Statistical Methods For Psychology 8th Edition David C. Howell - Solutions

Looking to master statistical concepts with ease? Dive into "Statistical Methods For Psychology 8th Edition" by David C. Howell. Our online resources offer comprehensive solutions, including an answers key and solutions PDF for every chapter. Whether you're seeking solved problems, step-by-step answers, or a full solution manual, we provide detailed explanations to support your learning. Explore our extensive questions and answers section, designed to complement your textbook study. For instructors, our manual and test bank are invaluable tools. Access chapter solutions and more, all available for free download, to enhance your understanding and excel in psychology statistics.

6. Relation of unbiasedness and inuariance. (i) If 60 is the unique (up to sets of measure 0) unbiased procedure with uniformly minimum risk, it is almost invariant. (ii) If G is transitive and G* commutative, and if among all invariant (almost invariant) procedures there exists a procedure 60 with
5. Let CC be any class of procedures that is closed under the transformations of a group G in the sense that 6 E 'i implies g*6g- 1 E 'i for all g E G. If there exists a unique procedure 60 that uniformly minimizes the risk within the class 'i, then 60 is invariant.' If 60 is unique only up to sets
4. Nonexistence of unbiased procedures. Let Xl " '" Xn be independently distributed with density (l/a)j(x - ~)/a), and let 0 =a, a) . Then no estimtor of exists which is unbiased with respect to the loss function (d - nk j a'' , Note. For more general results concerning the nonexistence of
3. Median unbiasedness. (i) A real number m is a median for the random variable Y if P{ Y m} t, P{Y m} t. Then all real ai' a2 such that m s a\ s a2 or m a\ a2 satisfy ElY - ad ElY - a21· (ii) For any estimate 8(X) of '1(0), let m-(O) and m+(O) denote the infimum and supremum of the medians of
2. Unbiasedness in point estimation . Suppose that 'I is a continuous real-valued function defined over n which is not constant in any open subset of n, and that the expectation h(O) = Eg8(X) is a continuous function of 0 for every estimate 8( X) of '1(0). Then (11) is a necessary and sufficient
1. The following distributions arise on the basis of assumptions similar to those leading to (1)-(3). (i) Independent trials with constant probability p of success are carried out until a preassigned number m of successes has been obtained. If the number of trials required is X + m, then X has the
The history of statistical hypothesis testing really began with a tea-tasting experiment(Fisher, 1935), so it seems fitting for this book to end with one. The owner of a small tearoom does not think that people really can tell the difference between the first cup made with a given tea bag and the
For the data in Exercise 18.5, we could say that 3 out of 10 residents used fewer hypotheses the second time and 7 used more. We could test this with x2. How would this differ from Friedman’s test applied to those data?
It would be possible to apply Friedman’s test to the data in Exercise 18.5. What would we lose if we did?
What advantage does the study described in Exercise 18.18 have over the study described in Exercise 18.17?
The test referred to in Exercise 18.19 is available on in the Window’s program on the book’s Web site. Run that program on the data for Exercise 18.18 and report the results. (There is a“read-me” file on the disk that will tell you how to run the resampling program.)
I did not discuss randomization tests on the evaluation of data that are laid out like a one-way analysis of variance (as in Exercise 18.17), but you should be able to suggest an analysis that would be appropriate if we had the software to carry out the calculations.How would you outline that test?
As an alternative method of evaluating a group home, suppose that we take 12 adolescents who have been declared delinquent. We take the number of days truant (1) during the month before they are placed in the home, (2) during the month they live in the home, and (3) during the month after they
A psychologist operating a group home for delinquent adolescents needs to show that it is successful at reducing delinquency. He samples nine adolescents living in their parents’ home that the police have identified as having problems, nine similar adolescents living in foster homes, and nine
Three rival professors teaching English I all claim the honor of having the best students. To settle the issue, eight students are randomly drawn from each class and are given the same exam, which is graded by a neutral professor who does not know from which class the students came.The data
Why is rejection of the null hypothesis using a t test a more specific statement than rejection of the null hypothesis using the appropriate nonparametric test?
One of the arguments put forth in favor of nonparametric tests is that they are more appropriate for ordinal-scale data. This issue was addressed earlier in the book in a different context. Give a reason why this argument is not a good one.
What is the difference between the null hypothesis tested by Wilcoxon’s matched-pairs signed-ranks test and the corresponding t test?
What is the difference between the null hypothesis tested by Wilcoxon’s rank-sum test and the corresponding t test?
The results in Exercise 18.8 are not quite as clear-cut as we might like. Plot the differences as a function of the first-born’s score. What does this figure suggest?
