The lecture focused on how to explore, clean and describe the data upon completion of running your studies.

Transcription

1 Yerika Jimenez Week 11 Scribe #2 Data Analysis The lecture focused on how to explore, clean and describe the data upon completion of running your studies. The first thing we need to do is import the data to the R Console. File.name <- file.choose() - My.data <- read.csv(file.name, row.names=null) After import, review for basic things about the data. Those simple things are the mean, standard deviation, min, and max. For instance, you have a case study with 8 UF undergraduate students who tried two different interfaces to make a drawing of an object. One participant did the drawing with the mouse and the other did the drawing with the leap motion controller. This is a between subject design that has participant 1 4 doing the first condition and 9 12 doing the second condition. Participant Interface Time taken Contact interface 1 Mouse Mouse Mouse Mouse Leap Leap Leap Leap 56 0 Table 1: Case study data Independent variable = interface Dependent variable = time taken You will want to see the dimensions of the data. In this case, 8 (the number of participants) and 4 (the number of fields). When running a between subject design you want to ensure that the number of rows in your data is equal to the number of participants you had. The number of row will be the number of participants x the number of conditions. dim(my.data [,3])

2 For example, in within subject design participants 1 4, did the experiment using the mouse and participants 9 11 perform the experiment using the leap motion controller. The number of rows will be 4 (participants) * 2 (conditions). Now lets look at the mean, standard deviation, min and max of the entire data set. *Number [,3] represents the column Mean(my.data, [,3]) = sd(my.data, [,3]) = min(my.data, [,3]) = 31 max(my.data, [,3]) = 80 This is a sanity check, as you may forget if you were using minutes instead of seconds. The mean and standard deviation helps you understand the data in more detail. As for finding the min and max that helps you ensure that they are not equal and ensures that you have good data to work with. Tip: When analyzing the data always record the overall mean, sd, min and max in your lab notebook or report. Histogram A histogram is visually representations of the distribution of a data. In trying to see if the frequency of getting a particular number over a pattern. This is how you read the data, numbers between 40 and 50 occurred only 2 times. Figure 2: Histogram of the time taken

3 Normal Distribution Normal distribution helps see a pattern in your data and in every study you run. You want the data to he normally distributed. However, that will not be the case for every instance. Usually, with few values it is not going to be normally distributed but you can look at the histogram, all the values are not skewed in any way. The entire range is well covered and over all that is what you want to have. Figure 3: Normal distribution [1] If your data is not normally distributed, it usually means that there is something wrong and you need to look at it again. Lets continue to the next example by looking at the dependent variable (time taken) for each of the conditions. This is to ensure that the conditions are normally distributed. *[1:4] represent data points 1 to 4 in your table [,3] represents the column hist(my.data[1:4, 3] Mouse condition hist(my.data[5:8, 3] Leap condition

4 Figure 4: Histogram of mouse condition (left) and leap condition (right) Tip: You might want to repeat this analysis for all conditions creating a table that contain the following: means, standard deviation, max and min. Condition Mean SD Max Min Mouse Leap Table 2: Summary of basic data for each condition *[1:4] represent data points 1 to 4 in your table [,3] represents the column. [5:8] represent the participants of second condition. Mean(my.data, [1:4,3]) = 70.5 sd(my.data, [1:4,3]) = max(my.data, [1:4,3]) = 80 min(my.data, [1:4,3]) = 63 Box Plots A box plot is another way to visually explore your data. In a box plot the mean marks (it will have a thick line) the mid- point of your that and it shown by a line that divides the box into two parts. The 25 th and 45 th percentile and the whisker represent the edges of your data. A box plot helps see if your data is normally distributed, skew or if it has any outliners.

5 Figure 5: A detail example of a Box Plot [2] If there appears to be too many outliners, this means that in your data there is a condition that participants thought was to easy or one participant did very poorly. When you find it you must excluded from your data analysis. Figure 6: Box Plot with column of the mouse condition

6 Student Example Conducting a between subject design case study with 8 UF graduate students who tried two different interfaces to make a type a paragraph. Participant 1-5 will perform the task with a regular keyboard and participant 6-10 will perform the task with a touchscreen keyboard. Participant Interface Time taken Contact interface 1 Keyboard Keyboard Keyboard Keyboard Touch Screen Touch Screen Touch Screen Touch Screen 57 0 Table 3: Case study data Over all mean, standard deviation, min and max of my data. Mean(my.data, [,3]) = 40.5 sd(my.data, [,3]) = min(my.data, [,3]) = 12 max(my.data, [,3]) = 75 Figure 7, represents the histrogram of the two conditions. Figure 7: Histogram of keyboard condition (left) and touch screen condition (right)

7 Summary of mean, standard deviation, min and max for each condition Condition Mean SD Max Min keyboard Touch Screen Table 4: Summary of basic data for each condition Figure 8, represents the plot box of the two conditions. Figure 8: Plot Box of keyboard condition (left) and touch screen condition (right) References [1] Normal Distribution. (n.d.). Retrieved March 30, 2015, fromhttps:// [2] How to Read and Use a Box-and-Whisker Plot. (2008, February 15). Retrieved March 31, 2015, from