Research Paper Components: Which one of the following types of variables is most difficult to evaluate objectively in a true experiment? Explain why you think that (See instructions below). a) Dependent variable b) Independent variable c) Confounding v

1. Which one of the following types of variables is most difficult to evaluate objectively in a true experiment? Explain why you think that (See instructions below).

a) Dependent variable

b) Independent variable

c) Confounding variable

d) Extraneous variable

e) None of the above

Instructions: Make selection, provide a concept definition (text), and support your opinion on the selection with an example from research that illustrates the concept. Do so in a maximum of 250 words. Use credible and peer reviewed sources. Credible sources include course materials, University Library research that is peer reviewed, and Internet sites ending in .edu or .gov with with the one exception of research pulled from the www.apa.org site. If research is pulled from the APA site, use the www.apa.org

2. GIVE FEEDBACK ON THE FOUR PARAGRAPHS LISTED BELOW 150-200 WORDS

1.The variable that is the most difficult to evaluate objectively in a true experiment would be Extraneous variable. According to Cozby & Bates (2015), "It would be impossible to know whether participants that were participating in an aerobics class or those watching aerobics on video, would have a better mood due to what they were doing" (p.162). With extraneous variable there are so many other factors that come into play such as; does either room have more doors, air conditioning, heating, windows, ect. Those things actually can change the response of each group making the data collected unreliable. In an article I found regarding women who are pregnant and using cocaine, the study that was done took place over quite a few years. According to Richardson & Day (1999), "One of the issues that were identified was the failure to control adequately for extraneous variables" (p.234). The researchers realized that some of the studies were inadequate and that most of the time information was not interpreted correctly to the client or their providers. The lack of communication caused further issues and endangered some of those pregnancies. Since the study on prenatal cocaine exposure was performed over such a lengthy period of time it is hard to make sure that there will not be anything extraneous that would have an effect on the study. Without trying to eliminate those extraneous variables the study becomes compromised and the data does not appear to be as relevant as other studies.

Reference:

Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research (12th ed.). New York, NY: McGraw-Hill.

Richardson, G. A., & Day, N. L. (1999). Studies of prenatal cocaine exposure: Assessing the influence of extraneous variables. Journal of Drug Issues, 29(2), 225-236. Retrieved from https://search-proquest-com.contentproxy.phoenix.edu/docview/208833439?accountid=458

2.Independent variables are tested to see of the have an effect on the dependent variable, which is why the extraneous variables (not intentionally studied) are known to be undesirable variables, and sometimes they are difficult for the researcher to control (Cozby, 2015). As an example, since the extraneous variable is not a variable of interest, they may still influence an outcome of a research study or experiment. According to Losen & Oyinalde (2014), the extraneous variable has its positives as it can be used to provide alternative explanations when coming to the experiments effects, but it must be controlled for and not take the place of the independent variable, which has to determine the actual effects. References:Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research (12th ed.). New York, NY: McGraw-Hill. Losen, A., & Oyinalde, A.O (2014) Extraneous Effects of Race, Gender, and Race-Gender Homo- and Heterophily Conditions on Data Quality 4(1) Directory of Open Access Journals DOI: 10.1177/2158244014525418

3.The variable that I think is most difficult to evaluate is the confounding variable. In our reading from chapter four they talk in depth about the confounding variable. They explain the third variable that is hard to get a read on. According to Cozby & Bates (2015) the confounding variable is what we call the third when an uncontrolled one is operating. When a third variable is operating it can cause a huge problem since it can introduce an alternative explanation which can reduce the overall validity of the study (Cozby & Bates, 2015). If two variables are confounded they are so intertwined that you will not be able to determine which of the variables is operating in a situation (Cozby & Bates, 2015). The example they give is about how exercise can cause a reduction in anxiety but when they input income that can cause the third variable (Cozby & Bates, 2015). The third variable which can be extraneous to the two variables being studied. There can be any number of third variables that may be responsible a relationship between two variables (Cozby & Bates, 2015).

Cozby, P. C., & Bates, S. C. (2015). Methods in Behavioral Research (12th ed.). New York, NY: McGraw-Hill.

4.The confounding variables can be difficult to control by the researcher (Cozby & Bates, 2015). In fact, it is said that researchers do fail to control it, as to eliminate the underlying problems the human judgment is necessary. The confounding variable also makes it difficult to find a linkage between treatments and the outcomes. According to Brodt, Dettori, Skelly (2012), Confounding happens when the effects are mixed, where the confounding factors may provide false demonstrations which show to be apparently associated with the treatments and or outcomes, when in reality there is not an association. When coming to exposures in the medical field, treatment group observations, consideration is recommended when coming to the effect truly due to exposure or alternative explanations, there for appropriate methods have to be used for adjustments, where the human judgment is required.

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RESEARCH PAPER: INCREASING THE NUMBER OF INDEPENDENT VARIABLES: FACTORIAL DESIGNS

Scenario: Researchers provided both content of class and gender of instructor within vignettes for 2 classes of students that were manipulated by the experimenter. For example, the content manipulated in the two different classes was either counseling or research methods. The gender of the instructor manipulated in the vignettes was either male or female. In the research results, the main effects indicated instructor gender and course content were not statistically significant.

Answer each question in a maximum of 250 words excluding citations: Which of the following research designs is the above experimenter using? Why do you say that? What is the strength of the design that you selected from the list below?

a) Inverted U

b) 2 x 2

c) IV x PV

d) None of the above (What alternative design then?)