How would we run a standard resampling test for the data in Exercise 18.8?
Rerun the analysis in Exercise 18.8 using the normal approximation.
It has been argued that first-born children tend to be more independent than later-born children.Suppose we develop a 25-point scale of independence and rate each of 20 first-born children and their second-born siblings using our scale. We do this when both siblings are adults, thus eliminating
How would you go about applying a resampling procedure to test the difference between Before and After scores in Exercise 18.6?
Refer to Exercise 18.5.a. Repeat the analysis using the normal approximation.b. How well do the two answers (18.5a and 18.6a) agree? Why do they not agree exactly?
Nurcombe and Fitzhenry-Coor (1979) have argued that training in diagnostic techniques should lead a clinician to generate (and test) more hypotheses in coming to a decision about a case. Suppose we take 10 psychiatric residents who are just beginning their residency and ask them to watch a
Repeat the analysis in Exercise 18.2 using the appropriate one-tailed test.
Repeat the analysis in Exercise 18.2 using the normal approximation.
Kapp, Frysinger, Gallagher, and Hazelton (1979) have demonstrated that lesions in the amygdala can reduce certain responses commonly associated with fear (e.g., decreases in heart rate). If fear is really reduced, then it should be more difficult to train an avoidance response in lesioned animals
McConaughy (1980) has argued that younger children organize stories in terms of simple descriptive (“and then . . .”) models, whereas older children incorporate causal statements and social inferences. Suppose that we asked two groups of children differing in age to summarize a story they just
Why might it not make much sense to examine the results for heterogeneity of effects?
Compute confidence limits on the mean risk ratio and draw the appropriate conclusions.
For the Bloch et al. study what would you conclude about the risk of increasing tic behavior using methylphenidate?
Create a forest plot for the data in Bloch’s study.
Why would it not make sense to try to treat this meta-analysis as a random model?
Calculate the mean effect size estimate and its confidence limits for the data in this table.
Recompute the mean effect size and its confidence limits assuming that the underlying model is fixed.
Test the hypothesis that there are no differences between the two subgroups in this table.
Calculate confidence limits on the mean effect size computed in the previous exercise.
Assuming a random model, calculate a mean effect size and its standard error.
Create a forest plot of these data.
Make up or find an example with respect to Exercise 16.25 where the slope is not nearly 1.0.Analyze it using both the analysis of covariance and a t test on difference scores. Do either of these analyses make sense?
I said that in any experiment where we have pretest and posttest scores we could either look at the difference scores (compared across groups) or use the pretest as a covariate. These two analyses will be equivalent only when the slope relating posttest to pretest scores is 1.00. How likely do you
I initially thought of creating an analysis of variance example from the example in Chapter 14, Section 14.7. I could have used Sex and Group as the independent variables, posttest scores as the dependent variable, and pretest scores as the covariate (ignoring FU6 and FU12 entirely). This would
Write up the results of Everitt’s experiment, including effect sizes.
Everitt reported data on a study of three treatments for anorexia in young girls. One treatment was cognitive behavior therapy, a second was a control condition with no therapy, and a third was a family therapy condition. These are the same data we examined in Chapter 14.The data follow and are
In Exercise 16.22 we used YearColl as the covariate. Run an analysis of variance on Year-Coll, using Gender and Group as the independent variables. What does this tell us that is relevant to the preceding analysis of covariance?
Using the same data as in Exercise 16.21, run an analysis of covariance instead, using year in college (YearColl) as the covariate.a. Why would we want to consider YearColl as a covariate?b. How would you interpret the results?
Use the data from Mireault and Bond (1992) in the file named Mireault.dat referred to in Exercise 7.6 to run a two-way analysis of variance on the Global Symptom Index T score (GSIT) using Gender and Group as independent variables. Plot out the cell means and interpret the results.
Use the data set named in Epinuneq.dat on the Web site to examine the results of the study by Introini-Collison and McGaugh (1986) described prior to Exercises 11.29–11.31. Using any statistical package, run a two-way analysis of variance with unequal sample sizes. What would you conclude from
Klemchuk, Bond, and Howell (1990) examined role taking in children. Children were administered a battery of role-taking tasks. They were classified as being in daycare or not being in daycare, and as ages 2–3 or ages 4–5. The hypothesis was that children with daycare experience would perform
Compute the energy savings per household for the data in Exercise 16.16 by subtracting this year’s bill from last year’s bill. Then run an analysis of variance on the savings scores and compare that to the analysis of covariance.
Compute the adjusted means for the data in Exercise 16.16.
