Statistics And Data Analysis For Nursing Research 2nd Edition Denise Polit - Solutions

Discover the essential resource for mastering "Statistics And Data Analysis For Nursing Research, 2nd Edition" by Denise Polit. This comprehensive guide offers online access to a robust answers key and detailed solutions manual, ensuring that you have all the answers at your fingertips. With solutions in PDF format, you can easily navigate through solved problems and explore questions and answers thoroughly. The test bank and chapter solutions provide step-by-step answers that enhance your understanding. Whether you're an instructor seeking a manual or a student looking for a textbook, this free download ensures you have the tools needed for success.

1.23 As they leave a popular grocery store, 73 people are stopped and the number of fruit and vegetable items they purchased is counted.a. What is the size of the sample?b. Identify the population.c. If this number of items counted is used to estimate the diets of all shoppers, is it a descriptive
1.22 A researcher studies the average distance that 130 people living in urban areas in India walk each week.a. What is the size of the sample?b. Identify the population.c. Is this "average" a descriptive statistic or an inferential statistic if it is used to describe the 130 people studied?d. How
1.21 Over the course of 1 week, a grocery store randomly selected 100 customers to complete a survey about their favorite products. Identify the sample and population for this example.
1.20 University bookstore employees asked 225 students to complete a customer satisfaction survey after these customers bought their books. The bookstore manager wanted to find ways to improve the customer experience. Identify the sample and population for this example.
1.19 A researcher studies the number of hours 2500 Canadians work out every week. Identify the sample and the population for this example. 1.20 University bookstore employees asked 225 students to complete a customer satisfaction survey after these customers bought their books. The bookstore
1.17 What is preregistration and why is it important? 1.18 What is HARKing and how can preregistration help to eliminate this practice?
1.16 Describe the kinds of unethical practices that researchers might be concerned with.
1.15 What do researchers mean by data ethics?
1.14 The following statements are wrong but can be corrected by substituting one word or phrase. (See the instructions in Exercise 1.13.) Identify the incorrect word or phrase in each of the following statements and supply the correct word.a. A researcher examined the effect of the ordinal variable
1.13 In statistics, it is important to pay close attention to language. The following statements are wrong but can be corrected by substituting one word or phrase. For example, the sentence "Only correlational studies can tell us something about causality" could be corrected by changing
1.12 What is the difference between a between-groups research design and a within-groups research design?
1.11 What is the difference between experimental research and correlational research?
1.10 In your own words, define the word experiment-first, as you would use it in everyday conversation and then, as a researcher would use it.
1.9 To test a hypothesis, we need operational definitions of the independent and dependent variables. What is an operational definition?
1.8 What is the difference between reliability and validity, and how are the two concepts related?
1.7 What are confounding variables (or simply confounds), and how are they controlled using random assignment?
1.6 What is the relation between an independent variable and a dependent variable?
1.5 Distinguish between discrete variables and continuous variables.
1.4 Describe two ways that statisticians might use the word scale.
1.3 Identify and define the four types of variables that researchers use to quantify their observations.
1.2 What is the difference between a sample and a population?
1.1 What is the difference between descriptive statistics and inferential statistics?
1.8 What is the difference between reliability and validity, and how are the two concepts related?
1.7 What are confounding variables (or simply confounds), and how are they controlled using random assignment?
1.6 What is the relation between an independent variable and a dependent variable?
1.5 Distinguish between discrete variables and continuous variables.
1.4 Describe two ways that statisticians might use the word scale.
1.3 Identify and define the four types of variables that researchers use to quantify their observations.
1.2 What is the difference between a sample and a population?
1.1 What is the difference between descriptive statistics and inferential statistics?
B11. If you have access to the Missing Values Analysis module in SPSS, impute the missing values for cesd using expectation maximization. Compare the means and SDs obtained through regression and EM.
B10. Using the answer to Exercise B9 (g), impute missing CES-D scores. First, create a new CES-D variable so that any possible mistakes do not affect the original scores.With the Compute Variable command, set the new variable(e.g., newcesd) equal to the original, cesd. Then, do another Compute
B9. With only 3.8% missing values on the CES-D scale in a very large sample, we might well use listwise deletion for any further substantive analyses with the cesd variable. As an exercise, however, we will impute missing values on the CES-D total scale using regression analysis. We will restrict
B8. If you have access to the SPSS Missing Values Analyses, run the MVA with the same set of variables and use Little’s MCAR test to see if you reach different conclusions based on the results of that test than you did based on results from Exercises B6 and B7.
