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Therefore buy 100 mg kamagra soft fast delivery, it is the characteristics of the dependent scores that determine whether we compute the mean discount kamagra soft 100 mg without prescription, median generic kamagra soft 100mg without prescription, or mode purchase 100 mg kamagra soft free shipping. Usually it is appropriate to compute the mean buy cheap kamagra soft 100mg on line, and we do so for each condition of the independent variable. Say that we think people will make more mistakes when recalling a long list of words than when recall- ing a short list. For each participant, we measure the dependent vari- able of number of errors made in recalling the list. A relationship is present here because a different and higher set of error scores occurs in each condition. Most experi- ments involve a much larger N, however, so to see the relationship buried in the raw scores, we compute a measure of central tendency. In our memory experiment, the variable of recall errors is a ratio variable that is as- sumed to form an approximately normal distribution. Therefore, we compute the mean score in each condition by computing the mean of the scores in each column. There- fore, to interpret the mean in any study, simply envision the scores that would typi- cally produce such a mean. For example, when X 5 3, envision a normal distribution of scores above and below 3, with most scores close to 3. Likewise, for each mean, essentially envision the kinds of raw scores shown in our columns. Thus, the means show that recalling a 5-item list resulted in one distribution located around three er- rors, but recalling a 10-item list produced a different distribution at around six errors, and recalling a 15-item list produced still another distribution at around nine errors. Further, we use the mean score to describe the individual scores in each condition. In Condition 1, for example, we’d predict that any participant would make about three errors. Most important is the fact that, by looking at the means alone, we see that a rela- tionship is present here: as the conditions change (from 5 to 10 to 15 items in a list), the scores on the dependent variable also change (from around 3, to around 6, to around 9 errors, respectively). For example, we might find that only the mean in the 5-item condition is different from the mean in the 15-item condition. We still have a relationship if, at least sometimes, as the conditions of the independent variable change, the dependent scores also change. For example, say that we study political party affiliation as a function of a person’s year in college. Our dependent variable is political party, a nominal variable, so the mode is the appropriate measure of central tendency. We might see that freshmen most often claim to be Republican, but the mode for sophomores is Democrat; for juniors, Socialist; and for seniors, Communist. These data reflect a relationship because they indicate that as college level changes, political affiliation tends to change. This tells us that the location Participants Recalling a of the distribution of incomes is dif- 5-, 10-, or 15-Item List ferent for each class, so we know The mean of each condition 4 that the income “scores” of individ- is under each column. Summarizing Research 75 Graphing the Results of an Experiment Recall that the independent variable involves the conditions “given” to participants so it is plotted on the X axis. However, be- cause we want to summarize the data, usually we do not plot the individual scores. Rather, we plot either the mean, median, or mode of the dependent scores from each condition. Note: Do not be confused by the fact that we use X to represent the scores when computing the means. The type of graph to select is determined by the characteristics of the independent variable. Line Graphs Create a line graph when the independent variable is an interval or a ratio variable. We use straight lines to connect the data points here for the same reason we did when producing polygons: Anytime the variable on the X axis in- volves an interval or ratio scale, we assume that it is a continuous variable and there- fore we draw lines. The lines show that the relationship continues between the points shown on the X axis. For example, we assume that if there had been a 6-item list, the mean error score would fall on the line connecting the means for the 5- and 10-item lists.