Basics of Social Research Canadian 3rd Edition Neuman - Test Bank

Chapter 11 Analysis of Quantitative Data 1) Explain how a researcher codes, enters, and cleans data so that it can be used for statistical analysis. What procedures are involved in each of these steps? Answer: • Coding the data: Data coding means systematically reorganizing raw numerical data into a format that is easy to analyze using computers; rules are developed whereby certain numbers are assigned to the attributes of each variable. • Entering data: Data are entered in a grid format where each row represents a respondent, subject, or case, and the column or a set of columns represents specific variables. Researchers can enter data into a computer by way of a code sheet, the direct-entry method, an optical scan, or bar code. • Cleaning the data: The researcher verifies the accuracy of coding after data are entered into a computer in two ways: possible code cleaning involves checking the categories of all variables for impossible codes; contingency cleaning (or consistency checking) involves cross-classifying two variables and looking for logically impossible combinations. Diff: 5 Type: ES Page Ref: 238–240 Learning Objective: 1. Explain that is meant by coding data. Skill: 04. Expresses familiarity with the range of acceptable techniques/methods in social research 2) Describe three ways a researcher can display information about univariate statistics. Answer: • Frequency distribution: A table that shows the distribution of cases into the categories of one variable (i.e., the number or percent of cases in each category) • Bar chart: A display of quantitative data for one variable in the form of rectangles where longer rectangles indicate more cases in a variable category • Pie chart: A display of numerical information on one variable that divides a circle into fractions by lines representing the proportion of cases in the variable’s attributes Diff: 3 Type: ES Page Ref: 240–241 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 3) Describe each of the three measures of central tendency. What are the main differences between them? How are they affected by a normal versus a skewed distribution of data? Answer: • Mean: A measure of central tendency for one variable that indicates the arithmetic average (i.e., the sum of all scores divided by the total number of scores) • Median: A measure of central tendency for one variable indicating the point or score at which half the cases are higher and half are lower • Mode: A measure of central tendency for one variable that indicates the most frequent or common score • Skewed distribution: If the frequency distribution forms a “normal” or bell-shaped curve (normal distribution), the three measures of central tendency equal each other. If the distribution is a skewed distribution (i.e., more cases are in the upper or lower scores), then the three will not be equal. If most cases have lower scores with a few extreme high scores, the mean will be the highest, the median in the middle, and the mode the lowest. If most cases have higher scores with a few extremely low scores, the mean will be the lowest, the median in the middle, and the mode the highest. Diff: 4 Type: ES Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 4) Why is knowing the variability or dispersion of a variable as important as knowing its central tendency? How is variation measured? Answer: • Two distributions can have identical measures of central tendency but differ in their spread about the centre. • Variability has important social implications. For example, in city X, the median and mean family income is $35,600 per year, and it has zero variation. Zero variation means that every family has an income of exactly $35,600. City Y has the same median and mean family income, but 95 percent of its families have incomes of $12 000 per year and 5 percent have incomes of $300,000 per year. City X has perfect income equality, whereas there is great inequality in city Y. A researcher who does not know the variability of income in the two cities misses very important information. • Researchers measure variation in three ways: range (the largest and smallest scores), percentile (the score at a specific place within the distribution), and standard deviation (the “average distance” between all scores and the mean). Diff: 4 Type: ES Page Ref: 243–244 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 5) Describe each of the three techniques researchers use when deciding whether a relationship exists between two variables. Answer: • Scattergram: A diagram to display the statistical relationship between two variables based on plotting each case’s values for both of the variables • Cross-tabulation: Placing data for two variables in a contingency table to show the number or percentage of cases at the intersection of categories of the two variables • Measures of association: A single number that expresses the strength, and often the direction, of a relationship. It condenses information about a bivariate relationship into a single number. Diff: 5 Type: ES Page Ref: 247–253 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 6) What are two ways in which statistical relationships can be described? Provide an example for each one. Answer: • Correlation: To be correlated means to vary together whereby cases with certain values on one variable are likely to have certain values on the other one (e.g., people with higher values on the income variable are likely to have higher values on the life expectancy variable). • Independence: There is no association (i.e., no relationship) between variables (e.g., there is likely no relationship between the two variables “number of siblings one has” and “life expectancy”). Diff: 4 Type: ES Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 7) What are five measures of association that are useful when interpreting bivariate statistics? Describe each one and also specify which level of data each one is applicable to. Answer: • Lambda: Used for nominal-level data and is based on a reduction in errors based on the mode and ranges between 0 (independence) and 1.0 (perfect prediction or the strongest possible relationship). • Gamma: Used for ordinal-level data and is based on comparing pairs of variable categories and seeing whether a case has the same rank on each. • Tau, or Kendall’s tau: Used for ordinal-level data where tau ranges from -1.0 to +1.0 with 0 meaning no association. • Rho, or Pearson’s product moment correlation coefficient: Used only for data measured at the interval or ratio level and tells how far cases are from a relationship (or regression) line in a scatterplot. • Chi-squared: Used for nominal and ordinal data; it has an upper limit of infinity and a lower limit of zero, meaning no association. Diff: 8 Type: ES Page Ref: 252–253 