Discussion Questions for Kerlinger and Lee (2000)

Kerlinger, F. N., & Lee, H. B. (2000). Foundations of Behavioral Research. 4th Edition, Wadsworth / Thomson Learning. ISBN: 0-15-507897-6

Chapter 1 Science and the Scientific Approach (p. 3)
Reading: Schroeder et al. (1985) (handout)
Chapter 2 Problems and Hypotheses (p. 23)
Chapter 3 Constructs, Variables, and Definitions (p. 41)

Chapter 11 Statistics: Purpose, Approach, Method (REVIEW)  (p. 257)
Chapter 26 Foundations of Measurement (REVIEW) (p. 623)
Reading: Calder et al. (1981) (handout)
Chapter 5 Relations (REVIEW) (p. 81)
Chapter 6 Variance and Covariance (p. 103)
Chapter 8 Sampling and Randomness (p. 163)    
Chapter 9 Principles of Analysis and Interpretation (REVIEW)  (p. 191)
Chapter 10 The Analysis of Frequencies (p. 221)                                                                          
Chapter 12 Testing Hypotheses and the Standard Error (REVIEW) (p. 275)
Chapter 13 ANOVA: Foundation (REVIEW) (p. 307)
Chapter 14 Factorial ANOVA (p. 345)

Chapter 17 Ethical Considerations in Conducting Behavioral Science Research (p.437)                                 

Chapter 18 Research Design: Purpose and Principles (p. 449)                                  
Chapter 19 Inadequate Designs & Design Criteria (p. 465)
Chapter 20 General Designs of Research (p. 481)
Chapter 21 Research Design Applications: Randomized Grps & Corr Grps (p. 501)
Chapter 32 Multiple Regression (p. 755)
   
 


 Chapter 1 Science and the Scientific Approach (p. 3)

  1. How does commonsense impede the progress of science?
  2. Which of Charles Peirce's "paths to understanding" would religion rely upon?
  3. How does a hypothesis differ from a theory?
  4. How are inductive and deductive reasoning related to hypotheses and theory?
  5. Why is theory the basic aim of science?
  6. How does science differ from technology and how are they interdependent?
  7. What is a research question you would like to explore? Describe how you think the conceptual variables are related.

Chapter 2 Problems and Hypotheses (p. 23)

  1. Write a research problem and hypothesis for each of the following variables: (1) anxiety, (2) motivation to study, (3) stress at home, (4) happiness
  2. How well do your research problems in #1 above meet the three criteria for problem statements?
  3. Are Freudian concepts appropriate for scientific inquiry?
  4. Formulate a hypothesis derived from Saal & Moore's (1993) findings.
  5. How would you test the hypothesis that cigarette smoking impairs cognitive performance?
  6. How would you test the hypothesis that an increase in task interest will change performance?
  7. How can motivation at work be construed as a multivariate problem for study?
  8. How can test anxiety be construed as a multivariate problem for study?

Chapter 3 Constructs, Variables, and Definitions (p. 41)

  1. What is the difference between a concept and construct? Give two psychological examples.
  2. How does a constitutive definition differ from an operational definition? Give an example.
  3. Provide an operational definition for delinquency that is binary and one that is polytomous.
  4. How does a true independent variable (IV) differ from a subject variable? Give an example of each.
  5. How can age be defined operationally as a trichotomous or a linear variable?
  6. Give an example of how gender can be used as either a true IV or subject variable.
  7. What is an intervening variable that could influence the relationship between psychotherapy and reduction in anxiety?
  8. How could anxiety be conceptualized as both an independent and dependent variable?

Chapter 11 Statistics: Purpose, Approach, Method (REVIEW)  (p. 257)

  1. What it the one major purpose of statistics? Give an example using your research question.
  2. Define sample variance and population variance in words. How do they differ?
  3. What is the law of large numbers and what implication does if have for research design?
  4. What is the difference between a bivariate distribution and a frequency distribution?
  5. How is the central limit theorem  important for inferential statistics?
  6. What is a standard error? Give examples of two two.
  7. Distinguish between a parameter and statistics and give an example of each from your research project.

Chapter 26 Foundations of Measurement (REVIEW) (p. 623)

  1. What is the first step in measurement?
  2. Give examples of two variables from your project that could be measured at the ordinal level.
  3. What are some advantages and disadvantages in reducing linear variables to the nominal levels for use in an experimental design?
  4. Give an example of two indicants of properties you will use in your project.
  5. If a music CD is awarded a five star rating, what level of measurement is being used? 
  6. At what level of measurement are Likert-type rating scales?
  7. If a construct could be measured using either an ordinal or ratio scale, is an advantage of using the ratio scale. Give an example.
  8. Think of two nominal variables with at least three categories that could reflect a construct with an underlying continuum.


