Research Methods for the Behavioral Sciences (MindTap Course List)
6th Edition
ISBN: 9781337613316
Author: Frederick J Gravetter, Lori-Ann B. Forzano
Publisher: Cengage Learning
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Chapter 10, Problem 4E
Explain why we can be more confident about causal relationships between variables when a quasi-experimental design is used instead of a nonexperimental design.
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Does a high value of r2 imply that two variables are causally related? Explain.
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Chapter 10 Solutions
Research Methods for the Behavioral Sciences (MindTap Course List)
Ch. 10.1 - Define, compare, and contrast the experimental,...Ch. 10.2 - Define a nonequivalent group design and identify...Ch. 10.2 - Explain how individual differences threaten the...Ch. 10.2 - Describe the two nonexperimental nonequivalent...Ch. 10.2 - Explain how a simple modification of the...Ch. 10.3 - Define a pre-post design and identify examples of...Ch. 10.3 - Identify the threats to internal validity for...Ch. 10.3 - Describe the nonexpenmental pretest-posttest...Ch. 10.3 - Explain how replacing the single observation...Ch. 10.4 - Define cross-sectional and longitudinal designs,...
Ch. 10.5 - Identify the statistical techniques that are...Ch. 10.5 - Explain how the terms quasi-independent variable...Ch. 10 - In addition to the key words, you should also be...Ch. 10 - Explain the distinction between experimental and...Ch. 10 - Why are studies that examine the effects of aging...Ch. 10 - Explain why we can be more confident about causal...Ch. 10 - Give an example of a situation (aside from gender)...Ch. 10 - Mueller and Oppenheimer (2014) conducted a series...Ch. 10 - A researcher measures personality characteristics...Ch. 10 - A researcher wants to describe the effectiveness...Ch. 10 - Explain how the pretest helps minimize the threat...Ch. 10 - Describe the basic characteristics of a pre-post...Ch. 10 - To evaluate the effectiveness of a new television...Ch. 10 - What characteristic differentiates a pretest-post...Ch. 10 - Explain how a time-series design minimizes most...Ch. 10 - A researcher wants to describe how fine motor...Ch. 10 - Although the cohort effect can be a serious...Ch. 10 - Identify the appropriate statistical test for each...Ch. 10 - The college offers all students an optional...
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- Omitting a variable from our model that has a causal effect on our dependent variable always leads to omitted variable bias. a. False b. Truearrow_forwardHow forecasting is quite different from the estimation of causal effects.?arrow_forwardRead through this scenario and look at the data that was collected. State the null and all possible research hypotheses. Review the results below (I used SPSS) and answer the questions that follow. Scenario: A researcher wants to see if gender and / or income affects the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affects the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables…arrow_forward
- Read through this scenario and look at the data that was collected. State the null and all possible research hypotheses. Review the results below (I used SPSS) and answer the questions that follow. Scenario: A researcher wants to see if gender and/or income affect the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affect the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables…arrow_forwardFind an interesting instance where two variables are strongly correlated, but share no causal link. Explain the situation, describe the variables, and explain why it doesn't make sense for the data to be correlated.arrow_forwardA group of educators want to determine how effective tutoring is in raising students' grades in a math class, so they arrange free tutoring for those who want it. Then they compare final exam grades for the group that took advantage of the tutoring and the group that did not. Suppose the group participating in the tutoring tended to receive higher grades on the exam. Does that show that the tutoring worked? If not, explain why not and suggest a confounding variable. Question content area bottom Part 1 Choose the correct answer below. A. Because this was an observational study, it only shows an association; it does not show that the tutoring worked. It could be that an insufficient number of students were studied to show that the tutoring worked. B. The study shows that the tutoring worked. C. Because this was an observational study, it only shows an association; it does not show that the tutoring worked. It could be that more motivated…arrow_forward
- Economists, Angrist and Lavy (1999) exploited an 800 year-old rule regarding classroom size to estimate the causal effect of smaller classes on student achievement. The rule, made famous by 12th century scholar Maimonides, states that one teacher is sufficient to manage a class with up to 40 students. Classes with more than 40 students will require 2 teachers. Angrist and Lavy noticed that strict application of this rule in certain areas meant that if 80 students were enrolled in a school, then the school would divide them up into two groups of 40 students per teacher. If 81 students were enrolled, however, they would be placed in three classes of 27 students each. The rule thus generated sharp discontinuities in class sizes within schools, which, the authors argue, allow them to provide causal evidence of a boost in fifth grade math and reading scores as a result of smaller class size. a. What research design is implied by the authors’ use of Maimonides’ rule to estimate the causal…arrow_forwardA significant interaction was found between depression, social support and GPA. These results should be considered with caution and further research should be done to better understand the relationships between these variables. Furthermore, those working with college students should consider the overall well-being of the student, as it is possible that their academic performance may not be affected by anxiety and/or depression. IS THIS A TYPE 1 ERROR OR A TYPE 2 ERROR IN RESEARCH?arrow_forwardWhich of the independent variables retains the strongest association with the number of children a respondent has when all other variables in the model are controlled?arrow_forward
- If there is a significant interaction for a CRD with a two-way treatment structure, then one should assess whether the main effects are significant. True or falsearrow_forwardOh no! Our expert couldn't answer your question. Don't worry! We won't leave you hanging. Plus, we're giving you back one question for the inconvenience. Here's what the expert had to say: Hi and thanks for your question! Unfortunately, per Question 1b The St. Lucian Government is interested in predicting the number of weekly riders on the public buses using the following variables: • Price of bus trips per week The population in the city The monthly income of riders Average rate to park your personal vehicle City Number of weekly riders Price per week Population of city Monthly income of riders Average parking rates per month 1 192,000 $15 1,800,000 $5,800 $50 2 190,400 $15 1,790,000 $6,200 $50 3 191,200 $15 1,780,000 $6,400 $60 4 177,600 $25 1,778,000 $6,500 $60 5 176,800 $25 1,750,000 $6,550 $60 6 178,400 $25 1,740,000 $6,580 $70 7 180,800 $25…arrow_forwardThe accompanying data file contains salary data (in $1,000s) for 30 college-educated men with their respective BMI and a dummy variable that represents 1 for a white man and O otherwise. Model 1 predicts Salary using BMI and White as predictor variables. Model 2 includes BMI and White along with an interaction between the two. Click here for the Excel Data File a-1. Use the holdout method to compare the predictability of the models using the first 20 observations for training and the remaining 10 observations for validation. Report the estimates of Models 1 and 2 derived from the training set. Note: Negative values should be indicated by a minus sign. Round your answers to 2 decimal places. Predictor Variable Model 1 (No interaction) Model 2 (Interaction) Constant BMI White BMI X White RMSE a-2. Calculate the RMSE of the two models in the validation set. Note: Do not round Intermediate calculations and round your final answers to 2 decimal places. Model 1 -0.96 a-3. Which model is…arrow_forward
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