Relationship Does a statistical relationship exist between the attributes of the variable? The correspondence between two variables.
  11. 11. Types of Relationships
  12. 12. For Example You do not become more female if your male level goes down. They are independent of each other. However, your level of assertiveness goes down as your responsiveness level goes up (negative relationship).
  13. 13. Levels of Measurement
  14. 14. Nominal Measures Attributes are only named. Also, referred to as categorical measures.
  15. 15. For Example <ul><li>SEX </li></ul><ul><ul><li>Male </li></ul></ul><ul><ul><li>Female </li></ul></ul><ul><li>Ethnicity </li></ul><ul><ul><li>Caucasian </li></ul></ul><ul><ul><li>African American </li></ul></ul><ul><ul><li>Middle Eastern </li></ul></ul><ul><ul><li>Indian </li></ul></ul><ul><ul><li>Native American </li></ul></ul><ul><ul><li>Other </li></ul></ul><ul><li>Had a heart attack? </li></ul><ul><ul><li>Yes </li></ul></ul><ul><ul><li>No </li></ul></ul>
  16. 16. 3 Characteristics of Nominal Variables 1. Must be mutually exclusive. 2. Must be equivalent. (no comparing apples to oranges) 3. Must be exhaustive.
  17. 17. What is the problem with this?
  18. 18. Republicans, Democrats, and Independents are not exhaustive of all possible political parties. How would you correct this? The simplest way would be to add an “other” category.
  19. 19. Ordinal Measures Attributes can be ordered.
  20. 20. For Example <ul><li>SES </li></ul><ul><ul><li>Lower Class </li></ul></ul><ul><ul><li>Middle Class </li></ul></ul><ul><ul><li>Upper Class </li></ul></ul><ul><li>Education Level </li></ul><ul><ul><li>Grade School </li></ul></ul><ul><ul><li>Middle School </li></ul></ul><ul><ul><li>High School </li></ul></ul><ul><ul><li>Junior College </li></ul></ul><ul><ul><li>College/University Graduate </li></ul></ul><ul><ul><li>Post Graduate </li></ul></ul><ul><li>Letter Grade </li></ul><ul><ul><li>A </li></ul></ul><ul><ul><li>B </li></ul></ul><ul><ul><li>C </li></ul></ul><ul><ul><li>D </li></ul></ul><ul><ul><li>F </li></ul></ul>
  21. 21. BIG NOTE We do not know the magnitude of the difference between the variables. We just know there is a clear difference.
  22. 22. In Other Words If Middle Class is $30-50,000 And Upper Class is $50,000+ Then The difference between the two could be pennies or millions of dollars – We don’t know.
  23. 23. Interval Measures The distance between attributes has real quantitative meaning.
  24. 24. The distance between IQs of 80 & 100 is the same as the distance between IQs of 140 & 160.
  25. 25. However, we cannot say that someone with an IQ of 160 is twice as intelligent as someone with an IQ of 80. This would require an Absolute Zero.
  26. 26. General Examples <ul><li>Patient Compliance </li></ul><ul><li>Temperature (Celsius and Fahrenheit) </li></ul><ul><li>Neuroticism </li></ul><ul><li>Extraversion </li></ul><ul><li>Patient Satisfaction </li></ul><ul><li>Physician Humor Orientation </li></ul>
  27. 27. Scaled Interval Measures - Likert - Semantic Differential - Thurstone
  28. 28. Likert Scales 5 = Strongly Agree; 4 = Agree; 3 = Undecided; 2 = Disagree; 1 = Strongly Disagree _____1. My physician regularly communicates with others joking with them. _____2. People usually do not laugh at my physician’s attempts at humor.
  29. 29. Semantic Differential Scales Numbers 1 and 7 indicate a very strong feeling. Numbers 2 and 6 indicate a strong feeling. Numbers 3 and 5 indicate a fairly weak feeling. Number 4 indicates you are undecided. Positive 7 6 5 4 3 2 1 Negative Wrong 1 2 3 4 5 6 7 Right Bad 1 2 3 4 5 6 7 Good
  30. 30. Thurstone Scales It’s easy to get AIDS. NO YES People with AIDS deserve what they got. NO YES People with AIDS are like my parents. NO YES
  31. 31. Important Scale Characteristics 1. Multiple questions measuring one concept. 2. Scales must be statistically reliable and valid. 3. Associated numerical values must be meaningful.
  32. 32. Ratio Measures The distance between attributes has real quantitative meaning, and has an absolute zero.
  33. 33. Celsius and Fahrenheit are Interval measures for temperature. Kelvin is a ratio measures for temperature because it has an absolute zero.
  34. 34. Examples <ul><li>Temperature (Kelvin) </li></ul><ul><li>Age </li></ul><ul><li>Height </li></ul><ul><li>Weight </li></ul><ul><li>Mass </li></ul><ul><li>Blood Pressure </li></ul><ul><li>Speed </li></ul><ul><li>Heart Rate </li></ul>
  35. 36. Why Do Levels of Measurement Matter? The type of variables you have determine the statistical devices you can and cannot use.
  36. 37. See Picking a Test
  37. 38. Variables in Research
  38. 39. Dependent Variables The variable that is effected or not effected by another variable in a research study.
  39. 40. Independent Variables The variable that is being manipulated or examined in a study to see if it effects the dependent variable.
  40. 41. Example In a study by Wrench and Booth-Butterfield (2001), it was found that a physician’s humor orientation influenced her or his patient’s level of satisfaction.
  41. 42. What was the IV and DV? IV = Physician Humor Orientation DV = Patient Satisfaction
  42. 43. Another Example Wrench and Booth-Butterfield (2001) also found that the severity of a diagnosis influenced a patient’s desire for information about the diagnosis and information about treatment, but did not effect patient compliance.
  43. 44. What were the IVs and DVs? <ul><li>IV – Severity </li></ul><ul><li> DV – Desired Information about Diagnosis </li></ul>2) IV – Severity DV – Desired Information about Treatment
  44. 45. What were the IVs and DVs? Cont… 3) IV – Severity DV – Patient Compliance
  45. 46. Last Example In a study by Wrench (2002), it was found that males were more physically and verbally aggressive than females.
  46. 47. What were the IVs and DVs? <ul><li>IV – Sex (Males & Females) </li></ul><ul><li> DV – Physical Aggression </li></ul>2) IV – Sex (Males & Females) DV – Verbal Aggression