Reading: Calder et al. (1981) (handout)
Chapter 5 Relations (REVIEW) (p. 81)

  1. Give an example of one set of pairs from your lab 1.
  2. What is meant by "ordered pairs" and why is it important?
  3. Give an example of the domain and range for an ordered set of pairs from your lab1.
  4. What is a function and why is it a special kind of relation?
  5. What two things does the correlation coefficient tell us?
  6. Write in narrative form the computational formula for the product-moment correlation (not in this chapter)
  7. Contrast and compare bivariate correlation and regression.
  8. Contrast and compare multivariate correlation and regression.
  9. What does multivariate regression provide that bivariate regression does not? 


Chapter 6 Variance and Covariance (p. 103)

  1. What is a sampling unit?
  2. Define variance and standard deviation
  3. How can sample variance be used to estimate population variance?
  4. What is the purpose of squaring a deviation score?
  5. What is the difference between systematic variance and error variance?
  6. Define error variance.
  7. What was the IV in one of the experimental studies used to illustrate between group variance? What does the X represent in this case?
  8. What variance component is left over after between group variance is removed from the total variance?
  9. What it the difference between correlations and covariances?


Chapter 8 Sampling and Randomness (p. 163)  

  1. Is it more important to external or interval validity for  a sample to be representative? Why? 
  2. Describe how randomization works with sample selection and assignment of Ss to conditions.
  3. What is the relationship between sample size and error, i.e. deviation from population values?
  4. Describe three kinds of sampling that will likely result in obtaining a representative sample. Which is most likely to result in a representative sample?
  5. How will you define the population for your study and what sampling strategy will you use?

Chapter 9 Principles of Analysis and Interpretation (REVIEW)  (p. 191)

  1. What is the difference between an observational unit (sampling unit, element of the set) and the measurement? 
  2. What is the difference between analysis of data and interpretation of data?
  3. How is the term frequency used to describe either nominal or ratio level data?
  4. Give an example of how two of the linear constructs from your project can be operationalized as a dichotomized variable.
  5. Describe the difference in how graph lines displaying main effects vs. interaction.
  6. Is ANOVA used most often to analyze experimental or correlational data? Why?
  7. What is profile analysis generally used for?
  8. How does multiple regression differ from canonical correlation and discriminant analysis?
  9. How did Lepper, Greene, and Nisbett (1973) find that extrinsic rewards influenced intrinsic interest?
  10. What are the differences between the two types of factor analysis procedures?
  11. Why is it more difficult to interpret inconclusive results rather than positive results?

Chapter 10 The Analysis of Frequencies  (p.221)     

  1.  Identify two true dichotomous variables (constructs) and two artificial dichotomies within the theoretical framework of your project.
  2. Create operational definitions for the IV and DV variables in your project that could be used in a 2 X 2 table.
  3. Use the on-line chi square calculator to calculate the X2 statistic for datasets in Tables 10.2 and 10.3. What conclusions can you draw?
  4. Use the on-line chi square calculator to calculate the X2 statistic for datasets in study questions #`1 and #2 for this chapter. Are the results the same given by the authors?
  5. Why is the one dimensional table (goodness of fit) generally not of interest to us in this course?   
  6. If  80%  of high aspiration students graduate from college and only 20% of low aspiration graduate, what are the odds that a high aspiration student will graduate? 
  7. Using SPSS, calculate the  Xin the chapter study question #1.


Chapter 12 Testing Hypotheses and the Standard Error (REVIEW) (p. 275)

  1. How is the standard error used in making inferences about research results?
  2. Why is it important to understand the implications for absolute vs. relative size?
  3. Would you ever use the null as a substantive hypothesis? If so, give an example.
  4. Name two types of standard errors.
  5. How do you calculate the standard error of the mean (SEm)?
        (note error in Table 21.1) N is actually Mean (N for each sample is 100)
  6. What is the relationship between the shape of a sampling distribution and the shape of the population distribution (Central limit theorem)?
  7. How does sample size affect the shape of the sampling distribution for differences between means?
  8. Give an example of a type I and type II errors that could occur with your project.
  9. What are the 5 steps of hypothesis testing?
  10. What is the relationship between the sample size and the standard error and what does this have to do with power?
  11. What information about the population can help in determining sample size needed?


Chapter 13 ANOVA: Foundation (REVIEW) (p. 307)

  1. How is the magnitude of the significance test affected by the effect size and size of the study? Why?
  2. Explain how three ways of maximizing t cause it to increase.
  3. What is the difference between the t for independent and non-independent samples?
  4. Explain each of three assumptions for errors when using the t test.
  5. How is t related to F and what are the advantages of each when comparing means?
  6. Why is eta used as an effect size estimate for F?
  7. Why is F a ratio and why does it have two sets of degrees of freedom?
  8. How does the Bonferonni procedure protect the researcher from doing something wrong?


