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essentials of statistics

Questions and Answers of Essentials Of Statistics

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
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,
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
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
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 ➜
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
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.
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
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
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.
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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,
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
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
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
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
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
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
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
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
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
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,
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,
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,
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
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
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
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
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)
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,
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
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
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
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
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
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.
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
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
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
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
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
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
A5. Suppose that a researcher regressed surgical patients’length of stay in hospital (Y ) on a scale of functional ability measured 24 hours after surgery (X). Given the following, solve for the
A4. In a random sample of 100 people, the correlation between amount of daily exercise and weight was found to be.21. What would be the likely effect on the absolute value of the correlation
A3. For each coefficient of determination below, calculate the value of the correlation coefficient:(a) r 2 .66(b) r 2 .13(c) r 2 .29(d) r 2 .07
A2. For each correlation coefficient below, calculate what proportion of variance is shared by the two correlated variables:(a) r .76(b) r .33(c) r .91(d) r .14
B6. Run Crosstabs for four outcomes in the Polit2SetC dataset.(Note that this is a different file than for the previous exercises.) The four outcomes are responses to a series of questions about
B5. In this analysis, you will be running a Kruskal-Wallis test, comparing women in the four BMI categories (bmicat)with regard to the number of miscarriages they had ever had (miscarr). Number of
B4. For this exercise, you will be running a Mann-Whitney U test, comparing smokers and nonsmokers on an ordinallevel variable, drunk. This variable measures how frequently in the prior month the
B3. In this exercise, we will be testing the null hypothesis that smoking status (smoker) is unrelated to having a health limitation. Because we want “cell a” to be the cell with the risk factor
B2. Within SPSS, it is possible to introduce a third categorical variable into a Crosstabs analysis, to see if the relationship between two variables is consistent for different levels of a third
B1. For Exercises B1 to B5, you will be using the SPSS dataset Polit2SetB. Begin by running a crosstabs (Analyze ➜Descriptive Statistics ➜ Crosstabs) for the variables bmicat and hlthlimit. The
A7. Using the information provided, indicate which test you think should be used for each of the following situations:(a) Independent variable: normal birthweight versus low birthweight infants;
A6. Match each of the nonparametric tests in Column A with its parametric counterpart in Column B:
A5. Assume that a researcher has conducted a pilot intervention study and wants to use the pilot results to estimate the number of participants needed in a full-scale study to achieve a power of .80
A4. Given each of the following situations, determine whether the calculated values of chi-square are statistically significant:(a) x2 3.72, df 1, a .05(b) x2 9.59, df 4, a .05(c) x2 10.67, df 3, a
A3. Using the statistical information from the first two exercises, write a paragraph summarizing the results of the analyses.

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