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 42. Able to explain and correctly interpret statistical significance 8) Discuss the concepts of control variables and trivariate tables. What are three limitations of trivariate tables? Answer: • In order to meet all the conditions needed for causality, researchers want to “control for” or see whether an alternative explanation explains away a causal relationship. If an alternative explanation explains a relationship, then the bivariate relationship is spurious. Alternative explanations are operationalized as third variables, which are called control variables because they control for alternative explanations. • A trivariate table has a bivariate table of the independent and dependent variable for each category of the control variable. These new tables are called partials. The number of partials depends on the number of categories in the control variable. Partial tables look like bivariate tables, but they use a subset of the cases. Only cases with a specific value on the control variable are in the partial. Thus, it is possible to break apart a bivariate table to form partials, or combine the partials to restore the initial bivariate table. • Trivariate tables have three limitations. First, they are difficult to interpret if a control variable has numerous categories. Second, control variables can be at any level of measurement, but interval or ratio control variables must be grouped (i.e., converted to an ordinal level), and how cases are grouped can affect the interpretation of effects. Finally, the total number of cases is a limiting factor because the cases are divided among cells in partials. Diff: 8 Type: ES Page Ref: 253–255 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 43. Able to interpret multivariate statistical relationships 9) Is a Type I or Type II error more likely if a 0.05 level is used? Explain. Answer: • Type I error: Falsely accepting the null hypothesis when in fact there is a causal relationship (usually occurs at a more precise level such as at the 0.01 level) • Type II error: Indicates a relationship when in fact no causal relationship exists (random factors actually caused the results and usually occurs at the 0.10 level) • 0.05 level is a compromise between Type I and Type II errors. Diff: 5 Type: ES Page Ref: 258–259 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 43. Able to interpret multivariate statistical relationships 10) Describe, as simply as possible, what is meant by the statement “It is statistically significant at the 0.05 level.” Answer: • The level of statistical significance (usually 0.05) is a way of talking about the likelihood that results are due to chance factors; that is, that a relationship appears in the sample when there is none in the population. • If a researcher says that results are significant at the 0.05 level, it means that one can be 95 percent confident that the results are due to a real relationship in the population, not chance factors. Diff: 5 Type: ES Page Ref: 257–258 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 43. Able to interpret multivariate statistical relationships 11) A “codebook” is A) only used in existing statistics research. B) a document that tells the researcher where variables are located in the data file and what numbers go with what variable attributes. C) the set of instructions that tell interviewers or experimenters how to treat respondents or subjects. D) an unnecessary part of data analysis since computers were invented. E) a sheet of paper with a grid of 80 columns corresponding to data card columns, with rows representing an individual card. Answer: B Diff: 1 Type: MC Page Ref: 238 Learning Objective: 1. Explain that is meant by coding of data. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 12) A researcher wants to express the middle of a distribution of numbers whereby half the cases are higher and half the cases are lower than the middle value. What statistical measure should the researcher use? A) Mean B) Median C) Mode D) Standard deviation E) Correlation Answer: B Diff: 2 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 13) Calculate the median for the following six shoe sizes: 9, 10, 10, 8, 12, 11. A) 8 B) 8.5 C) 9 D) 9.5 E) 10 Answer: E Diff: 2 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 14) Calculate the mode for the following six shoe sizes: 9, 10, 10, 8, 12, 11. A) 8 B) 8.5 C) 9 D) 9.5 E) 10 Answer: E Diff: 2 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 15) You discover that the years of marriage before a divorce for nine whites and nine nonwhites are as follows: Whites 12, 1, 8, 9, 10, 17, 3, 6, 6 Non-Whites 1, 9, 15, 18, 11, 13, 14, 7, 3 Which statement about this data is true? A) There is no difference in the range for the two groups. B) The median years of marriage prior to divorce is three years longer for whites than nonwhites. C) The mean years of marriage prior to divorce are the same for both groups. D) On average, nonwhites stay married longer prior to divorce than whites. E) The mode for the two groups is the same. Answer: D Diff: 4 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 16) Calculate the mean for the following six shoe sizes: 9, 10, 10, 8, 12, 11. A) 8 B) 8.5 C) 9 D) 9.5 E) 10 Answer: E Diff: 3 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 17) An elementary school teacher has three classes. She finds the following mean and standard deviations for student IQ scores. Mean Standard Deviation Class #1 106 32 Class #2 104 9 Class #3 110 16 She knows she is most effective when the students all have similar IQ levels. Which class is she likely to be most effective with? A) Class #1 B) Class #2 C) Class #3 D) Classes #1 and #2 equally E) Classes #1 and #3 equally Answer: B Diff: 4 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics Refer to the information in the following table when answering questions 18 to 26: 18) Which neighbourhood(s) has an income distribution resembling a skewed curve? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: D Diff: 5 Type: MC Page Ref: 242–243 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 19) Which neighbourhood(s) has an income distribution resembling a normal curve? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: B Diff: 5 Type: MC Page Ref: 242–243 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 20) Which neighbourhood(s) has the greatest differences in family income? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: A Diff: 5 Type: MC Page Ref: 244–245 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 21) Which neighbourhood(s) has the smallest differences in family income? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: B Diff: 5 Type: MC Page Ref: 244–245 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 22) In which neighbourhood(s) do half the families have incomes of $28,000 or more? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: B Diff: 5 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 23) Which neighbourhood(s) has the highest number of families with the lowest incomes? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: C Diff: 5 Type: MC Page Ref: 241–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 24) Which neighbourhood(s) has the 50th percentile of education as a college education? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: B Diff: 5 Type: MC Page Ref: 241–242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 25) Which neighbourhood(s) has a small number of very high income people and a greater proportion of families earning $26,000? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: A Diff: 5 Type: MC Page Ref: 241–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 26) Which neighbourhood(s) has the greatest variation in years of education? A) Glenbrook B) Meadowbrook C) Elmbrook D) Glenbrook and Elmbrook E) Meadowbrook and Elmbrook Answer: B Diff: 5 Type: MC Page Ref: 241–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 27) Fatima Fashionista wears a size 2 blazer. She went to her favourite clothing store and found that the mean size of the store’s stock of blazers is a size 10 with a standard deviation of 4 sizes. What is her z-score in the distribution of the store’s blazers? A) zero B) 1 C) 2 D) -1.5 E) -2 Answer: E Diff: 7 Type: MC Page Ref: 245–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 28) Susan weighs 140 pounds. You learn that for the women in her sorority the mean weight is 130 pounds, the median is 125, the mode is 120, and the standard deviation is 10 pounds. What is Susan’s z-score in the distribution weight in the sorority? A) zero B) 1 C) 2 D) 1.5 E) -1 Answer: B Diff: 7 Type: MC Page Ref: 245–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 29) All the sales representatives at Acadia Insurance are female, and their mean annual salary is $60,000 with a standard deviation of $5000. All customer account managers are male and they have a mean salary of $80,000 with a standard deviation of $15,000. Heather knows she is one standard deviation above the mean of the sales representatives. She wants to transfer to become the first female customer account manager and will begin at her same salary. After she transfers, compared to the customer account managers her salary will be at what z-score? A) -2 B) -1 C) 0 D) +1 E) +2 Answer: B Diff: 8 Type: MC Page Ref: 245–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 01. Applies abstract learning to realistic situations 30) Connie and Tom both received 75 percent on the social research methods final exam. The mean for all women who took the test was 80 percent and the median was 75 percent with a standard deviation of 5 percent. The mean and median for the men was 65 percent with a standard deviation of 10 percent. What is the z-score for Connie and Tom relative to those of their own sex? A) Connie’s z-score is +1 and Tom’s is -2, so Connie did better. B) Connie’s z-score is -1 and Tom’s is +1, so Tom did better. C) Both Connie and Tom have the same z-score, 1. D) Connie’s z-score is 0 and Tom’s is +1, so he did worse. E) Connie’s z-score is -2 and Tom’s is -1, so Tom did better. Answer: B Diff: 8 Type: MC Page Ref: 245–247 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 40. Able to calculate, read, and correctly interpret univariate statistics 31) Your research assistant finds a positive relationship between mother’s education and income at age 40. The relationship remained after controlling for the family’s social class. This means that A) social class is really the causal variable. B) social class does not have an impact on the relationship. C) the original relationship is spurious. D) income is caused by neither the mother’s education level nor her social class. E) the mother’s education has no effect on her future income. Answer: B Diff: 6 Type: MC Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics Refer to the information in the following table when answering questions 32 and 33: 32) What type of relationship exists between level of violence and level of education? A) There is no relationship. B) There is a negative relationship. C) There is a positive relationship. D) There is a nonlinear relationship. E) There is a recursive relationship. Answer: B Diff: 5 Type: MC Page Ref: 248 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 33) How many individuals with 17+ years of education engaged in a level of violence defined as medium? A) 20 B) 180 C) 160 D) 55 E) 5 Answer: A Diff: 6 Type: MC Page Ref: 249–251 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 34) Among the violent offenders in the following table, what is the ratio of males to females? A) 1 male to 1 female, or 1:1 B) 2 males to 3 females, or 2:3 C) 4 males to 1 female, or 4:1 D) 2 males to 1 female, or 2:1 Answer: D Diff: 6 Type: MC Page Ref: 249–251 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 35) If the true situation in the world is that there is no causal relationship, but a researcher states that there is a causal relationship, what error occurs? A) Type I B) Type II C) Falsely reject the null hypothesis D) Falsely accept the null hypothesis E) A and C Answer: E Diff: 4 Type: MC Page Ref: 258–259 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 42. Able to explain and correctly interpret statistical significance 36) What does it mean if someone tells you that they calculated a correlation of -0.75? A) There is a strong negative relationship between two ratio or interval level variables. B) There is statistical independence between the two variables. C) The variables that were used were measured at the nominal level and the relationship between them is strong. D) The researcher looked at two ordinal-level variables and found no relationship between them. E) The person made an error in calculation: correlations can never be negative numbers. Answer: A Diff: 7 Type: MC Page Ref: 253 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 42. Able to explain and correctly interpret statistical significance 37) Which of the following research topics is an example of multivariate analysis? A) An analysis of the ages of all women who are corporate executives B) An analysis of the relationship between age, sex, and type of restaurant frequented in Saskatoon C) An analysis of the relationship between undergraduate majors and level of position held in a major corporation D) An analysis of the relationship between type of offense and length of prison sentence E) The relationship between socio-economic status and annual income Answer: B Diff: 4 Type: MC Page Ref: 240 Learning Objective: 1. Explain that is meant by coding data. Skill: 43. Able to interpret multivariate statistical relationships 