Assignment 4 Population And Sample Instructions & Reading Material

Introduction

Developing the research topic required identifying a population. Identifying and defining the population, along with sampling the population, will be the focus of this assessment. Defining the population tends to be general, as it relates to the research topic. For example, if the researcher is interested in studying a population with mild traumatic brain injury (mTBI), the researcher will need to define this population: all people who have had a mild TBI. A sample is a specific group of the general population that is accessible to the researcher. It should also connect to the research topic—for example, male college athletes who play football at state schools across the United States.

Sampling design is needed to identify how the sample will be accessed. The type of sampling reveals how the sample will be selected. Page 150 of the Creswell text provides definitions and descriptions of the population, sampling design, and types of sampling. The population and sample must be addressed in the concept paper under the methodology. Use scholarly literature to define and describe the population. Identify the sampling design and type of sampling. Explain why this sampling approach is fitting.

Overview

Complete a draft of your population and sample. Complete the Population and Sample sections of the 
Concept Paper Template [DOCX]
. Describe the population in one paragraph (5–7 sentences). Include characteristics of the population that make the population a focus for research. Describe the sample, sampling design, and sampling type, sample size, in another paragraph (5–7 sentences). Use the course text to guide your selection and to provide a rationale for your choices.

Instructions

· Discuss the setting, general population, target population, and study sample. The discussion of the sample includes the research terminology specific to the type of sampling for the study.

· When describing the sample size, provide evidence that the sample size is adequate for the quantitative research design.

· Consult the Concept Paper Template for additional guidance.

· The population and sample assessment should be one page, not including references.

Competencies Measured

By successfully completing this assessment, you will demonstrate your proficiency in the course competencies through the following assessment scoring guide criteria:

· Competency 4: Embrace, respect, and respond to individual differences and diversity in the practice of psychology.

. Describe the population from which the sample will be taken that represents a sampling of the general population in a way that respects diversity, including race, ethnicity, sexual orientation, age, physical ability, and gender.

. Describe the sample that would come from the population.

· Competency 5: Communicate psychological concepts effectively using the professional standards of the discipline.

. Convey purpose, in an appropriate tone and style, incorporating supporting evidence and adhering to organizational, professional, and scholarly writing standards.

· Competency 6: Communicate in a manner that is scholarly, professional, and consistent with expectations for members of the psychological professions.

. Exhibit proficiency in writing and use of APA (7th edition) style.

Chapter 8 Quantitative Methods

We turn now from the introduction, the purpose, and the questions and hypotheses to the method section of a proposal. This chapter presents essential steps in designing quantitative methods for a research proposal or study, with specific focus on survey and experimental designs. These designs reflect postpositivist philosophical assumptions, as discussed in 
Chapter 1
. For example, determinism suggests that examining the relationships between and among variables is central to answering questions and hypotheses through surveys and experiments. In one case, a researcher might be interested in evaluating whether playing violent video games is associated with higher rates of playground aggression in kids, which is a correlational hypothesis that could be evaluated in a survey design. In another case, a researcher might be interested in evaluating whether violent video game playing causes aggressive behavior, which is a causal hypothesis that is best evaluated by a true experiment. In each case, these quantitative approaches focus on carefully measuring (or experimentally manipulating) a parsimonious set of variables to answer theory-guided research questions and hypotheses. In this chapter, the focus is on the essential components of a method section in proposals for a survey or experimental study.

Defining Surveys and Experiments

A survey design provides a quantitative description of trends, attitudes, and opinions of a population, or tests for associations among variables of a population, by studying a sample of that population. Survey designs help researchers answer three types of questions: (a) descriptive questions (e.g., What percentage of practicing nurses support the provision of hospital abortion services?); (b) questions about the relationships between variables (e.g., Is there a positive association between endorsement of hospital abortion services and support for implementing hospice care among nurses?); or in cases where a survey design is repeated over time in a longitudinal study; (c) questions about predictive relationships between variables over time (e.g., Does Time 1 endorsement of support for hospital abortion services predict greater Time 2 burnout in nurses?).

An experimental design systematically manipulates one or more variables in order to evaluate how this manipulation impacts an outcome (or outcomes) of interest. Importantly, an experiment isolates the effects of this manipulation by holding all other variables constant. When one group receives a treatment and the other group does not (which is a manipulated variable of interest), the experimenter can isolate whether the treatment and not other factors influence the outcome. For example, a sample of nurses could be randomly assigned to a 3-week expressive writing program (where they write about their deepest thoughts and feelings) or a matched 3-week control writing program (writing about the facts of their daily morning routine) to evaluate whether this expressive writing manipulation reduces job burnout in the months following the program (i.e., the writing condition is the manipulated variable of interest, and job burnout is the outcome of interest). Whether a quantitative study employs a survey or experimental design, both approaches share a common goal of helping the researcher make inferences about relationships among variables, and how the sample results may generalize to a broader population of interest (e.g., all nurses in the community).

