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ExampleResearchProposal-APA7th.pdf

ExampleResearchProposal-APA7th.pdf

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Example Research Proposal

Pamela Murphy

PSY 326 Research Methods

Instructor’s Name

Date Submitted

NOTE: The details in this example research proposal are based on a published study which I co-authored with Charles B. Hodges and my doctoral dissertation, both in 2009. Portions of the text are excerpted from the published article (Hodges & Murphy, 2009) and the dissertation (Murphy, 2009).

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Example Research Proposal

Introduction

The concept of self-efficacy was introduced nearly 40 years ago. “Perceived self-efficacy

refers to beliefs in one’s capabilities to organize and execute the courses of action required to

produce given attainments” (Bandura, 1977, p. 3). Self-efficacy has been identified as an

important construct for academic achievement in traditional learning environments for at least

two decades. Zimmerman and Schunk (2003) go so far as to say that “the predictive power of

self-efficacy beliefs on students’ academic functioning has been extensively verified” (p. 446).

Its importance has been noted consistently through all levels of the educational process, with

various student populations, and in varied domains of learning.

While learner self-efficacy has a well-established literature base in the context of

traditional learning environments, self-efficacy research related to learners in online and other

non-traditional learning environments is relatively new. Hodges (2008a) has called for

researchers to explore self-efficacy in online learning environments. Additionally, in terms of

students’ self-efficacy beliefs toward academic achievement, “there have been few efforts to

investigate the sources underlying these self-beliefs” (Usher, 2009, p. 275). The purpose of the

proposed study is to investigate the relative strength of the four traditionally proposed sources of

self-efficacy beliefs of students enrolled in a technology-intensive asynchronous college math

college.

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Literature Review

Self-efficacy beliefs have been found to be significant contributors to motivation and

performance in academic achievement (Multon, Brown, & Lent, 1991), group functioning

(Gully, Incalcaterra, Joshi, & Beaubien, 2002; Stajkovic & Luthans, 1998), health (Holden,

1991), and sports performance (Moritz, Feltz, Fahrbach, & Mack, 2000). Research revealing the

connection between self-efficacy and mathematics, the context of the proposed study, includes

many cultures and levels of education (Malpass, O’Neil, & Hocevar, 1999; Pietsch, Walker, &

Chapman, 2003; Randhawa, Beamer, & Lundberg, 1993; Stevens, Olivarez, Lan, & Tallent-

Runnels, 2004) and continues to the present (Usher, 2009).

Sources of Self-Efficacy

Albert Bandura’s (1977) introduction of self-efficacy theory included the proposition that

self-efficacy is derived from four principal sources: mastery experiences, vicarious experience,

social persuasion, and physiological/affective states. These four areas are generally accepted in

the literature as core elements in the development of self-efficacy beliefs, but an ordering of the

importance of each of these sources is unsettled.

Mastery Experiences. Mastery experiences refer to previous, successful experiences a

learner has had performing a task. Successes build positive self-efficacy beliefs and failures

undermine self-efficacy. If failures are experienced before a firm positive belief in one’s self-

efficacy is formed, the creation of positive self-efficacy beliefs is more difficult.

Vicarious Experience. Vicarious experience refers to one’s observation of a role model

performing a task. Knowledge of how others have performed a similar task helps one determine

whether or not a performance should be judged a success or failure. Surpassing the performances

of others increases self-efficacy and falling below others’ performances lowers self-efficacy.

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Note the importance of the selection of individuals for comparison. Self-efficacy beliefs will

vary depending on the abilities of those chosen for comparison, thus, models for comparison

should be selected carefully (Wood, 1989).

Social Persuasion. Social persuasion is commonly used due to the ease with which it can

be dispensed. The believability of the persuader(s) is important in the use of social persuasion.

The receiver must view the persuader as competent to provide meaningful and accurate

feedback. Bandura (1997) cautions that verbal persuasion consists of more than flippant, off-

hand comments of encouragement. Unrealistic comments from the persuader may mislead the

receiver, which may decrease self-efficacy and diminish the belief in the persuader as one

competent to evaluate the performance. “Skilled efficacy builders encourage people to measure

their successes in terms of self-improvement rather than in terms of triumphs over others”

(Bandura, 1997, p. 106).

Physiological/Affective States. Stress, emotion, mood, pain, and fatigue are all

interpreted when making judgments regarding self-efficacy. For example, someone may have

prepared well for an exam, but upon learning of some unfortunate news, stress may reduce

concentration, thus impacting performance on the exam. In general, success is expected when

one is not in a state of aversive arousal (Bandura, 1997).

Usher and Pajares (2006) summarize the inconsistent findings regarding the relative

strength of each self-efficacy source well. They follow with the proposition that “exploring the

predictive value of the sources of students’ academic self-efficacy beliefs and determining

whether this prediction varies as a function of group membership such as gender, academic

ability, and race/ethnicity is a matter of import” (p. 130).