B7. We will continue to test whether missingness on the CES-D scale is related to mothers’ characteristics, this time with chi-square tests to test differences in proportions on categorical variables. Select Analyze ➜ Descriptive Statistics➜ Crosstabulations. Use cesdstat as the column
B6. Next, we will run a series of statistical tests to see if missingness is related to other characteristics of the women in this sample, beginning with t tests. Select Analyze ➜Compare Means ➜ Independent Samples T Test. Move the following quantitative variables into the Test Variable
B5. We will next explore patterns of missingness for the CES-D scale score. First, create a missing values indicator variable, which we will call cesdstat. Select Transform ➜ Compute Variable, and enter cesdstat into the slot for Target Variable.Set the new variable equal to 1 by entering the
B4. The Polit2SetC dataset includes the variable cesd, which is a total CES-D score for cases with nonmissing values, plus cases for whom missing values were imputed using case mean substitution. (The imputation was done only for cases that had no more than five missing values.) Looking at the
B3. In the situation such as the one we have with missing CES-D items, we would recommend case mean substitution for cases with five or fewer missing values, but achieving this through SPSS commands is tedious, particularly for scales with 20 items such as the CES-D. With small samples, it is
B2. Now we will look at how many missing values individual participants had for these 20 CES-D items. To do this, we need to create a new variable (we will call it misscesd) that is a count of how many values are missing for each person. Select Transform ➜ Count Occurrences of Values Within
B1. For these exercises, you will be using the SPSS dataset Polit2SetC. We will begin by looking at individual responses to items on the Center for Epidemiologic Studies—Depression (CES-D) Scale. For a description of these 20 items, refer to Exercise B1 in the chapter "Factor Analysis and
A5. Look at a recent issue of a nursing research journal, such as Nursing Research, Journal of Advanced Nursing, or Research in Nursing & Health. How many of the quantitative studies had any reference to missing values? Did any article discuss patterns and amounts of missingness? What methods were
A4. Manually impute the missing child weight values, using the regression equation presented in the text, for children with the following values on the predictor variables:
A3. Using the guideline we suggested in a Tip, would it be advisable to impute missing values for two items on the Parenting Stress subscale?
A2. For the five items in Exercise A.1 with missing values, the item means for all cases for whom data were available are as follows:Write a brief statement about whether you think that mean substitution or case mean substitution would be preferred for the five cases with a missing value, based on
A1. The following table shows actual data values on the seven items used to create the Parenting Stress scale in our factor analysis of 11 parenting items for five mothers. Each case has a missing value, coded 99. Impute values to replace the missing values, using case mean substitution.
B5, creating a table appropriate for a research report.Remember to include some basic information about factorability from Exercise B3.
B5. In this exercise, run a reliability analysis for each of the two factors from the previous exercise. In the first reliability analysis, use all 16 of the negatively worded items that had loadings greater than .30 on Factor I in Exercise B4. Then, do a second analysis for the four items that had
B4. Now you will undertake a factor analysis of the CES-D items using principal axis factoring, two factors, and oblique rotation. Proceed with the same set of variables as in Exercise B3. You can remove some of the Descriptive options (e.g., no further need for the KMO test or inspection of the
B3. In this exercise, you will undertake a principal components analysis of the CES-D items, using all 20 original items(no reversed items). Go to Analyze ➜ Data Reduction ➜Factor Analysis. Select the 20 CES-D items, being careful not to select reversed items, and move them into the box for
B2. Before performing a factor analysis, do a reliability analysis for the entire 20-item scale. Click Analyze ➜ Scale ➜Reliability Analysis. Move the 16 negatively worded CESD items and the four reverse-coded items into field for items.Click the Statistics pushbutton and in the next dialog
B1. For these exercises, you will be using the SPSS dataset Polit2SetC. This file contains responses to individual items on the Center for Epidemiologic Studies—Depression (CES-D) Scale. This scale presents respondents with 20 statements about their mood and feelings.They are asked to rate each
A6. Comment on the researchers’ decisions in the research example at the end of this chapter (Cˇrncˇ ec et al., 2008). What, if anything, would you recommend doing differently?
A5. Suppose that a seventh test (Test G) was added to the factor analysis graphed in Figure 5. This test has the following coordinates on the unrotated axes: Y (.40), X (.45).Plot this test on graph (B) of this figure. What would the coordinates be on the new (rotated) reference axes? Is it more
A4. In a PCA of eight items, assume initial eigenvalues were:2.83, 2.08, 1.09, .80, .42, .36, .22 , .20. Graph these values on a scree plot. How many factors do you think should be extracted and rotated?
A3. In Table 4, what are the eigenvalues for Factors I through III?