38) What feature of experimental research allows it to demonstrate causality without control variables? A) Experimental researchers cannot demonstrate causality without control variables. B) Experimental researchers eliminate alternative explanations by choosing a research design that physically controls potential alternative explanations for results. C) Experimental researchers eliminate alternative explanations by testing temporal order and association. D) Experimental researchers can be assured causal relationships are not spurious because they use random sampling procedures. E) Experimental researchers make use of partials instead of control variables in order to demonstrate causality. Answer: B Diff: 4 Type: MC Page Ref: 253 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 43. Able to interpret multivariate statistical relationships 39) Which of the following tells how well a set of variables explains a dependent variable? A) Z-score B) Standard deviation C) Chi-square D) R-squared E) Gamma Answer: D Diff: 4 Type: MC Page Ref: 242–243 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 43. Able to interpret multivariate statistical relationships 40) If the true situation in the world is that there is a causal relationship, but a researcher states that there is no causal relationship, what error occurs? A) Type I B) Type II C) Falsely reject the null hypothesis D) Falsely accept the null hypothesis E) B and D Answer: E Diff: 4 Type: MC Page Ref: 258–259 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 42. Able to explain and correctly interpret statistical significance 41) Which of the following cannot be learned from a scattergram? A) Form B) Intensity C) Direction D) Precision E) None of the above Answer: B Diff: 6 Type: MC Page Ref: 248–249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 42) Which of the following is an example of a multivariate statistic? A) Multiple regression B) Standard deviation C) Z-score D) Chi-square E) Age Answer: A Diff: 4 Type: MC Page Ref: 256 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 43. Able to interpret multivariate statistical relationships 43) Usually, when constructing a scattergram, the __________ goes on the X axis and the __________ goes on the Y axis. A) independent variable; dependent variable B) dependent variable; independent variable C) form; direction D) direction; form E) control variable; causal variable Answer: A Diff: 4 Type: MC Page Ref: 247–248 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 41. Able to calculate, read, and correctly interpret simple bivariate statistics 44) Professor Quincy Quacker found a statistically significant relationship between variable X and variable Y. It is statistically significant at the 0.05 level. What does this mean? A) There are 95 chances in 100 that the results are true. B) There is a 5 percent chance that the results are true. C) If 100 samples were drawn, results like these could be obtained by pure random chance 10 percent of the time. D) A person could be 95 percent sure that the results of the study were a reflection of the population if random sampling was used. E) A and D. Answer: E Diff: 3 Type: MC Page Ref: 257–258 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 42. Able to explain and correctly interpret statistical significance 45) bar chart Answer: A display of quantitative data for one variable in the form of rectangles, where longer rectangles indicate more cases in a variable category. Diff: 4 Type: ES Page Ref: 241 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 46) bivariate statistics Answer: Statistical measures that involve two variables only. Diff: 4 Type: ES Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 47) body of a table Answer: The centre part of a contingency table. It contains all the cells, but not the totals or labels. Diff: 4 Type: ES Page Ref: 249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 48) cell of a table Answer: A part of the body of a table. In a contingency table, it shows the distribution of cases into categories of variables as a specific number or percentage. Diff: 4 Type: ES Page Ref: 249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 49) code sheet Answer: Paper with a printed grid on which a researcher records information so that it can be easily and quickly entered into a computer. It is an alternative to a direct-entry method and using optical-scan sheets. Diff: 4 Type: ES Page Ref: 239 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 50) codebook Answer: A document that describes the procedure for coding variables and their location in a format for computers. Diff: 4 Type: ES Page Ref: 238 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 51) contingency cleaning Answer: Cleaning data using a computer in which the research looks at the combination of categories for two variables to seek out logically impossible combinations. Diff: 4 Type: ES Page Ref: 240 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 52) contingency table Answer: A table that shows the cross-tabulation of two or more variables. It usually shows bivariate quantitative data for variables in the form of percentages across rows or down columns. Diff: 4 Type: ES Page Ref: 249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 53) control variable Answer: A third variable that shows whether a bivariate relationship holds up under alternative explanations. The control variable may occur prior to both variables or between the two variables of a bivariate relationship. Diff: 4 Type: ES Page Ref: 253 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 50. Able to define key terms 54) correlation Answer: The idea that two variables vary together, such that knowing the values in one of the variables provides information about values to be found on the second variable. Diff: 4 Type: ES Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 55) cross-tabulation Answer: Placing data for two variables in a contingency table to show the number or percentage of cases at the intersection of categories of the two variables. Diff: 4 Type: ES Page Ref: 249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 56) curvilinear relationship Answer: A relationship between two variables such that as the values of one variable increase, the values of the second show a changing pattern—such as first decreasing then increasing then decreasing. It is the opposite of a linear relationship. Diff: 4 Type: ES Page Ref: 248 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 57) descriptive statistics Answer: A general type of simple statistics used by researchers to describe basic patterns in the data. Diff: 4 Type: ES Page Ref: 240 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 58) direct entry method Answer: A method for entering data into a computer by typing data values on a keyboard. Diff: 