To refine the experiment described in Exercise 16.15, a psychologist added an additional set of households to each group. This group had a special meter installed to show them exactly how fast their electric bill was increasing. (The amount-to-date was displayed on the meter.)The data follow; the
In studying the energy consumption of families, we have broken them into three groups.Group 1 consists of those who have enrolled in a time-of-day electrical-rate system (the charge per kilowatt-hour of electricity is higher during peak demand times of the day).Group 2 is made up of those who
If you have access to SAS, use that program to analyze the data in Exercise 16.7. Add /SS1 SS2 SS3 SS4 to the end of your Model command and show thata. Type I sums of squares adjust each term in the model only for those that come earlier in the model statement.b. Type II sums of squares adjust main
Draw a Venn diagram representing the sums of squares in Exercise 16.7.
Draw a Venn diagram representing the sums of squares in Exercise 16.5.
Using the following data, demonstrate that Method III (the method advocated in this chapter)really deals with unweighted means.B1 B2 A1 5 11 3 9 14 611 9A2 10 6 11 2 12 7
For the data in Exercise 16.7, the complete model is 1.2306A1 2 3.7167B1 2 0.3500B2 1 0.4778AB11 2 0.5444AB12 1 13.6750a. Show that this model reproduces the treatment and interaction effects as calculated in Table 16.3.
For the data in Exercise 16.5, the complete model is 1.1667A1 2 3.1667B1 2 0.1667B2 1 0.8333AB11 2 0.1667AB12 1 13.4167a. Show that this model reproduces the treatment and interaction effects as calculated by the method shown in Table 16.2.
Using only the SES predictors for the data in Exercise 16.7, we find SSreg1b2 5 379.3325.Why is this not the same as SSSES in Exercise 16.7?
When we take the data in Exercise 16.5 and delete the last two low-SES males, the last three average-SES males, and the last two high-SES females, we obtain the following sums of squares:Compute the analysis of variance using these sums of squares.
Using the SES portion of the design matrix as our predictor, we find that SSreg1b2 5 338.6667.a. Why is this value the same as SSSES in the answer to Exercise 16.5?b. Will this be the case in all analyses of variance?
A psychologist was concerned with the relationship between Gender, Socioeconomic Status(SES), and perceived Locus of Control. She took eight adults (age 5 25 to 30 years) in each Gender–SES combination and administered a scale dealing with Locus of Control (a high score indicates that the
Rerun the analysis of Exercise 16.2 for the amended data from Exercise 16.3.
Taking the data from Exercise 16.1, add the scores 5 and 8 to the Average group and the scores 2, 3, 3, and 5 to the Good group. Rerun the analysis for Exercise 16.1 using the more complete data.
For the data in Exercise 16.1,a. Calculate treatment effects and show that the regression model reproduces these treatment effects.b. Demonstrate that R2 for the regression model is equal to h2 for the analysis of variance.
The following hypothetical data were obtained from poor, average, and good readers on the number of eye fixations per line of text.Poor Average Good 10 5 3 7 8 5 8 4 2 11 6 3 5 5 4a. Construct the design matrix for these data.b. Use any standard regression program to calculate a least-squares
In this chapter we spent a lot of time with Guber’s study of educational expenditures and found that when we controlled for the percentage of students taking the SAT exam, Expend was not a significant predictor. However, the SAT is not a good dependent variable in discussing the quality of
Feinberg & Willer (2011) studied the effect of just-world beliefs on people’s willingness to accept the concept of global warming. They presented participants with material designed to prime them toward or away from just-world beliefs, and then had them fill out a questionnaire concerning their
Paul Jose has a Web site referred to in the section on mediation. He discusses a problem in which he believes that stress leads to depression through a mediating path via rumination.(In other words, some stressed people ruminate, and as a consequence they become depressed.)The path diagram derived
As you know, the regression coefficient gives the effect of one variable holding all other variables constant. How would you view this interpretation when you have an interaction term in your model?
Using the data you created in Exercise 15.37, demonstrate the effect of “centering” your predictor variables.
Make up a very simple example with very simple variables to illustrate how one could see the effect of an interaction between two predictors.
What are some of the reasons why stepwise regression (broadly defined) would not find favor with most statisticians?
In Exercise 15.34 we had a data set where BlamBeh was related to later distress at time 2.When it is included as a predictor along with Stress1 and BlamPer it is no longer a significant predictor. Why would this be likely to happen?
Malcarne, Compas, Epping, and Howell (1995) examined 126 cancer patients soon after they were diagnosed with cancer and at a four-month follow-up. At the initial interviews(Time 1) they collected data on the patients’ current levels of distress (Distress1), the degree to which they attributed the
I was surprised to see that frequency of the behavior was not related to the likelihood of reporting. Can you suggest reasons why this might be so?