A1. Using information from Figure 4 (PCA communalities)and Figure 7 (factor loadings for three factors), compute the absolute values of the loadings for items 7 and 8 on Factor II.A2. With regard to Figure 2, indicate the equation for Component 1 that yielded the percent of variance (26.86).
B5. Select another variable in the Polit2SetB dataset that you might hypothesize as a predictor of good health in this population of women. Run another logistic regression, entering the new predictor in a second block (use the Next button on the opening dialog box to get a new screen for a second
B4. In this next exercise, we will again use five independent variables to predict the probability of good health (health), but instead of using bmi as a continuous variable, we will use the variable bmicat, which classifies the women based on BMI values as normal weight, overweight, obese, or
B3. In this next exercise, we will use five predictors to predict the probability of good health (health) in a standard logistic regression: The predictors include smoking status (smoker)and four additional predictors, which include the woman’s age (age), whether or not she is currently
B2. In this exercise, use the Logistic regression program in SPSS rather than Crosstabs to look at the bivariate relationship between health and smoker. In the Analyze ➜Regression ➜ Binary Logistic dialog box, move health into the Dependent slot and move smoker into the Covariate slot. Click
B1. For these exercises, you will be using the SPSS dataset Polit2SetB. The analyses will focus on predicting the probability that a woman is in good-to-excellent health versus fair-to-poor health, which is coded 1 versus 0, respectively, on the variable health. Begin by looking at results for the
A4. In the example at the end of the chapter (the study by Kanak et al.), could the researchers have used a continuous variable for number of units? Why do you think they used a four-category variable to represent this independent variable? Is there evidence in Table 4 that the linearity assumption
A3. Based on either your clinical knowledge or on a brief literature search, what variable would you recommend adding to improve the prediction of a woman’s decision to have a tubal ligation? How would you measure or construct that variable?
A2. What is the logistic regression equation for predicting the probability of having a tubal ligation, based on information shown in Figure 6?
A1. In Table 2, students were classified as completing versus not completing a graduate program based on a cutpoint of.50 in the estimated probabilities. What percentage of cases would be correctly classified if the cutpoint were.40? What types of misclassifications would this cutpoint produce?
B6. Using output from one of the previous three exercises(B3 through B5), create a table to summarize key results of the analyses. Then write a paragraph summarizing the findings.
B5. In this exercise, we will test racial/ethnic differences in depression over time, using CES-D scores from the two waves of interviews with a subsample of these women. You will need to begin by excluding women in the white/other group because there were too few of them in this small ?
B4. Re-run the analysis in exercise B3 as a MANCOVA by selecting a covariate from the data set. Was the covariate a significant predictor of the SF-12 scores? Did including the covariate in the analysis alter the relationship between racethn and SF-12 scores?
B3. For this exercise, you will use MANOVA to test the hypothesis that there are racial/ethnic differences in scores on the SF-12, using scores from both the physical health component (sf12phys) and mental health component(sf12ment) as the dependent variables and racethn as the independent
B2. Now we can proceed with the ANCOVA analysis described in Exercise B1. Open the GLM Univariate dialog box again, which should already have the necessary variable information (unless you run Exercise B1 and B2 on different days).
B1. You will be using the SPSS dataset Polit2SetC to do various analyses. For the first analysis (ANCOVA), you will be testing for racial/ethnic differences (racethn) in physical health scores (sf12phys), controlling for total household income (income). Begin by testing the assumption of
A5. Using data from Table 18, make a graph displaying group differences over time for one of the outcomes in the Kim and Song study.
A4. Following are some means from a randomized controlled trial. Indicate at least two ways to analyze the data to test for treatment effects.
A3. Use the following information to compute unadjusted and adjusted group means on patient satisfaction scale scores:Grand Mean 20.521
A2. Suppose you were interested in studying the effect of a person’s early retirement (at or below age 62 versus at age 65 or later) on indicators of physical and emotional health.What variables would you suggest using
A1. Indicate which statistical procedure discussed in this chapter would most likely be used in the following circumstances:(a) Independent variables: age, length of time in nursing home, gender, marital status, number of kin living in 25-mile radius; dependent variables: functional ability,
B7. Using output from one of the previous four exercises (B3 through B6), create a table to summarize key results of the analyses. Then write a paragraph summarizing the findings.
B6. It is tempting to think of the results obtained in the previous three analyses as suggesting a causal link between the women’s abuse experiences and their level of depression—that is, inferring that being abused caused higher levels of depression. However, the opposite might be the case.
B5. In this exercise, run a hierarchical multiple regression analysis to predict cesd, using the same predictors as in Exercises B3 and B4. Select the variables you would like to enter in each step, being sure to think of a reasonable rationale for the order of entry. In the first SPSS Linear
B4. In this exercise, run a stepwise multiple regression to predict depression scores, using the same predictors as in Exercise B3. In the first SPSS Linear Regression dialog box, enter cesd as the Dependent variable and the list of predictors in the Independent slot, as in the previous exercise.