4 Type: ES Page Ref: 240 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 59) frequency distribution Answer: A table that shows the distribution of cases into the categories of one variable (i.e., the number or percentage of cases in each category). Diff: 4 Type: ES Page Ref: 240 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 60) independence Answer: The absence of a statistical relationship between two variables. It indicates there is no association between the variables. Diff: 4 Type: ES Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 61) level of statistical significance Answer: A set of numbers that researchers use to simplify their discussion of statistical significance. It measures the degree to which a statistical relationship is likely to be due to a true relationship rather than to random chance. Diff: 4 Type: ES Page Ref: 258 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 50. Able to define key terms 62) linear relationship Answer: An association between two variables that is positive or negative consistently across the levels of the variables. When plotted in a scattergram, the basic pattern of association forms a straight line. Diff: 4 Type: ES Page Ref: 248 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 63) marginals Answer: The totals of a contingency table, outside the body of a table. Diff: 4 Type: ES Page Ref: 250 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 64) mean Answer: A measure of central tendency for one variable that indicates the arithmetic average (i.e., the sum of all scores divided by the number of scores). Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 65) measure of association Answer: A single number that expresses the strength, and often the direction, of a relationship. It condenses information about a bivariate relationship into a single number. Diff: 4 Type: ES Page Ref: 252 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 66) median Answer: A measure of central tendency for one variable that indicates the point at which half the cases have higher values and half have lower values; also called the midpoint. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 67) mode Answer: A measure of central tendency for one variable that indicates the most frequent value or category of a variable. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 68) multiple regression Answer: A statistical technique whose results (1) indicate how well a set of variables explains a dependent variable; and (2) measure the direction and size of the effect of each variable on a dependent variable. Diff: 4 Type: ES Page Ref: 256 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 50. Able to define key terms 69) normal distribution Answer: Also called a “bell-shaped” frequency polygon. When the number of cases is plotted by values of a variable, the distribution shows a peak at the centre and symmetrical downward sloping curves on each side of it. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 70) partials Answer: In contingency tables for three variables, the tables that show an association between the independent and dependent variable for each category of the control variable. Diff: 4 Type: ES Page Ref: 255 Learning Objective: 4. Describe the purpose of multivariate analysis. Skill: 50. Able to define key terms 71) percentile Answer: A measure of dispersion for one variable that indicates the percentage of cases that are at or below a specific point or score. Diff: 4 Type: ES Page Ref: 244 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 72) pie chart Answer: A display of numerical information for one variable that shows a circle with slices indicating the number or percentage of cases in each category of a variable. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 73) possible code cleaning Answer: Cleaning quantitative data using a computer in which the researcher looks for responses or answer categories that are not legitimate values. Diff: 4 Type: ES Page Ref: 240 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 74) precision Answer: The amount of spread in the points on the graph. A high level of precision occurs when the points hug the line that summarizes the relationship. A low level occurs when the points are widely spread around the line. Diff: 4 Type: ES Page Ref: 249 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 75) range Answer: A measure of dispersion for one variable indicating the highest and lowest scores. Diff: 4 Type: ES Page Ref: 244 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 76) scattergram Answer: A diagram that displays the statistical relationship between two continuous variables by plotting each case’s values for both of variables. Diff: 4 Type: ES Page Ref: 247 Learning Objective: 3. Explain the techniques of bivariate analysis. Skill: 50. Able to define key terms 77) skewed distribution Answer: A distribution of cases for the categories of one variable that is not normal (i.e., it is not “bell-shaped”). Instead of an equal number of cases at each extreme, more are at one extreme than the other. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 78) standard deviation Answer: A measure of dispersion for one continuous variable that indicates the average distance of scores from the mean. Diff: 4 Type: ES Page Ref: 244 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 79) statistical significance Answer: A way to discuss the likelihood that a finding or statistical relationship in a sample is due to random factors rather than due to the existence of an actual relationship in the entire population. Diff: 4 Type: ES Page Ref: 257 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 50. Able to define key terms 80) Type I error Answer: The logical error of falsely rejecting the null hypothesis that uses statistical significance. Diff: 4 Type: ES Page Ref: 258 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 50. Able to define key terms 81) Type II error Answer: The logical error of falsely accepting the null hypothesis based on statistical significance. Diff: 4 Type: ES Page Ref: 258 Learning Objective: 5. Describe the relationship between inferential statistics, levels of significance, and Type I and Type II errors. Skill: 50. Able to define key terms 82) univariate statistics Answer: Statistical measures that deal with one variable only. Diff: 4 Type: ES Page Ref: 240 Learning Objective: 1. Explain that is meant by coding data. Skill: 50. Able to define key terms 83) z-score Answer: A way to locate a score in a distribution of scores by determining the number of standard deviations it is above or below the mean. Diff: 4 Type: ES Page Ref: 245 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 84) histogram Answer: A type of bar chart used to visually display the distribution of a continuous variable. Diff: 4 Type: ES Page Ref: 241 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 85) bimodal Answer: A distribution with two modes. