2. SHOWTIME!

3. STATISTICAL TOOLS IN EVALUATION CORRELATION TECHNIQUE SIMPLE PREDICTION TESTS OF DIFFERENCE

4. DETERMINING RELATIONSHIPS BETWEEN SCORES MANY SITUTATIONS WHERE ONE MAY WANT TO KNOW THE RELATIONSHIP BETWEEN: SCORES ON TWO SIMILAR TESTS (I.E., RELIABILITY MEASURE) OR TWO DIFFERENT TESTS (AMOUNT OF SHARED VARIANCE OR INFORMATION OF TWO TESTS) “if there are seven tests in a battery of tests and two of the tests are highly related, the battery could be reduced to six tests with no loss of information”

5. DETERMINING RELATIONSHIPS BETWEEN SCORES - GRAPHING TECHNIQUE PLOTTING OF THE SCORES FOR TWO TESTS OF EACH INDIVIDUAL IN A GRAPH THE CLOSER ALL PLOTTED POINTS ARE TO THE TREND LINE, THE HIGHER OR LARGER THE RELATIONSHIP WHEN THE PLOTTED POINTS RESEMBLE A CIRCLE MAKING IT IMPOSSIBLE TO DRAW A TREND LINE, THERE IS NO LINEAR RELATIONSHIP BETWEEN THE TWO MEASURES BEING GRAPHED

6. DETERMINING RELATIONSHIPS BETWEEN SCORES - GRAPHING TECHNIQUE

9. DETERMINING RELATIONSHIPS BETWEEN SCORES - GRAPHING TECHNIQUE OF A LARGE DATA BASES USING A COMPUTER

11. CORRELATION TECHNIQUE MATHEMATICAL TECHNIQUE FOR DETERMINING THE RELATIONSHIP BETWEEN TWO SETS OF SCORES PEARSON PRODUCT-MOMENT CORRELATION USED WITH RATIO AND INVERVAL DATA SPEARMAN’S RHO OR RANK ORDER CORRELATION TECHNIQUE USED WITH ORDINAL DATA

12. PEARSON PRODUCT-MOMENT FORMULA

13. CALCULATION USING PEARSON PRODUCT-MOMENT FORMULA

16. TWO CHARACTERISTICS OF CORRELATIONAL COEFFICIENTS DIRECTION OF THE RELATIONSHIP IS INDICATED BY WHETHER THE CORRELATION COEFFICIENT IS POSITIVE OR NEGATIVE POSITIVE COEFFICIENT INDICATES THAT AN INCREASE IN SCORES ON ONE MEASURE IS ACCOMPANIED BY AN INCREASE IN SCORES ON THE OTHER MEASURE OR THAT A DECREASE IN SCORES ON ONE MEASURE IS ACCOMPANIED BY A DECREASE IN SCORES ON THE OTHER MEASURE NEGATIVE COEFFICIENT INDICATES THAT AN INCREASE IN SCORES ON ONE MEASURE IS ACCOMPANIED BY A DECREASE IN SCORES ON THE OTHER MEASURE -EXISTS BECAUSE OF OPPOSITE SCORING SCALES OR A TRUE NEGATIVE RELATIONSHIP EXISTS STRENGTH OF THE RELATIONSHIP IS INDICATED BY HOW CLOSE THE COEFFICIENT IS TO 1; THE CLOSER THE COEFFICIENT IS TO 1, THE STRONGER THE RELATIONSHIP BETWEEN THE TWO VARIABLES

17. INTERPREATATION OF CORRELATION COEFFICIENT A HIGH CORRELATION (r) BETWEEN TWO VARIABLES DOES NOT DOES NOT IMPLY A CAUSE- AND EFFECT-RELATIONSHIP A STRONG CORRELATION (r) BETWEEN SHOE SIZE AND MATH ABILITY IN K-12 STUDENTS DOES NOT MEAN THAT AN INCREASE IN SHOE SIZE WILL INCREASE MATH ABILITY COEFFICIENT OF DETERMINATION (r 2 ) IS THE TRUE INDICATOR OF THE DEGREE OF RELATIONSHIP INDICATES THE AMOUNT OF VARIABILITY IN ONE MEASURE THAT IS EXPLAINED BY THE OTHER MEASURE IF r =.90 BETWEEN HEIGHT AND BODY WEIGHT, THE COEFFICIENT OF DETERMINAITON (r 2 ) EQUALS.81 MEANING THAT 81% OF THE VARIABILITY IN BODY WEIGHT SCORES IS DUE TO THE INDIVIDUALS’ HAVING DIFFERENT HEIGHT AS r DECREASES, r 2 DROPS DRAMATICALLY AS AN r =.60 HAS AN r 2 =.36 or 36% AND r =.40 HAS AN r 2 =.16 or 16% BASED ON THE ASSUMPTION THAT THE RELATIONSHIP BETWEEN THE TWO VARIABLES IS LINEAR

18. SIMPLE PREDICATION OF AN UNKNOWN SCORE (Y’) FOR AN A KNOWN MEASURE (X)

20. QUESTIONS OR COMMENTS?? THANK YOU!!