Components of a Survey Study Method Plan

The design of a survey method plan follows a standard format. Numerous examples of this format appear in scholarly journals, and these examples provide useful models. The following sections detail typical components. In preparing to design these components into a proposal, consider the questions on the checklist shown in 
Table 8.1
 as a general guide.

The Survey Design

The first parts of the survey method plan section can introduce readers to the basic purpose and rationale for survey research. Begin the section by describing the rationale for the design. Specifically:

· Identify the purpose of survey research. The primary purpose is to answer a question (or questions) about variables of interest to you. A sample purpose statement could read: “The primary purpose of this study is to empirically evaluate whether the number of overtime hours worked predicts subsequent burnout symptoms in a sample of emergency room nurses.”

· Indicate why a survey method is the preferred type of approach for this study. In this rationale, it can be beneficial to acknowledge the advantages of survey designs, such as the economy of the design, rapid turnaround in data collection, and constraints that preclude you from pursuing other designs (e.g., “An experimental design was not adopted to look at the relationship between overtime hours worked and burnout symptoms because it would be prohibitively difficult, and potentially unethical, to randomly assign nurses to work different amounts of overtime hours.”).

· Indicate whether the survey will be cross-sectional—with the data collected at one point in time—or whether it will be longitudinal—with data collected over time.

· Specify the form of data collection. Fowler (2014) identified the following types: mail, telephone, the Internet, personal interviews, or group administration (see also Fink, 2016; Krueger & Casey, 2014). Using an Internet survey and administering it online has been discussed extensively in the literature (Nesbary, 2000; Sue & Ritter, 2012). Regardless of the form of data collection, provide a rationale for the procedure, using arguments based on its strengths and weaknesses, costs, data availability, and convenience.

The Population and Sample

In the method section, follow the type of design with characteristics of the population and the sampling procedure. Methodologists have written excellent discussions about the underlying logic of sampling theory (e.g., Babbie, 2015; Fowler, 2014). Here are essential aspects of the population and sample to describe in a research plan:

· The population. Identify the population in the study. Also state the size of this population, if size can be determined, and the means of identifying individuals in the population. Questions of access arise here, and the researcher might refer to availability of sampling frames—mail or published lists—of potential respondents in the population.

· Sampling design. Identify whether the sampling design for this population is single stage or multistage (called clustering). Cluster sampling is ideal when it is impossible or impractical to compile a list of the elements composing the population (Babbie, 2015). A single-stage sampling procedure is one in which the researcher has access to names in the population and can sample the people (or other elements) directly. In a multistage or clustering procedure, the researcher first identifies clusters (groups or organizations), obtains names of individuals within those clusters, and then samples within them.

· Type of sampling. Identify and discuss the selection process for participants in your sample. Ideally you aim to draw a random sample, in which each individual in the population has an equal probability of being selected (a systematic or probabilistic sample). But in many cases it may be quite difficult (or impossible) to get a random sample of participants. Alternatively, a systematic sample can have precision-equivalent random sampling (Fowler, 2014). In this approach, you choose a random start on a list and select every X numbered person on the list. The X number is based on a fraction determined by the number of people on the list and the number that are to be selected on the list (e.g., 1 out of every 80th person). Finally, less desirable, but often used, is a nonprobability sample (or convenience sample), in which respondents are chosen based on their convenience and availability.

· Stratification. Identify whether the study will involve stratification of the population before selecting the sample. This requires that characteristics of the population members be known so that the population can be stratified first before selecting the sample (Fowler, 2014). Stratification means that specific characteristics of individuals (e.g., gender—females and males) are represented in the sample and the sample reflects the true proportion in the population of individuals with certain characteristics. When randomly selecting people from a population, these characteristics may or may not be present in the sample in the same proportions as in the population; stratification ensures their representation. Also identify the characteristics used in stratifying the population (e.g., gender, income levels, education). Within each stratum, identify whether the sample contains individuals with the characteristic in the same proportion as the characteristic appears in the entire population.

· Sample size determination. Indicate the number of people in the sample and the procedures used to compute this number. Sample size determination is at its core a tradeoff: A larger sample will provide more accuracy in the inferences made, but recruiting more participants is time consuming and costly. In survey research, investigators sometimes choose a sample size based on selecting a fraction of the population (say, 10%) or selecting a sample size that is typical based on past studies. These approaches are not optimal; instead sample size determination should be based on your analysis plans (Fowler, 2014).

