conversations.from: human
conversations.value: a description for a book titled Understanding Statistics in the Behavioral Sciences about University, Washington, Pittsburgh with the genre: Science & Math, Mathematics. End with the sentence ' While at the University of Washington, he was a finalist for the Outstanding Teaching award for his instruction of introductory statistics.'.
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conversations.value: the author received a Bachelor of Electrical Engineering degree from Rensselaer Polytechnic Institute in 1956 and a Ph.D. in Biological Psychology from Yale University in 1965. He was Assistant Professor and Associate Professor in the Department of Psychology at the University of Washington, Seattle, from 1965 to 1989. He was Associate Chairman of the Department of Neuroscience at the University of Pittsburgh, from 1990 to June 2000. While there, in addition to his other duties, he served as Director of Undergraduate Studies, was the departmental adviser for undergraduate majors, taught both undergraduates and graduate statistics courses, and served as a statistical consultant for departmental faculty. the author was also Director of the Statistical Cores for two NIH center grants in schizophrenia and Parkinson's disease. He retired from the University of Pittsburgh in June 2000. the author taught introductory statistics courses at the University of Washington and at the University of Pittsburgh for over thirty years combined. While at the University of Washington, he was a finalist for the Outstanding Teaching award for his instruction of introductory statistics.
conversations.from: human
conversations.value: the first five paragraphs for this book. Start with the sentence 'Understanding Statistics in the Behavioral Sciences
  A single-factor study, as the name implies, is concerned with the effect of a single research factor on the dependent variable'.
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conversations.value: Understanding Statistics in the Behavioral Sciences
  A single-factor study, as the name implies, is concerned with the effect of a single research factor on the dependent variable. As such, single-factor studies are among the simplest researchers use. For example, if we confine our attention solely to parental status, then the button-pushing study could be an example of a single-factor study. Moreover, it would be the simplest single-factor between-subjects study possible, one involving only two groups or two levels of the factor. We have already discussed in chapters 8 and how to analyze data from single-factor between-subjects studies when only two groups are involved, but studies involving more than two groups are common. For example, a researcher might want to investigate the effect of three or four different kinds of treatment on patients' neuroticism or the effect of religious background on individual attitudes toward abortion.
  In general terms, the analysis of data from a single-factor study allows us to determine whether knowing the group to which a subject belongs (which could be a self-assigned natural group or an experimenter-assigned experimental group) allows us to make better predictions than otherwise as to how that individual will respond. In statistical terms, the question is whether the proportion of variance accounted for by the research factor is significantly different from zero. The null hypothesis, which claims no effect for the research factor, would predict an R2 of zero. A sample will almost always yield a value of R2 higher than that, but the key statistical question is whether the obtained value of R2 would occur less than 5% of the time just due to the luck of draw, if the population value for R2 really were zero. If the F ratio associated with R2 exceeds the appropriate critical value, we declare the null hypothesis untenable and label our obtained value statistically significant: In other words, we claim a statistically significant effect.
  In order to test the significance of a single research factor, we need some way to represent the group to which each subject belongs. If the single research factor were a quantitative variable, like maternal age, then one predictor variable would suffice and for each subject the value for this variable would be that subject's age. This variable could be entered into a multiple-regression equation, where it would account for one degree of freedom.
  Similarly, if the single research factor were a binary qualitative variable, like parental status (parent vs. nonparent), again one predictor variable would suffice. Parents would be assigned one value, nonparents a second value. As we saw in Exercise 9.4, the values selected to represent the two groups are not critical; the two values must be different and that is all. A binary research factor, then, is represented with one coded variable and hence accounts for one degree of freedom.
  Qualitative research factors comprising more than two groups or levels are handled somewhat differently. The general rule is, a factor with G groups or levels must be represented with G − 1 coded variables. There are several ways to do this, but we emphasize two here: dummy variable coding, because it is so simple, and contrast coding, because contrasts can be interpreted as planned comparisons.
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conversations.value: three more paragraphs. Start with the sentence 'If the obtained F ratio exceeds its critical value, we would say that the drug effect was statistically significant'.
conversations.from: assistant
conversations.value: If the obtained F ratio exceeds its critical value, we would say that the drug effect was statistically significant. The next exercise demonstrates in detail how this works, so do not worry if the procedure is not yet completely clear. For the moment, focus on the overall strategy. First total variability is partitioned into two pieces: variability between subjects and variability within subjects. Then variability within subjects is subdivided into variability due to the repeated factor and residual variability. Finally, the significance of the repeated factor is determined by comparing a mean square (variance estimate) for the repeated factor with the mean square for the residual.
  The proportion of total variance associated with the mean square for the within-subjects F test is smaller than the proportion associated with the between-subjects F test. There is an easy way to visualize this situation. Consider the drug versus placebo independent groups version of the lie detection study. There are 10 subjects, 10 scores, and 9 degrees of freedom total. Therefore total variability can be represented with nine predictor variables, one representing drug group and the remaining eight representing residual error between subjects or error within groups. Before we have only formed the first predictor variable, the one that represents drug group (labeled X in Fig. 10.2) but it is possible to complete the set, forming the remaining eight predictor variables (see Fig. 15.1).
  In Fig. 15.1 the predictor variable representing drug group is labeled A and the eight predictor variables representing subjects are labeled S1–S8. Predictor variable A is coded 1 for the drug subjects and 0 for the placebo subjects. Five subjects are nested within each group. Predictor variables S1–S4 represent the subjects nested within the drug group, and predictor variables S5–S8 represent the subjects nested within the placebo group. Variables S1–S4 are coded as though the first five subjects each represented different groups. Variable S1 is coded 1 for subject one, variable S2 is coded 1 for subject two, S3 is coded 1 for subject three, and S4 is 1 for subject four. Subject five, whose values for S1–S4 are all 0, represents, in effect, a comparison subject for the drug group. Similarly, variable S5 is coded 1 for subject six (the first subject in the placebo group), S6 is coded 1 for subject seven, S7 is coded 1 for subject eight, and S8 is coded 1 for subject nine. Subject ten, like subject five, represents a comparison subject but for the placebo group. However, subjects five and ten differ in their value for variable A. Thus a different pattern of predictor variable values is associated with each of the 10 scores.
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conversations.value: the last paragraph. Start with the sentence 'The first number in each row gives a Z score accurate to the first decimal digit'.
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conversations.value: The first number in each row gives a Z score accurate to the first decimal digit. Subsequent columns provide the second decimal digit. The numbers in the table give the proportion of area under the normal curve to the left of the indicated Z score. For example, the proportion of area to the left of a Z score of −1.96 is.0250 and the proportion of area to the left of a Z score of +2.58 is.9951.