B3. In this exercise, you will run a simultaneous multiple regression analysis to predict the women’s level of depression(scores on the CESD depression scale, cesd) based on several demographic characteristics, socioeconomic characteristics, health status, and self-reported incidence of abuse in
B2. Now you can create the new dummy-coded variables. Select Transform ➞ Recode ➞ Into Different Variables. Find racethn in the variable list and move it into the slot for“Numeric Variable Output Variable.” On the right, under Output Variable, type in a name for your first new
B1. For these exercises, you will be using the SPSS dataset Polit2SetC to do multiple regression analyses to predict level of depression in the sample of low-income urban women.You will need to begin by dummy coding the variable race/ethnicity. First, create a frequency distribution for racethn
A7. For the following situations, estimate how large a sample would be needed for a multiple regression analysis to achieve standard statistical criteria, using Table 7.(a) Estimated R2 .20, k 6(b) Estimated R2 .13, k 8(c) Estimated R2 .08, k 4
A6. Using the Internet resource recommended in this chapter(or another similar online calculator), find the 95% confidence limits of R2 for the following situations:(a) R2 .22, k 6, N 100(b) R2 .22, k 6, N 200(c) R2 .22, k 10, N 100
A5. Suppose that, using dummy codes, smokers were coded 1 and nonsmokers were coded 0 on SMOKSTAT, and that males were coded 1 and females were coded 0 on GENDER. What would be the 4 codes for the interaction term?
A4. Following is a correlation matrix:(a) If DVAR were regressed on VARA, VARB, and VARC, what is the lowest possible value of R2 ?(b) In a stepwise regression, what would be the first predictor variable into the equation?(c) In a stepwise regression, what would be the second predictor variable
A3. Which, if any, of the tests described in Exercise A2 would be statistically significant with a .001?
A2. Using the following information for R2, k, and N, calculate the value of the F statistic for testing the overall regression equation and determine whether the F is statistically significant at the .05 level:(a) R2 .53, k 5, N 120(b) R2 .53, k 5, N 30(c) R2 .28, k 4, N 64(d) R2 .14, k 4, N 64
A1. Using the regression equation for predicting graduate GPA presented in this chapter, compute the following for the last two students in Table 1: (a) the predicted value of Y;and (b) the squared error term.
B6. Run Correlations for the following variables in the Polit2SetB dataset (note that this is a different file than for the previous exercises): the woman’s age (age); number of children living in the household (kidshh); number of ER visits in the past 12 months (ervisit); number of doctor visits
B5. Run a simple regression between family income (income), which we will use as the Y or dependent variable, and number of hours worked (workweek), which we will use as the X or predictor variable. You can do this through Analyze ➜ Regression ➜ Linear. Move income into the slot for Dependent
B4. In this exercise you will be running correlations between a variable measuring the women’s overall satisfaction with their material well-being (satovrl) and a variable measuring hunger and food insecurity (foodscor). Begin, however, by examining how scores on the variables foodscor and
B3. Examine the scatterplot for the relationship between workweek and income through the Graphs ➜ Legacy Dialogs ➜ Scatter/Dot ➜ Simple commands. Insert the variable workweek as the X axis variable and income as the Y axis variable. Run the analysis and answer the following questions: (a)
B2. In this exercise, run the exact same analysis as in Exercise B1, except select the option Exclude cases listwise as the Missing Values option. Then answer the following questions: (a) How many cases were in this analysis? Why do you think so few women were included in this analysis—for
B1. For these exercises, you will be using the SPSS dataset Polit2SetA. Begin by computing a correlation matrix for four variables using Analyze ➜ Correlate ➜ Bivariate.Move the following four variables into the slot for Variables: the woman’s age at first birth (age1bir), highest grade
A8. Assuming in question A7 that .19 is a good estimation of the population correlation, what sample size would be needed in a replication study to achieve power .80 at a .05?
A7. A researcher studying the relationship between maternal age and length of breastfeeding in a sample of 75 primiparas found a correlation of .19, which was not statistically significant at the .05 level. What was the estimated power of the statistical test? Conversely, what was the risk that a
A6. Using the regression equation calculated in response to question 5, compute the predicted value of Y (length of hospital stay) for patients with the following functional ability scores:(a) X 52(b) X 68(c) X 23(d) X 10

Showing 2400 - 2500 of 5165

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

Statistics And Data Analysis For Nursing Research 2nd Edition Denise Polit - Solutions

Step by Step Answers