Calculations for a 2-factor repeated measures design. Participants were asked to

Question

Calculations for a 2-factor repeated measures design. Participants were asked to read information as it scrolled across a screen. They were then asked to recall as much of the information as possible.

The first factor is content difficulty and it has 2 levels (Easy vs. Difficult)

The second factor is the speed at which the information scrolled across the screen and it has 3 levels (Fast, Moderate, and Slow).

The Dependent Variable is the accuracy of their recall on a scale of 0 to 10

Small within data

Large within data

The following questions are what I need help with, thank you!

1) explain what the two data sets share in common, and

2) what is different about the files, and

3) what this means for the final hypothesis testing.

Diticult. Easy Fa 3.07 2.6 3.52 4.24 2.29 2.52 2.36 2.42 3.32 2.74 4.39 3.2 4.52 4.26 3.08 3.14 2.19 3.88 4.32 3.4 4.15 3 BB Mod 3.04. 2.58 3.7 4.35 3.14 5.17 2.65 2.45 3.75 3.2 2.85 2.48 2.27 ,47 2.56 47 2.59 3.09 2.63 3.47 2.05. Slow 2.20 2.97 2.E' 3.26 3.06 2.56 3.03 2.B7 4.63 4.96 3,76 443 4.87 3.25 4.9 4.92 3.EA 3.73 3.69 3.02 3,59 2.40 420 0,956 9.4249 6.7600 7.724 23904 4.884 79776 5.244 4.88 6,35 5.5696 5.8564 0224 64025 9272 6882 02400 20.4304 0.2400 9 4864 9.8596 4.79 50544 32496 8.6824 6.080 5600 72225 5.0544 Sum XA2 087 4234 6.6564 3.764 8.9225 9.8596 5.1321 3.4819 26,7289 9.4884 6.0025 0.6276 4.5024 4.0625 5.0544 8.1225 6.1504 5.1529 4.452 2.592 2.1609 6.5536 2.1609 8.708 9,548 6.9169 2.0409 4.2025 2.3409 2.7556 8.8209 6.7600 2.9584 6.8 0.6276 4.0000 9,3636 3.4225 5.7600 6.5536 3.7249 9.1809 8.2369 2 9584 21.4369 24.6016 376 19.6249 23.7169 0.5625 33.6400 22.2784 24.108 24.2064 3.2496 3.9129 7,8929 3.616 9.12 0.0715562 0.0007562 0,195806L 3630563 0.2280063 0.69 3058L 4340003 0.5662563 0.6930583 0.2730063 0.4658063 0.3875063 0.07700 0150563 0.0915063 0.4615504 0.1595004 0.2607379 0.6550879 0.3018129 0.2607379 0.3976879 0.3256129 2.3123004 0.0286879 0.0049879 0.3713379 0.0896254 0.0964879 0.19 3050M 0.0286879 SS C 7 8775 5.9478938 0.890664 0.034689 0.682689 0.147264 0.134139 0.02 2.7 0.196914 0.763439 52939 0.069564 0.087764 0.061189 0.126914 0.104814 0.4 4473 0.0766588 0.0044723 0.0086723 0.35 0.5374723 0.1273598 0.5374723 0.1496724 0.7865473 0.182 049723 0.0234473 0.4530973 0.2949848 290473 7.24017! 6.1863438 0.3256128 0.0102503 0.46156039 443628 0.7406753 0.1695003 0,4364253 0.3018128 3.90625 -07 0.02580039 0.2203128 0.3976878 0.2214878 0.4615503 0253 0.1373628 0.4908753 4937539 0.0201128 0.3728628 BB378 2.3735253 0.2121753 0.4233128 0.43642539 0.3836253 0.2302478 0.0008628 0.3241878 360503 0.4480628 4.32 29375 8.71689375 Total Variabi MS Within Within Variability 40.29 0.447746 etween Variability 50.34829583 Speed Main Effect MS Speed F Speed 2.573982 5.147984563 5.74876 Between treatments 50.3MB3 5.00 88815 88815 683838 0.004088 Speed. 2 2573982 47965 5.74876 00447 Ease X 41.31218 2 20.65609 46.13 0.00 0.00447114

Explanation / Answer

1) The p-value for the interaction is same in both the datasets. As, p-value < 0.05, we reject the null hypothesis and conclude that there is interaction between Ease and Speed.

2) The p-values for Ease and Speed main effect is different in 2 files. For 1st file, p-values are less than 0.05, so we reject the null hypothesis and conclude that there is significant Ease and Speed main effect in the first file. As , p-value > 0.05, for Ease and Speed main effect in 2nd file, we fail to reject the null hypothesis and there is no significant Ease and Speed main effect in the second file.

3) This means for the final hypothesis testing that there is no significant Ease and Speed main effect but there is interaction between Ease and Speed.

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