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Methods

Design

The proposed study is quantitative in nature and will use a survey research design

(Newman, 2016). Survey research falls into the non-experimental category of research designs.

The survey questions use mostly ordinal scales and will result in numeric scores summarizing the

extent of use of each source of self-efficacy beliefs as well as a score representing the level of

self-efficacy held by each student in relation to the ability to learn math in an asynchronous

learning environment.

Participants

Approximately 300 students in an asynchronous college algebra course offered at a large,

state supported university in the mid-Atlantic region of the United States will be invited to

participate in a survey. This is a convenience sample, and participation is voluntary, so the final

sample size may be considerably smaller than the number of students invited. The course is

delivered using an emporium format (Twigg, 2003) which is technology intensive. The students

enrolled in the course tend to be engaged in academic majors that are not math-intensive. They

may have a high degree of math anxiety or at least some negative feelings toward their math

abilities. In addition, the emporium model may be an unfamiliar concept for them.

Procedure/Measures

This course is offered through the Math Emporium and has no traditional class meetings.

After a brief, face-to-face, orientation meeting, students complete the course asynchronously.

There are weekly deadlines for quizzes, and proctored tests are administered periodically.

Students prepare for the quizzes and tests by taking advantage of various technology resources

available to them online. Lesson pages serve as an online textbook for the course, short

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streaming video lectures are available on most topics, and an unlimited number of practice

quizzes are available. For students who desire it, face-to-face interactions with assistants in the

computer lab are available several hours each week. No appointment is needed for the face-to-

face assistance.

At the conclusion of the course, data will be collected using a web-based survey tool.

Students who provide informed consent to participate will be given an ID number and survey

access information. They may access the survey either in the Math Emporium or offsite through

the internet. Specific instruments to be used are the Self-Efficacy for Learning Mathematics

Asynchronously (SELMA) survey (Hodges, 2008b), a demographics survey, and the Sources of

Mathematics Self-Efficacy (SMSE) scale (Lent, Lopez, & Bieschke, 1991).

The SELMA survey is a 25-question survey constructed for use in college algebra and

trigonometry courses offered in an emporium model. A validation study showed an internal

consistency Cronbach’s alpha value of 0.87 (Hodges, 2008b) which is greater than the 0.80

minimum level recommended by Gable and Wolf (1993) for instruments in the affective domain.

The SMSE scale consists of four 10-question subscales designed to measure each of the

four sources of self-efficacy: mastery, vicarious experiences, social persuasion, and

affective/physiological state. In a validation study of the SMSE, Lent et al. (1991) reported

internal consistencies of 0.86 for mastery, 0.56 for vicarious, 0.74 for persuasion, and 0.90 for

affective/physiological arousal.

Data Analysis

To investigate the relative strength of the four traditional sources of self-efficacy beliefs

of students in an asynchronous math course, analysis of variance (ANOVA) and multiple

regression will be used. Scores from each of the four subscales of the SMSE will be used as

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predictors of the SELMA score. Bivariate correlations will also be examined. Significant

correlations among the predictor variables may present a problem of multicollinearity. If

necessary, additional statistical tests such as ridge regression (Joe & Mendoza, 1989; Kidwell &

Brown, 1982) will be applied to solve this problem.

Ethical Issues

Participation in the survey will be strictly voluntary, and will not be tied to evaluation of

the student’s performance in the course in any way. As a non-experimental survey study, no

deception will be used. Signed informed consent will be obtained from those who wish to

participate. Those who agree to participate may withdraw from the study at any time without any

type of penalty.

Confidentiality of participants will be protected by the assignment of ID numbers to be

used on the survey documents instead of names or any other type of identifying information. A

single copy of the list matching the ID numbers with participants’ names will be kept in a secure,

locked location for a period of three years after the completion of the study. After three years, the

list will be destroyed in accordance with the instructions of the Institutional Review Board

(IRB).

As a token of appreciation, all participants will be entered into a drawing for an Amazon

gift card. The proposed amount of the gift card, subject to IRB approval, is $25. University

facilities, including the computer lab known as the Math Emporium, its computers and a survey

software program, will be used if this study is approved. This project will not receive any

external funding from commercial or other sources, and no conflicts of interest are reported by

the researchers.

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Conclusion

Self-efficacy and its relationship to academic achievement in asynchronous online

learning environments are only recently beginning to be researched (Hodges, 2008a). Given the

growing prominence of asynchronous online learning, it is essential that we understand what role

constructs such as self-efficacy play in these learning environments. The proposed study will

address this need by using a survey research design. The surveys will provide data on the four

sources of self-efficacy which will serve as predictors of students’ self-efficacy for learning

mathematics in an asynchronous online setting. A multiple regression model using the four

predictors with the SELMA survey score as the dependent variable will indicate how much each

source contributes to self-efficacy.

