1.

An educational psychologist is examining response times to an on-screen stimulus. The researcher believes there might be a weak effect from ager but expects a more pronounced effect for different color contrasts. She decides to examine a black on white (BM) combination compared to 2 alternatives: red on white {WW} and yellow on blue (WB). Here is the data for response times (in milliseconds]: BM RM Y! B 3 0 20 23 'I 5 43 44 21 7 26 1o 16 25 16-17 9 20 8 25 31 34 35 23 21 21 19 4 33 15 23 16 4? 36 25 3? 31 2o 36 31 13-19 1? 19 45 24 1? 23 12 35 36 14 39 23 13 20 26 39 31 39 24 19 36 211-21 16 29 43 2 1 2? 28 2 7 41 1 7" 2 3 29 4D 27 26 43 Using MS Excel, conduct a 2-wayANOVA with or z 0.05. Fill in the summary table. The last column in the table below is read as, " Partial Eta-squared " and is a concept that was NOT covered in the lectures. Partial 732 is used when there is a chance the data is not independent lclick here to read more if interested E'FU Computing the values for "Partial 1} 2" is not difcult. Simply divide $5.3m?ct by the sum of SSEffect and Sam. For exa mp le, Partial 11 2m} = SSA \"SSA + SSerror} F-values should be accurate to 4 decimal places and all othervalues accurate to 3 decimal places. Age (A) Color (B) II'IIE l'aClIlO (A x B} Provide the conclusions for this 2Away ANOVA. what is the conclusion regarding the main effect for age [A or rows}: C:- There appears to be an effect due to age. There is not enough evidence to reject the assumption that the marginal means across age groups are equal. What is the conclusion regarding the main effect for color {B or columns}: C:- There appears to be an effect due to color. C? There is not enough evidence to reject the assumption that the marginal means across color groups are equal. what is the conclusion regarding an interaction effect between age and color {A x B): \"."J- There appears to be an interaction effect between age and color. C. There is not enough evidence to reject the assumption of a lack ofan interaction. A researcher believes that if students participate in an extracurricular training session, then they will be more likely to perform better on the mathematics test. Use the data below and Excel's ANOVA testing with o: = 0.05 to test the relevant hypotheses. Control: Treatment: No Training Attend Training 56.4 60.3 34.1 53.6 52.3 52.6 Year 7 58.6 61.5 58.3 55.2 48.6 416 51.0 51.5 46.8 67.4 43.8 53.4 49.6 62.2 Year 1|] 5?.5 65.4 39.1 62.0 53.]Ir 64.6 42.5 729 (1a) What is the Fvalue for the treatment effect? | (Report answer accurate to 2 decimal place.) (1b) what is the Jo-y'alue for the Fualue for the treatment effect? I (Report answer accurate to 4 decimal places.) (1:) Does this support the researcher's hypothesis that the treatment has an effect on ability to solve the mathematics exercise? ff)- yes r'\". u" no (2a) What is the Fvalue for the school-year effect? ' (Report answer accurate to 2 decimal place.) (2b) What is the Jt:i-i.ialue for the Fvalue for the school-year effect? I (Report answer accurate to 4 decimal places.) (2: ) Does this support the researcher's assumption that school-year does NOT have an effect on ability to solve the mathematics exercise? 11'} yes 0 no (3a) What is the Fvalue for the interaction effect? I (Report answer accurate to 2 decimal place.) (3b) What is the Jo-y'alue for the Fyalue for the interaction effect? I (Report answer accurate to 4 decimal places.) (3: ) Does this support the researcher's hypothesis that the treatment effect is moderated by school-year? =.'. '3' ya 5 r . L" no An educational psychologist is examining response times to an on-screen stimulus. The researcher believes there might be a weak effect from age, but expects a more pronounced effect for different color contrasts. She decides to examine a black on white (BM) combination compared to 2 alternatives: red on white (RM) and yellow on blue (WE). Here is the data for response times {in milliseconds]: BM RIW 'HB 31 23 42 1 O 43 23 36 20 32 22 T" 2 16-1? 16 19 18 26 2'0 19 22 31 6 11 16 21 15 35 30 26 19 30 13 39 35 25 4? 44 15-19 1? 36 22 34 17 2? 21 3? 35 18 15 22 29 28 16 23 3O 42 41 19 2? 2&21 29 25 25 1 8 18 35 20 26 39 26 40 38 25 28 42 Using MS Excel, conduct a 2-way ANOVAwith or = 0.05. Fill in the summary table. The last column in the table below is read as, "Partial Eta-squared " and is a concept that was NOT covered in the lectures. Partial 73 2 is used when there is a chance the data is not independent (click here to read more if interested '5} Computing the values for "Partial r; 2" is not difcult. Simply divide 559nm by the sum ofSSeect and 55mm. For example, Partial 1r; 2m) = SSA {{SSA + 55mm) P-values should be accurate to 4 decimal places and all other values accurate to 3 decimal places. Color (B) Interaction Provide the conclusions for this 2-way ANOVA. What is the conclusion regarding the main effect for age {A or rows}: IL") There appears to be an effect clue to age. (3' There is not enough evidence to reject the assumption that the marginal means across age groups are equal. What is the conclusion regarding the main effect for color (B or columns}: If") There appears to be an effect due to color. C3 There is not enough evidence to reject the assumption that the marginal means across color groups are equal. What is the conclusion regarding an interaction effect between age and color (A x B]: I'I l' There appears to be an interaction effect between age and color. {3' There is not enough evidence to reject the assumption of a lack of an interaction