Chapter 14 Factorial ANOVA (p. 345)

  1. What are the two primary reasons for conducting a factorial design?
  2. How many factors and levels of each factor are there in a 4X5 factorial design?
  3. How are  row effects and column effects defined?
  4.  What are interaction, or residual effects that are left over after the row and column effects have been partitioned from the grand mean?
  5. Is the source of error in a factorial design derived from the individual differences within each condition or within each treatment (factor)?
  6. Why is eta used to estimate the effect size in factorial designs?
  7. How many interactions are possible in a A X B X C design? What are they?
  8. How may two-way interactions are there in a four-way design and how many three-way interactions?

Chapter 17 Ethical Considerations in Conducting Behavioral Science Research (p.437)    

  1. Is it important, from an ethical standpoint, to debrief even when deception was not employed?
  2. Is surreptitious observation of elevator riders an unethical use of surveillance?
  3. Is it ethical to conduct research on elderly demented patients if consent is given by their power of attorney? 
  4. Give an example of conducting research when the cost is higher for doing it than not doing it, and another in which the cost is higher for NOT doing it.   

                             
Chapter 18 Research Design: Purpose and Principles (p. 449)   

  1. What are the two basic purposes of research design?
  2. What is the interaction hypothesis in the Walster, Cleary & Clifford (1970) study?
  3. Why is the design in figure 18.3 inadequate to test the interaction hypothesis?
  4. What is the mechanism operating behind the principle of controlling variance?
  5. What happens to variance when a continuous variable is dichotomized? 
  6. What is a way to avoid the problem created by dichotomizing the continuous variable?
  7. What are the active and attribute variables in the study comparing mastery with traditional methods of teaching?
  8. How can you maximize your experimental variance in your project?
  9. What are some ways you can minimize extraneous variables other than using random assignment?
  10. What effect does increasing reliability have on error variance?

 
Chapter 19 Inadequate Designs & Design Criteria (p. 465)

  1. What is a problem involving your project that can only be studied using a non-experimental design?
  2. How would you conduct a "one-shot case study" for your project?
  3. How is the one group, before-after design an improvement over the one-shot case study?
  4. Give an example of how a "reactive" effect could occur in studying your research problem.
  5. Give an example of how a "history" effect could be confounded with a manipulation. 
  6. Give an example of how maturation may affect a dependent variable.
  7. What problem is encountered when high and low scoring participants are selected for participants.
  8. How could the one group before-after design be improved to account for the regression 
  9. Is generalizability more important for basic or applied research?
  10. Are rival hypotheses a problem for internal or external validity?
  11. Explain how instrumentation, selection, and attrition could jeopardize internal validity in an experiment to test the effectiveness of an exercise program.
  12. What are ecological and variable representativeness?
  13. How can a "reactive" effect affect external validity? Give an example.


Chapter 20 General Designs of Research (p. 481)

  1. Are you using a "randomized control group post-test design" only for your project?
  2. What is a  "matched-by-correlated criterion design"?
  3. What are the two main disadvantages of trying to use a matched design?
  4. What difficulty arises using  frequency distribution matching for age and reaction time?
  5. What is a nuisance variable you could use for a control in your project?
  6. What is an advantage and disadvantage of using participants are their own controls?
  7. What are two ways to analyze the pre-post design data?

    Chapter 21 Research Design Applications: Randomized Grps & Corr Grps (p. 501)
  1. Does "nested" or "crossed" describe a repeated measures design?
  2. Why is repeated measures referred to as a "within" subjects design?
  3. What is the advantage of using subjects as their own controls?
  4.  What is the difference between how between subjects variance is used in a randomized (group) design and a repeated measures design?
  5. What is the difference between fixed and random effects?
  6. What is the purpose of a Latin Square design?
  7. Give an example of a study using a repeated measures design that would require counterbalancing.
  8. What is the difference between sequence and order?
  9. Give an example of a 2 (between) X 2 (within) design using variables in your project.


Chapter 32 Multiple Regression (p. 755)
    

  1. What's the difference between correlation and regression?
  2. What information does the intercept and slope tell about the relationship between the predictor(s) and criterion?
  3. What is the standard error of estimate and what does it tell us about the accuracy of prediction?
  4. What is a "residual"?
  5. For the best prediction to occur what should be the level of correlation among the predictors?
  6. Does a standardized or unstandardized  regresion weight reflect the measurement scale used?
  7. How could MR help to examine theory related to your project?

C'est finis