The results of this study are expected to be important to instructional designers and

educational practitioners who either currently use or are considering using an emporium model,

as they will give indications of which elements of the asynchronous course design should be

emphasized to best promote students’ self-efficacy relating to the subject matter. An expedited

review of this proposal by the IRB is requested for approval to begin this research as soon as

possible.

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References

Allison, P. D. (1999). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press.

Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change.

Psychological Review, 84(2), 191-215.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and

Company.

Gable, R. K., & Wolf, M. B. (1993). Instrument development in the affective domain: Measuring

attitudes and values in corporate and school settings, 2nd ed. Boston: Kluwer Academic

Publishers.

Gully, S. M., Incalcaterra, K A., Joshi, A., & Beaubien, J. M. (2002). A meta-analysis of team-

efficacy, potency, and performance: Interdependence and level of analysis as moderators

of observed relationships. Journal of Applied Psychology, 87, 819-832.

Hodges, C. B. (2008a). Self-efficacy in the context of online learning environments: A review of

the literature and directions for research. Performance Improvement Quarterly, 20(3-4),

7-25.

Hodges, C. B. (2008b). Self-efficacy, motivational email, and achievement in an asynchronous

math course. Journal of Computers in Mathematics and Science Teaching, 27(3), 265-

285.

Hodges, C. B., & Murphy, P. F. (2009). Sources of self-efficacy beliefs of students in a

technology-intensive asynchronous college algebra course. Internet and Higher

Education, 12(2), 93-97. doi:10.1016/j.iheduc.2009.06.005.

Holden, G. (1991). The relationship of self-efficacy appraisals to subsequent health-related

outcomes: A meta-analysis. Social Work in Health Care, 16, 53-93.

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Howell, D. C. (2002). Statistical methods for psychology, 5th ed. Pacific Grove, CA: Duxbury.

Joe, G. W., & Mendoza, J. L. (1989). The internal correlation: Its applications in statistics and

psychometrics. Journal of Educational Statistics, 14(3), 211-226.

Kidwell, J. S., & Brown, L. H. (1982). Ridge regression as a technique for analyzing models

with multicollinearity. Journal of Marriage and the Family, 44(2), 287-299.

Lent, R. W., Lopez, F. G., & Bieschke, K. J. (1991). Mathematics self-efficacy: Sources and

relation to science-based career choice. Journal of Counseling Psychology, 38(4), 424-

430.

Malpass, J. R., O’Neil, H. F., & Hocevar, D. (1999). Self-regulation, goal orientation, self-

efficacy, worry, and high-stakes math achievement for mathematically gifted high school

students. Roeper Review, 21, 281-295.

Moritz, S. E., Feltz, D. L., Fahrbach, K. R., & Mack, D. E. (2000). The relation of self-efficacy

measures to sport performance: A meta-analytic review. Research Quarterly for Exercise

and Sport, 71, 280-294.

Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic

outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38(1), 30-

38.

Murphy, P. F. (2009). Relationships of parenting practices, independent learning, achievement,

and family structure (Doctoral dissertation). Virginia Tech, Blacksburg, VA. Retrieved

from http://scholar.lib.vt.edu/theses/available/etd-04022009-174950/

Newman, M. (2016). Research methods in psychology, 2nd edition. San Diego, CA: Bridgepoint

Education.

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Pietsch, J., Walker, R., & Chapman, E. (2003). The relationship among self-concept, self-

efficacy, and performance in mathematics during secondary school. Journal of

Educational Psychology, 95, 589-603.

Randhawa, B. S., Beamer, J. E., & Lundberg, I. (1993). Role of mathematics self-efficacy in the

structural model of mathematics achievement. Journal of Educational Psychology, 85(1),

41-48.

Stajkovic, A. D., & Luthans, F. (1998). Self-efficacy and work-related performance: A meta-

analysis. Psychological Bulletin, 124, 240-261.

Stevens, T., Olivarez, A. J., Lan, W. Y., & Tallent-Runnels, M. K. (2004). Role of mathematics

self-efficacy and motivation in mathematics performance across ethnicity. Journal of

Educational Research, 97(4), 208-221.

Twigg, C. A. (2003). Improving learning and reducing costs: New models for online learning.

EDUCAUSE Review (September/October), 28-38.

Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A

qualitative investigation. American Educational Research Journal, 46(1), 275-314.

Usher, E. L., & Pajares, F. (2006). Sources of academic and self-regulatory efficacy beliefs of

entering middle school students. Contemporary Educational Psychology, 31(2), 125-141.

Wood, J. V. (1989). Theory and research concerning social comparison of personal attributes.

Psychological Bulletin, 106(2), 231-248.

Zimmerman, B. J., & Schunk, D. H. (2003). Albert Bandura: The scholar and his contributions to

educational psychology. In B. J. Zimmerman, & D. H. Schunk (Eds.), Educational

psychology: A century of contributions (pp. 431-457). Mahwah, NJ: L. Erlbaum

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