Repeat Exercise 15.31 but this time use just the dichotomous predictor Marital Status. Create a contingency table of Married/Unmarried by Report/No Report, calculate odds ratios, and compare those ratios to the results of the logistic regression. (The result will not be significant, but that is not
The data set Harass.dat contains slightly modified data on 343 cases created to replicate the results of a study of sexual harassment by Brooks and Perot (1991). The dependent variable is whether or not the subjects reported incidents of sexual harassment, and the independent variables are, in
It is useful to examine the effects of measurement reliability on the outcome of a regression problem. In Exercise 15.24 the variable PVLoss was actually a reasonably reliable variable.However, for purposes of illustration we can manufacture a new, and less reliable, measure from it by adding a bit
Repeat the analysis of Exercise 15.24, requesting statistics on regression diagnostics.a. What, if anything, do these statistics tell you about the data set?b. Delete the subject with the largest measure of influence (usually indexed by Cook’s D).What effect does that have for this particular
Notice that in the diagram in Exercise 15.27 SuppTotl has both a direct and an indirect effect on Depression. Its direct effect is the arrow that goes from SuppTotl to DepressT. The indirect effect (which here is not significant) comes from the fact that SuppTotl influences PVLoss, which in turn
Draw one diagram to illustrate the relationships examined in Exercises 15.24 and 15.26.Use arrows to show predicted relationships, and write the standardized regression coefficients next to the arrows. (You have just run a simple path analysis.)
In Exercise 15.24 we posited a model in which depression was a function of perceived vulnerability, social support, and age at loss. An alternative, or additional, view might be that vulnerability itself is a function of social support and age at loss. (If you lost a parent when you were very young
A compulsive researcher who wants to cover all possibilities might throw in the total score on perceived vulnerability (PVTotal) as well as PVLoss. (The total includes vulnerability to accidents, illness, and life-style related problems.)a. Run this analysis adding PVTotal to the variables used in
Use the data set Mireault.dat from Mireault (1990), described in the Appendix and found on the Web site for this book, to examine the relationship between current levels of depression and other variables. A reasonable model might propose that depression (DepressT) is a function of (1) the
Produce a set of data where the variance of Y values associated with large values of X is greater than the variance of Y values associated with small values of X. Then run the regression and plot the residuals on the ordinate against X on the abscissa. What pattern emerges?
Create a set of data on 10 cases that illustrates leverage, distance, and influence. Use any standard regression program to produce statistics measuring these attributes.
For the data in Exercise 15.16, would it be safe to conclude that decreasing the number of mothers who fail to seek medical care before the third trimester is a good way to decrease the incidence of low-birthweight infants?
For the data in Exercise 15.16, compute Y^5 1X2 1 1X4 2 3X5. How well does this equation fit compared with the optimal equation? Why should this be the case?
In Exercise 15.16 the fifth predictor has a very low correlation with the criterion (r 5 .05)and yet plays a significant role in the regression. Why?
In Exercise 15.16 the adjusted R2 would actually be lower for five predictors than for three predictors. Why?
In Exercise 15.16 what meaning attaches to R* as far as the Vermont Department of Health is concerned?
The State of Vermont is divided into 10 Health Planning Districts—they correspond roughly to counties. The following data represent the percentage of live births of babies weighing under 2500 grams (Y), the fertility rate for females 17 years of age or younger (X1), total high-risk fertility rate
Refer to the first three variables in Exercise 15.4.a. Draw a figure comparable to Figure 15.1.b. Obtain the regression solution for these data and relate the solution to the figure.
Refer to the first three variables from Exercise 15.4.a. Use any computer program to calculate the squared semipartial correlation and the squared partial correlation for Satisfaction as the criterion and No. Supervised as the predictor, partialling out Responsibility.b. Draw a Venn diagram to
Calculate the adjusted R2 for the 15 cases in Exercise 15.12.
Using the following (random) data, demonstrate what happens to the multiple correlation when you drop cases from the data set (e.g., use 15 cases, then 10, 6, 5, 4).Y 5 0 5 9 4 8 3 7 0 4 7 1 4 7 9 X1 3 8 1 5 8 2 4 7 9 1 3 5 6 8 9 X2 7 6 4 3 1 9 7 5 3 1 8 6 0 3 7 X3 1 7 4 1 8 8 6 8 3 6 1 9 7 7 7 X4

Showing 2400 - 2500 of 4976

First
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
Last

Statistical Methods For Psychology 8th Edition David C. Howell - Solutions

Step by Step Answers