· Power analysis. If your analysis plan consists of detecting a significant association between variables of interest, a power analysis can help you estimate a target sample size. Many free online and commercially available power analysis calculators are available (e.g., G*Power; Faul, Erdfelder, Lang, & Buchner, 2007; Faul, Erdfelder, Buchner, & Lang 2009). The input values for a formal power analysis will depend on the questions you aim to address in your survey design study (for a helpful resource, see Kraemer & Blasey, 2016). As one example, if you aim to conduct a cross-sectional study measuring the correlation between the number of overtime hours worked and burnout symptoms in a sample of emergency room nurses, you can estimate the sample size required to determine whether your correlation significantly differs from zero (e.g., one possible hypothesis is that there will be a significant positive association between number of hours worked and emotional exhaustion burnout symptoms). This power analysis requires just three pieces of information:

0. An estimate of the size of correlation (r). A common approach for generating this estimate is to find similar studies that have reported the size of the correlation between hours worked and burnout symptoms. This simple task can often be difficult, either because there are no published studies looking at this association or because suitable published studies do not report a correlation coefficient. One tip: In cases where a published report measures variables of interest to you, one option is to contact the study authors asking them to kindly provide the correlation analysis result from their dataset, for your power analysis.

1. A two-tailed alpha value (α). This value is called the Type I error rate and refers to the risk we want to take in saying we have a real non-zero correlation when in fact this effect is not real (and determined by chance), that is, a false positive effect. A commonly accepted alpha value is .05, which refers to a 5% probability (5/100) that we are comfortable making a Type I error, such that 5% of the time we will say that there’s a significant (non-zero) relationship between number of hours worked and burnout symptoms when in fact this effect occurred by chance and is not real.

2. A beta value (β). This value is called the Type II error rate and refers to the risk we want to take in saying we do not have a significant effect when in fact there is a significant association, that is, a false negative effect. Researchers commonly try to balance the risks of making Type I versus Type II errors, with a commonly accepted beta value being .20. Power analysis calculators will commonly ask for estimated power, which refers to 1 − beta (1 − .20 = .80).

· You can then plug these numbers into a power analysis calculator to determine the sample size needed. If you assume that the estimated association is r = .25, with a two-tailed alpha value of .05 and a beta value of .20, the power analysis calculation indicates that you need at least 123 participants in the study you aim to conduct.

· To get some practice, try conducting this sample size determination power analysis. We used the G*Power software program (Faul et al., 2007; Faul et al., 2009), with the following input parameters:

. Test family: Exact

. Statistical test: Correlation: Bivariate normal model

. Type of power analysis: A priori: Compute required sample size

. Tails: Two

. Correlation ρ H1: .25

. α err prob: .05

. Power (1 – β err prob): .8

. Correlation ρ H0: 0

· This power analysis for sample size determination should be done during study planning prior to enrolling any participants. Many scientific journals now require researchers to report a power analysis for sample size determination in the Method section.

Instrumentation

As part of rigorous data collection, the proposal developer also provides detailed information about the actual survey instruments to be used in the study. Consider the following:

· Name the survey instruments used to collect data. Discuss whether you used an instrument designed for this research, a modified instrument, or an instrument developed by someone else. For example, if you aim to measure perceptions of stress over the last month, you could use the 10-item Perceived Stress Scale (PSS) (Cohen, Kamarck, & Mermelstein, 1983) as your stress perceptions instrument in your survey design. Many survey instruments, including the PSS, can be acquired and used for free as long as you cite the original source of the instrument. But in some cases, researchers have made the use of their instruments proprietary, requiring a fee for use. Instruments are increasingly being delivered through a multitude of online survey products now available (e.g., Qualtrics, Survey Monkey). Although these products can be costly, they also can be quite helpful for accelerating and improving the survey research process. For example, researchers can create their own surveys quickly using custom templates and post them on websites or e-mail them to participants to complete. These software programs facilitate data collection into organized spreadsheets for data analysis, reducing data entry errors and accelerating hypothesis testing.

· Validity of scores using the instrument. To use an existing instrument, describe the established validity of scores obtained from past use of the instrument. This means reporting efforts by authors to establish validity in quantitative research—whether you can draw meaningful and useful inferences from scores on the instruments. The three traditional forms of validity to look for are (a) content validity (Do the items measure the content they were intended to measure?), (b) predictive or concurrent validity (Do scores predict a criterion measure? Do results correlate with other results?), and (c) construct validity (Do items measure hypothetical constructs or concepts?). In more recent studies, construct validity has become the overriding objective in validity, and it has focused on whether the scores serve a useful purpose and have positive consequences when they are used in practice (Humbley & Zumbo, 1996). Establishing the validity of the scores in a survey helps researchers to identify whether an instrument might be a good one to use in survey research. This form of validity is different from identifying the threats to validity in experimental research, as discussed later in this chapter.