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms 86) multimodal Answer: A distribution with more than one mode. Diff: 4 Type: ES Page Ref: 242 Learning Objective: 2. Define and give examples of univariate analysis. Skill: 50. Able to define key terms Chapter 3 Ethics in Social Research 1) Why are some groups of people called “special populations” in social research? What things must a researcher do differently when studying them? Answer: • Some populations or groups of research participants are not capable of giving true, voluntary informed consent. • Special populations are people who lack the necessary cognitive competency to give valid informed consent or people in a weak position who might cast aside their freedom to refuse in order to participate in a study. • Students, prison inmates, employees, military personnel, the homeless, welfare recipients, children, or the developmentally disabled may not be fully capable of making a decision, or they may agree to participate only because they see their participation as a means of obtaining a desired good—such as higher grades, early parole, promotions, or additional services. • It is unethical to involve “incompetent” people (e.g., children, the mentally challenged) in research unless a researcher meets two minimal conditions: (1) a legal guardian grants written permission, and (2) the researcher follows all standard ethical principles to protect the participant from harm. Diff: 7 Type: ES Page Ref: 52 Learning Objective: 6. Explain special considerations that need to be made when working with special populations. Skill: 12. Recognizes and can apply ethical principles to social research situations 2) How do power relations, deception, and coercion to participate in research conflict with the principle of voluntary consent? Answer: • The principle of voluntary consent is an ethical principle of social research that states that people should never participate in research unless they first explicitly agree to do so. • With regard to power relations, a professional researcher and the research participants or employee-assistants are in a relationship of unequal power and trust. An experimenter, survey director, or research investigator has power over participants and assistants, and, in turn, they trust his/her judgment and authority. The researcher’s credentials, training, and professional role and the place of science in modern society legitimate the power and make it into a form of expert authority. Some ethical issues involve an abuse of power and trust. A researcher’s authority to conduct social research and to earn the trust of others is always accompanied by an immutable ethical responsibility to guide, protect, and oversee the interests of the people being studied. • Social researchers sometimes deceive or lie to participants in field and experimental research. A researcher might misrepresent his/her actions or true intentions for legitimate methodological reasons. For example, if participants knew the true purpose, they would modify their behaviour, making it impossible to learn their real behaviour. Another situation occurs when access to a research site would be impossible if the researcher told the truth. Deception is never preferable if the researcher can accomplish the same thing without using deception. • The use of coercion to participate in a study can be a tricky issue, and it depends on the specifics of the situation. Today it is unlikely that individuals will be coerced; however, there are still cases where people may be implicitly coerced. For example, undergraduate students who are asked to participate in research in classes may feel obligated to participate because they want a good grade. Diff: 9 Type: ES Page Ref: 45, 49 Learning Objective: 3. Describe power relations in social research. Skill: 12. Recognizes and can apply ethical principles to social research situations 3) What is informed consent? Why was it developed? How does it protect research subjects? Answer: • Informed consent is an agreement by participants stating they are willing to be in a study after they learn something about what the research procedure will involve. • It was developed so that research participants would be aware of their rights and what they are getting involved in. • Full disclosure with the researcher’s identification helps to protect research participants against fraudulent research and to protect legitimate researchers. Informed consent lessens the chance that a con artist in the guise of a researcher will defraud or abuse subjects. It also reduces the chance that someone will use a bogus identity to market products or obtain personal information about people for unethical purposes. Diff: 4 Type: ES Page Ref: 50–51 Learning Objective: 5. Differentiate between voluntary and informed consent. Skill: 12. Recognizes and can apply ethical principles to social research situations 4) Describe the difference between anonymity and confidentiality. Answer: • Anonymity means that people remain anonymous or nameless. For example, a field researcher provides a social picture of an individual but gives a fictitious name and location and alters some characteristics. The subject’s identity is protected, and the individual remains unknown or anonymous. • Confidentiality can include information with participant names attached, but the researcher holds it in confidence or keeps it secret from public disclosure. The researcher releases data in a way that does not permit linking specific individuals to responses and presents it publicly only in an aggregate form (e.g., as a percentage or statistical means). Diff: 4 Type: ES Page Ref: 54–56 Learning Objective: 7. Define privacy, anonymity, and confidentiality. Skill: 12. Recognizes and can apply ethical principles to social research situations 5) How might a sponsor attempt to illegitimately influence a researcher? What can the researcher do about it and why might a researcher hesitate about being ethical? Answer: • A sponsor may attempt to illegitimately influence a researcher by: (1) telling the researcher, directly or indirectly, what results the researcher should come up with before the study is undertaken; (2) telling the researcher to suppress scientific information that contradicts official policy or embarrasses high officials; (3) placing unreasonable limitations on research (e.g., withholding funding necessary to uphold generally accepted standards of research, demanding a biased sample or leading research questions, etc.); (4) telling the researcher not to reveal to participants information about who is funding the study. • For (1), a researcher should refuse to participate if she or he is told to arrive at specific results as a precondition