Lab 5 SPF

Lab 5 SPF - Statistics 101A Lab 5 Split plot factorial...

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Statistics 101A Lab 5 Split plot factorial Professor Esfandiari The objective of this lab is to: Introduce you to the situations in which you typically conduct split-plot factorial Show you how to enter and run the data on SPSS Show you how to interpret the results I When do we use SPF? Example 1: I will first use the data in chapter 12 of Kirk to illustrate SPF design. As you know the Split plot factorial is a mixed design; a combination of a within effect factor (such a repeated measures, or making blocks of subjects based on a confounding factor and randomly assigning them to treatments) plus a between subject factor. Example: Measuring the blood pressure of a random number of five males and five females who are taking a blood pressure medication (after one, two, three and four weeks). The between subject factor is gender and the within subject factor is the four measures of blood pressure. Blocking nine males and nine females based on their weight. Randomly assigning the three males with the highest weight to three weight loss programs (A, B, and C) and continuing this till all the nine males are assigned to the three programs and doing the same thing with females. The between subject factor is gender and the within subject factor is the method of weight loss. The dependent variable is the amount of weight loss. So, one can conclude that the above examples are like a combination of one-way ANOVA and randomized block design. Just like two way-anova, the major reason that we carry out a split-plot factorial design is the fact that we are interested in the interaction between the within and the between subject factor. In example 1, we want to find out if the trend in the change of blood pressure is similar for males and females and in example two we want to find out if the three methods of weight loss are equally effective for males and females. II Example from Kirk: (page 494). The objective of a study was to find out if the type of signal (auditory or tone and visual or light) had any effect on response latency during a four hour monitoring period. Type of signal is variable A and monitoring period (first, second, third, and fourth hour) is variable B. The dependent variable is response latency to the auditory and 1

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visual signals. A random sample of eight subjects were obtained and they were randomly assigned to two sub-samples and observed under all of four levels of treatment B. The data is given below: B1 B2 B3 B4 A1 S1 3 4 7 7 S2 6 5 8 8 S3 3 4 7 9 S4 3 3 6 8 A2 S5 1 2 5 10 S6 2 3 6 10 S7 2 4 5 9 S8 2 3 6 11 The linear model is as follows: Yijk = μ + α j + π i(j) + β k + αβ jk + e ijk The score of each person can be partitioned to: Grand mean or the mean of all the 32 observations, The effect of the type of signal (the row effect or the between subject effect) The effect of the block i The effect of the monitoring period (the column effect or the within subject
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Lab 5 SPF - Statistics 101A Lab 5 Split plot factorial...

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