· Reliability of scores on the instrument. Also mention whether scores resulting from past use of the instrument demonstrate acceptable reliability. Reliability in this context refers to the consistency or repeatability of an instrument. The most important form of reliability for multi-item instruments is the instrument’s internal consistency—which is the degree to which sets of items on an instrument behave in the same way. This is important because your instrument scale items should be assessing the same underlying construct, so these items should have suitable intercorrelations. A scale’s internal consistency is quantified by a Cronbach’s alpha (α)value that ranges between 0 and 1, with optimal values ranging between .7 and .9. For example, the 10-item PSS has excellent internal consistency across many published reports, with the original source publication reporting internal consistency values of α = .84–.86 in three studies (Cohen, Kamarck, and Mermelstein, 1983). It can also be helpful to evaluate a second form of instrument reliability, its test-retest reliability. This form of reliability concerns whether the scale is reasonably stable over time with repeated administrations. When you modify an instrument or combine instruments in a study, the original validity and reliability may not hold for the new instrument, and it becomes important to establish validity and reliability during data analysis.

· Sample items. Include sample items from the instrument so that readers can see the actual items used. In an appendix to the proposal, attach sample items or the entire instrument (or instruments) used.

· Content of instrument. Indicate the major content sections in the instrument, such as the cover letter (Dillman, 2007, provides a useful list of items to include in cover letters), the items (e.g., demographics, attitudinal items, behavioral items, factual items), and the closing instructions. Also mention the type of scales used to measure the items on the instrument, such as continuous scales (e.g., strongly agree to strongly disagree) and categorical scales (e.g., yes/no, rank from highest to lowest importance).

· Pilot testing. Discuss plans for pilot testing or field-testing the survey and provide a rationale for these plans. This testing is important to establish the content validity of scores on an instrument; to provide an initial evaluation of the internal consistency of the items; and to improve questions, format, and instructions. Pilot testing all study materials also provides an opportunity to assess how long the study will take (and to identify potential concerns with participant fatigue). Indicate the number of people who will test the instrument and the plans to incorporate their comments into final instrument revisions.

· Administering the survey. For a mailed survey, identify steps for administering the survey and for following up to ensure a high response rate. Salant and Dillman (1994) suggested a four-phase administration process (see Dillman, 2007, for a similar three-phase process). The first mail-out is a short advance-notice letter to all members of the sample, and the second mail-out is the actual mail survey, distributed about 1 week after the advance-notice letter. The third mail-out consists of a postcard follow-up sent to all members of the sample 4 to 8 days after the initial questionnaire. The fourth mail-out, sent to all nonrespondents, consists of a personalized cover letter with a handwritten signature, the questionnaire, and a preaddressed return envelope with postage. Researchers send this fourth mail-out 3 weeks after the second mail-out. Thus, in total, the researcher concludes the administration period 4 weeks after its start, providing the returns meet project objectives.

Variables in the Study

Although readers of a proposal learn about the variables in purpose statements and research questions/hypotheses sections, it is useful in the method section to relate the variables to the specific questions or hypotheses on the instrument. One technique is to relate the variables, the research questions or hypotheses, and sample items on the survey instrument so that a reader can easily determine how the data collection connects to the variables and questions/hypotheses. Plan to include a table and a discussion that cross-reference the variables, the questions or hypotheses, and specific survey items. This procedure is especially helpful in dissertations in which investigators test large-scale models or multiple hypotheses. 
Table 8.2
 illustrates such a table using hypothetical data.

Data Analysis

In the proposal, present information about the computer programs used and the steps involved in analyzing the data. Websites contain detailed information about the various statistical analysis computer programs available. Some of the more frequently used programs are the following:

· IBM SPSS Statistics 24 for Windows and Mac (


www.spss.com


). The SPSS Grad Pack is an affordable, professional analysis program for students based on the professional version of the program, available from IBM.

· JMP (


www.jmp.com


). This is a popular software program available from SAS.

· Minitab Statistical Software 17 (minitab.com). This is an interactive software statistical package available from Minitab Inc.

· SYSTAT 13 (systatsoftware.com). This is a comprehensive interactive statistical package available from Systat Software, Inc.

· SAS/STAT (sas.com). This is a statistical program with tools as an integral component of the SAS system of products available from SAS Institute, Inc.

