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Week 5 ETEC 668 Quantitative Research in Educational Technology
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  1. Week 5 ETEC 668 Quantitative Research in Educational Technology Dr. Seungoh Paek February 12, 2014

  2. Tonight’s Agenda • Continuing SPSS • Introduction to PSPP • Introduction to RStudio • Introduction to Probability • Group Discussion for Research Paper

  3. Continuing Week 4

  4. Using SPSS

  5. Using SPSS

  6. Sigma Freud & Descriptive Statistics A Picture is Really Worth a Thousand Words

  7. Histograms with Polygon Hand Drawn Histogram

  8. Cool Ways to Chart Data • Line Chart

  9. Cool Ways to Chart Data • Pie Chart

  10. Using the Computer to Illustrate Data • Creating Histogram Graphs

  11. Using the Computer to Illustrate Data • Creating Bar Graphs

  12. Using the Computer to Illustrate Data • Creating Line Graphs

  13. Using the Computer to Illustrate Data • Creating Pie Graphs

  14. A Taste of PSPP

  15. PSPP Download PSPP - For Mac, click here. For Window, click here.

  16. A TASTE of RSTudio

  17. R • R is a free software environment for statistical computing and graphics.

  18. RStudio • RStudio is a free and open source integrated development environment (IDE) for R, a programming language for statistical computing and graphics.

  19. Probability, Samples, Bell Curve, zScores, Hypotheses, Hypothesis Testing, & significance Chapter 7 & Chapter 8

  20. Probability

  21. Why Probability? • Describe and predict what we don’t know from current data • Basis for the Degree of confidence a Hypothesis is “true” • statistical significance

  22. Examples • Flip a coin • 2 possible outcomes • Heads or Tails • 50% chance each • Role a Die • 6 possible outcomes • 1 – 2 – 3 – 4 – 5 – 6 • 16.6% chance each • Flip 2 coins • How many possible outcomes? • What % chance for each?

  23. Examples • Flip 2 coins • 4 possible outcomes • 25% chance each

  24. Sample v Population

  25. Definitions • Population • The large group to which you would like to generalize your findings • Sample • The smaller, representative group of the population used for research.

  26. Characteristics of a sample • Needs to be representative • Truly random = representative = unbiased • Sampling error – • how well the sample represents the population • Size matters – • larger sample = more representative

  27. Mathematical Symbols • Mean • Population = μ • Sample = X • Standard Deviation • Population = σ • Sample = SD • Variance • Population = σ2 • Sample = SD2 • Number of Cases • Population = N • Sample = n

  28. The Normal Curve

  29. The Normal curve

  30. More Normal Curve

  31. About Normal Curve • Almost all scores fall between -3 and +3 SD from mean • 99.74% • Specific percentages between points on x-axis • 2 or more normal curves can be compared

  32. Normal Curve and Percents

  33. zScores

  34. The z Score • The number of standard deviations from the mean • Negative scores are below (left of) the mean • Positive scores are above (right of) the mean

  35. The z Score • Standard Score • Allows you to compare apples and oranges • The probability of a score occurring =

  36. Hypotheses

  37. What is a Hypothesis? • An “educated guess” • Direct extension of the question • Translates problem or research question into a testable form • Two types • Null Hypothesis • Research Hypothesis

  38. A Good Hypothesis • Declarative statement • Expected relationship between variables • Reflection of theory/literature • Brief, to the point • Testable

  39. Why a Null Hypothesis? No amount of experimentation can ever prove me right; a single experiment can prove me wrong. ~Albert Einstein

  40. The Null Hypothesis • Statement of no relationship • Two things are equal H0 : μA = μB • Refers to Population • Indirectly tested

  41. The Research Hypothesis • Definite Statement • Relationship exists between variables • Two types • Nondirectional - H1 : XA ≠ XB • Directional - H1 : X1 > X2 • Refers to sample • Directly tested

  42. Hypothesis Testing

  43. Hypothesis Testing • All events have a probability associated with them • p = your guess of chance p < .05 • .05 or 5% in Education and Psychology • 5% likelihood of results occurring by chance alone

  44. Error types • Type I • Reject H0 when you should not • Type II • Fail to reject H0 when you should

  45. Error Table

  46. Significance

  47. Statistical • Based on probability • Research was technically successful • H0 was rejected • P value • Education p < .05 = 5% chance • Medical p < .01 or .001 = 1% or .1% chance

  48. Practical • Does it mean anything to the population? • Is that new treatment worth the cost? • Are my students really doing that much better?

  49. Questions?

  50. Stating the Research Question February 12, 2014