for doing research. Legitimate research is conducted without restrictions on the possible findings that the study might yield. For (2), a researcher should negotiate conditions for releasing the findings prior to beginning the study and sign a contract to that effect. It may be unwise to conduct the study without such a guarantee, although competing researchers who have fewer ethical scruples may do so. Alternatively, a researcher can accept the sponsor’s criticism and hostility and release the findings over the sponsor’s objections. For (3), a researcher should refuse to continue a study if he or she cannot uphold generally accepted standards of research. For (4), in general, an ethical researcher will tell the subjects who is sponsoring the study unless there is a strong methodological reason for not doing so. When reporting or publishing results, the ethical mandate is very clear: A researcher must always reveal the sponsor who provides funds for a study. Diff: 9 Type: ES Page Ref: 59–61 Learning Objective: 8. Explain ethical issues that are specific to research involving sponsors. Skill: 13. Explains the need to balance competing ethical principles 6) Identify three major cases in the history of research ethics and describe the basic principles of ethical research they illustrate. Answer: • Stanley Milgram’s obedience study in which ethical concerns were raised over the use of deception and the extreme emotional stress experienced by subjects. • Laud Humphreys’ tearoom trade study in which the subjects never consented; deception was used; and the names could have been used to blackmail subjects, to end marriages, or to initiate criminal prosecution. • Zimbardo’s prison experiment in which the risk of permanent psychological harm, and even physical harm, was great. Diff: 4 Type: ES Page Ref: 47 Learning Objective: 4. Identify major ethical issues involving research with human participants. Skill: 12. Recognizes and can apply ethical principles to social research situations 7) Describe what a whistle-blower is in social research settings, and what pressures a whistle-blower might feel to keep quiet or go public. Answer: • A whistle-blower is a person who sees ethical wrongdoing, tries unsuccessfully to correct it internally, and then informs an external audience, agency, or the media. • By doing what is moral, a whistle-blower needs to be prepared to make sacrifices—loss of a job or promotion, lowered pay, an undesirable transfer, abandonment by friends or work, or legal costs. There is no guarantee that doing the ethical or moral thing will stop the unethical behaviour or protect the honest researcher from retaliation. Diff: 3 Type: ES Page Ref: 58–59 Learning Objective: 8. Explain ethical issues that are specific to research involving sponsors. Skill: 13. Explains the need to balance competing ethical principles 8) In what ways do political pressures affect the conduct of social research? What are the three main causes of attempts to block or steer social research? Answer: • The politics of research usually involves actions by organized advocacy groups, powerful interests in society, governments, or politicians trying to restrict or control the direction of social research. • Historically, the political influence over social research has included preventing researchers from conducting a study, cutting off or redirecting funds for research, harassing individual researchers, censoring the release of findings, and using social research as a cover or guise for covert government intelligence/military actions. • Attempts to block and to steer social research have three main causes: (1) some people defend or advance positions and knowledge that originate in deeply held ideological, political, or religious beliefs, and they fear that social researchers might produce knowledge that contradicts them; (2) powerful interests want to protect or advance their political/financial position and fear social researchers might yield findings showing that their actions are harmful to the public or some sectors of society; (3) some people in society do not respect the ideal of science to pursue truth and knowledge and instead view scientific research only as a means for advancing private interests Diff: 6 Type: ES Page Ref: 61–62 Learning Objective: 8. Explain ethical issues that are specific to research involving sponsors. Skill: 12. Recognizes and can apply ethical principles to social research situations 9) Identify and define two forms of scientific misconduct and provide an example of each. Answer: • Research fraud occurs when a researcher fakes or invents data that he or she did not really collect or fails to honestly and fully report how he or she conducted a study. • The student must provide an example of research fraud similar to the following: The most famous case of research fraud was that of Sir Cyril Burt, the father of British educational psychology. Burt died in 1971 as an esteemed researcher who was famous for his studies on twins, which showed a genetic basis for intelligence. In 1976, however, it was discovered that he had falsified data and the names of his coauthors. Unfortunately, the scientific community had been misled for nearly 30 years. • Plagiarism occurs when a researcher “steals” the ideas or writings of another or uses them without citing the source. Plagiarism includes stealing the work of another researcher, an assistant, or a student and misrepresenting it as one’s own. • The student must provide an example of plagiarism similar to the following: An undergraduate student at Memorial University was recently accused of child abuse when she failed to reference an account of a juvenile sex offender who abused children in the offender’s care. A graphic description of the abuse, which was added as an appendix to her research paper, had been copied word for word out of a textbook. The student failed to reference the source, however, and the professor marking her paper thought that it was the student’s personal account of abusing children. The professor then contacted Child Protection Services, which resulted in a 12-year battle for this student to clear her name. Diff: 7 Type: ES Page Ref: 44 Learning Objective: 2. Define scientific misconduct, research fraud, and plagiarism. Skill: 12. Recognizes and can apply ethical principles to social research situations 10) What are the three types of harm a researcher must be aware of when conducting a study? What steps can researchers take to mitigate the potential impact of these three types of harm? Answer: • (1) physical harm; (2) psychological abuse, stress, or loss of self-esteem; (3) legal harm • For (1), anticipate risks, including safety concerns, before beginning a study; screening high-risk subjects • For (2), only highly experienced researchers should consider conducting a study that purposely induces