Instruction: Provide a definition of your concept design from our text then, discuss support for your selection including an example from research that illustrates your point. Do so with a maximum of 250 words excluding citations.

Complex Experimental Designs

LEARNING OBJECTIVES

· Define factorial design and discuss reasons a researcher would use this design.

· Describe the information provided by main effects and interaction effects in a factorial design.

· Describe an IV × PV design.

· Discuss the role of simple main effects in interpreting interactions.

· Compare the assignment of participants in an independent groups design, a repeated measures design, and a mixed factorial design.

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THUS FAR WE HAVE FOCUSED PRIMARILY ON THE SIMPLEST EXPERIMENTAL DESIGN, IN WHICH ONE INDEPENDENT VARIABLE IS MANIPULATED AND ONE DEPENDENT VARIABLE IS MEASURED. However, researchers often investigate problems that demand more complicated designs. These complex experimental designs are the subject of this chapter.

We begin by discussing the idea of increasing the number of levels of an independent variable in an experiment. Then, we describe experiments that expand the number and types of independent variables. These changes impact the complexity of an experiment.

INCREASING THE NUMBER OF LEVELS OF AN INDEPENDENT VARIABLE

In the simplest experimental design, there are only two levels of the independent variable. However, a researcher might want to design an experiment with three or more levels for several reasons. First, a design with only two levels of one independent variable cannot provide very much information about the exact form of the relationship between the independent and dependent variables. For example, Figure 10.1 is based on the outcome of an experiment on the relationship between amount of “mental practice” and performance on a motor task: dart throwing score (Kremer, Spittle, McNeil, & Shinners, 2009). Mental practice consisted of imagining practice throws prior to an actual dart throwing task. Does mental practice improve dart performance? The solid line describes the results when only two levels were used—no mental practice throws and 100 mental practice throws. Because there are only two levels, the relationship can be described only with a straight line. We do not know what the relationship would be if other practice amounts were included as separate levels of the independent variable. The broken line in Figure 10.1 shows the results when 25, 50, and 75 mental practice throws are also included. This result is a more accurate description of the relationship between amount of mental practice and performance. The amount of practice is very effective in increasing performance up to a point, after which further practice is not helpful. This type of relationship is termed a positive monotonic relationship; there is a positive relationship between the variables, but it is not a strictly positive linear relationship. An experiment with only two levels cannot yield such exact information.

FIGURE 10.1

Linear versus positive monotonic functions

Note: Data based on an experiment conducted by Kremer, Spittle, McNeil, and Shinners (2009); that experiment did not include a 75-practice-throws condition.

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FIGURE 10.2

Curvilinear relationship

Note: At least three levels of the independent variable are required to show curvilinear relationships.

Recall from Chapter 4 that variables are sometimes related in a curvilinear or nonmonotonic fashion; that is, the direction of relationship changes. Figure 10.2 shows an example of a curvilinear relationship; this particular form is called an inverted-U because the wide range of levels of the independent variable produces an inverted U shape (recall our discussion of inverted-U relationships in Chapter 4). An experimental design with only two levels of the independent variable cannot detect curvilinear relationships between variables. If a curvilinear relationship is predicted, at least three levels must be used. As Figure 10.2 shows, if only levels 1 and 3 of the independent variable had been used, no relationship between the variables would have been detected. Many such curvilinear relationships exist in psychology. The relationship between fear arousal and attitude change is one example—we can be scared into changing an attitude, but if we think that a message is “over the top,” attitude change does not occur. In other words, increasing the amount of fear aroused by a persuasive message increases attitude change up to a moderate level of fear; further increases in fear arousal actually reduce attitude change.

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Methods in Behavioral Research: Sociology and Psychology Essays

Week Four Homework Exercise

PSYCH/610 Version 2

1

Week Four Homework Exercise

Answer the following questions, covering material from Ch 8–10 of Methods in Behavioral Research:

1. What is a confounding variable and why do researchers try to eliminate confounding variables? Provide two examples of confounding variables.

2. What are the advantages and disadvantages of posttest only design and pretest-posttest design?

3. What is meant by sensitivity of a dependent variable?

4. What are the differences between an independent groups design and a repeated measures design?

5. How does an experimenter’s expectations and participant expectations affect outcomes?

6. Provide an example of a factorial design. What are the key features of a factorial design? What are the advantages of a factorial design?

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Research Proposal Preparation Using A Research Evaluation Worksheet

Research Evaluation Worksheet

PSYCH/610 Version 2

1

Select a research article of interest to you, preferably related to your Research Proposal, and use the Research Evaluation Worksheet to analyze the article. You can use this information to help you form the literature review section of your research proposal.

Research Evaluation Worksheet

Title:

Full Article Reference (APA style):

Abstract

Introduction

a. Is the need for the study clearly stated in the introduction? Explain by using information presented in the literature review.

b. What is the research hypothesis or question?

c. What are the variables of interest (independent and dependent variables)?

d. How are the variables operationally defined?

Method

a. Sample Size (Total): ________________ Size Per Group/Cell: _______________

b. Were the methods and procedures described so that the study could be replicated without further information? What information, if any, would you need to replicate or reproduce this study?

Participants

a. How were participants selected and recruited? b. Were subjects randomly selected? c. Were there any biases in sampling? Explain

d. Were the samples appropriate for the population to which the researcher wished to generalize?

e. What are the characteristics of the sample populations?

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