great stress or anxiety in research participants; consult with others who have conducted similar studies and with mental health professionals as they plan the study. They should screen out high-risk populations (e.g., those with emotional or cardiac problems) and arrange for emergency interventions or termination of the research if dangerous situations arise. They must always obtain written informed consent before the research commences, and they must debrief subjects immediately afterward (i.e., explain any deception and what actually happened in the study). • For (3), if a researcher observes illegal behaviour, they should weigh the benefit of reporting to law enforcement agencies against their ethical commitments to participants (i.e., to uphold confidentiality) and the potential for undermining future research; when a researcher learns of illegal activity when collecting data, they must weigh the value of protecting the researcher–subject relationship and the benefits to future researchers against potential serious harm to innocent people. Diff: 8 Type: ES Page Ref: 46–48 Learning Objective: 4. Identify major ethical issues involving research with human participants. Skill: 13. Explains the need to balance competing ethical principles 11) Chad Hunt is a graduate student who studies the ways people use social assistance. Chad grew up hearing news reports and dinner table conversations about how welfare recipients are typically lazy people who abuse the system even though they can and should work. The data Chad collects indicates that the majority of people on social assistance are hard-working persons who have been displaced by economic restructuring. Chad believes his data does not accurately depict the truth of the matter, so he falsifies documents to make it appear as though abuse of social assistance is common. What offence has Chad committed? A) Research fraud B) Data reconfiguration C) Plagiarism D) Breaking confidentiality E) Breaking anonymity Answer: A Diff: 2 Type: MC Page Ref: 44 Learning Objective: 2. Define scientific misconduct, research fraud, and plagiarism. Skill: 12. Recognizes and can apply ethical principles to social research situations 12) If a researcher conducting a survey gets “informed consent,” she will A) get an ok from funding agencies to experiment with controlled substances. B) get permission to interview friends and family members about personal behaviour (such as sexual relations). C) get permission to conduct the interview with the respondent after telling the respondent something about the interview. D) get permission from other researchers to use non-random sampling. E) get permission from people to use their actual names and addresses in published studies about them. Answer: C Diff: 2 Type: MC Page Ref: 50 Learning Objective: 5. Differentiate between voluntary and informed consent. Skill: 12. Recognizes and can apply ethical principles to social research situations 13) The principle of voluntary consent in social research means A) a professor who hands out a questionnaire to students should inform students that their participation is voluntary and that they can refuse to participate without penalty. B) many research findings actually have limited generalizability to those subjects/respondents who agree to participate in research. C) this norm is violated in covert field research. D) a researcher using deception should tell subjects that they can leave at any time. E) all of the above. Answer: E Diff: 4 Type: MC Page Ref: 49 Learning Objective: 5. Differentiate between voluntary and informed consent. Skill: 12. Recognizes and can apply ethical principles to social research situations 14) For an honours undergraduate project, Steven Smith conducts a survey on students’ beliefs and behaviours, including sexual behaviour. While distributing the questionnaire, he assures the group of students that no one will be able to trace responses to an individual. He notices where each person sat before he or she returns the questionnaire to the front of the room and memorizes who turned in the first, second, etc., questionnaire. Which ethical principle is Steven violating? A) Confidentiality B) Anonymity C) Harm to subjects D) Concealed identity of researcher E) Voluntary participation Answer: B Diff: 5 Type: MC Page Ref: 54 Learning Objective: 7. Define privacy, anonymity, and confidentiality. Skill: 01. Applies abstract learning to realistic situations 15) What is the general ethical principle regarding deception in social research? A) It is fully acceptable and does not involve ethical issues. B) It is forbidden under all circumstances of ethical research. C) It can be ethically used if essential to the research so long as subjects are not physically harmed. D) It can be ethically used if essential to the research, but only to the minimal degree necessary and it must be followed by debriefing. E) Deception can only be ethically used when subjects are “captive” populations (e.g., prisoners, students, mental hospital patients, military personnel). Answer: D Diff: 3 Type: MC Page Ref: 49–50 Learning Objective: 5. Differentiate between voluntary and informed consent. Skill: 12. Recognizes and can apply ethical principles to social research situations 16) The original source for the principles of codes of ethics for research on human subjects was developed out of A) the Amnesty International Code of 1975. B) the Nuremburg Trial of Nazi war crimes in 1946–47. C) the 1964 Civil Rights Act. D) the League of Nations Charter of 1919. E) the Constitution of the United States. Answer: B Diff: 2 Type: MC Page Ref: 57 Learning Objective: 7. Define privacy, anonymity, and confidentiality. Skill: 12. Recognizes and can apply ethical principles to social research situations 17) Professor Ivan Ishtar copied five pages from a student paper that was not protected by copyright laws and put it in an article he published but failed to give credit to the student. He also claimed to interview 10 people who he never interviewed. Professor Ishtar committed all the following activities EXCEPT which one? A) Plagiarism B) Research fraud C) Scientific misconduct D) Unethical but legal behaviour E) Illegal but ethical behaviour Answer: E Diff: 6 Type: MC Page Ref: 43–45 Learning Objective: 2. Define scientific misconduct, research fraud, and plagiarism. Skill: 01. Applies abstract learning to realistic situations 18) It is NOT ethical for a sponsor to do which of the following? A) A school district wants a study of students, but demands that a researcher report findings showing an improvement in student scores during the past five years. B) A supervisor requires prior review of questionnaire items to make some of them “leading” to make certain that the company looks good. C) A government agency that paid for a study suppresses findings that indicate that it has not