· Stata, release 14 (stata.com). This is a data analysis and statistics program available from StataCorp.

Online programs useful in simulating statistical concepts for statistical instruction can also be used, such as the Rice Virtual Lab in Statistics found at 
http://onlinestatbook.com/rvls.html
, or SAS Simulation Studio for JMP (
www.jmp.com
), which harnesses the power of simulation to model and analyze critical operational systems in such areas as health care, manufacturing, and transportation. The graphical user interface in SAS Simulation Studio for JMP requires no programming and provides a full set of tools for building, executing, and analyzing results of simulation models (Creswell & Guetterman, in press).

We recommend the following research tip—presenting data analysis plans as a series of steps so that a reader can see how one step leads to another:

Step 1. Report information about the number of participants in the sample who did and did not return the survey. A table with numbers and percentages describing respondents and nonrespondents is a useful tool to present this information.

Step 2. Discuss the method by which response bias will be determined. Response bias is the effect of nonresponses on survey estimates (Fowler, 2014). Bias means that if nonrespondents had responded, their responses would have substantially changed the overall results. Mention the procedures used to check for response bias, such as wave analysis or a respondent/nonrespondent analysis. In wave analysis, the researcher examines returns on select items week by week to determine if average responses change (Leslie, 1972). Based on the assumption that those who return surveys in the final weeks of the response period are nearly all nonrespondents, if the responses begin to change, a potential exists for response bias. An alternative check for response bias is to contact a few nonrespondents by phone and determine if their responses differ substantially from respondents. This constitutes a respondent-nonrespondent check for response bias.

Step 3. Discuss a plan to provide a descriptive analysis of data for all independent and dependent variables in the study. This analysis should indicate the means, standard deviations, and range of scores for these variables. Identify whether there is missing data (e.g., some participants may not provide responses to some items or whole scales), and develop plans to report how much missing data is present and whether a strategy will be implemented to replace missing data (for a review, see Schafer & Graham, 2002).

Step 4. If the proposal contains an instrument with multi-item scales or a plan to develop scales, first evaluate whether it will be necessary to reverse-score items, and then how total scale scores will be calculated. Also mention reliability checks for the internal consistency of the scales (i.e., the Cronbach alpha statistic).

Step 5. Identify the statistics and the statistical computer program for testing the major inferential research questions or hypotheses in the proposed study. The inferential questions or hypotheses relate variables or compare groups in terms of variables so that inferences can be drawn from the sample to a population. Provide a rationale for the choice of statistical test and mention the assumptions associated with the statistic. As shown in 
Table 8.3
, base this choice on the nature of the research question (e.g., relating variables or comparing groups as the most popular), the number of independent and dependent variables, and the variables used as covariates (e.g., see Rudestam & Newton, 2014). Further, consider whether the variables will be measured on an instrument as a continuous score (e.g., age from 18 to 36) or as a categorical score (e.g., women = 1, men = 2). Finally, consider whether the scores from the sample might be normally distributed in a bell-shaped curve if plotted out on a graph or non-normally distributed. There are additional ways to determine if the scores are normally distributed (see Creswell, 2012). These factors, in combination, enable a researcher to determine what statistical test will be suited for answering the research question or hypothesis. In 
Table 8.3
, we show how the factors, in combination, lead to the selection of a number of common statistical tests. For additional types of statistical tests, readers are referred to statistics methods books, such as Gravetter and Wallnau (2012).

Step 6. A final step in the data analysis is to present the results in tables or figures and interpret the results from the statistical test, discussed in the 
next section
.

Interpreting Results and Writing a Discussion Section

An interpretation in quantitative research means that the researcher draws conclusions from the results for the research questions, hypotheses, and the larger meaning of the results. This interpretation involves several steps:

· Report how the results addressed the research question or hypothesis. The Publication Manual of the American Psychological Association (American Psychological Association [APA], 2010) suggests that the most complete meaning of the results come from reporting extensive description, statistical significance testing, confidence intervals, and effect sizes. Thus, it is important to clarify the meaning of these last three reports of the results. The statistical significance testing reports an assessment as to whether the observed scores reflect a pattern other than chance. A statistical test is considered to be significant if the results are unlikely by chance to have occurred, and the null hypothesis of “no effect” can be rejected. The researcher sets a rejection level of “no effect,” such as p = 0.001, and then assesses whether the test statistic falls into this level of rejection. Typically results will be summarized as “the analysis of variance revealed a statistically significant difference between men and women in terms of attitudes toward banning smoking in restaurants F (2, 6) = 8.55, p = 0.001.”

Two forms of practical evidence of the results should also be reported: (a) the effect size and (b) the confidence interval. A confidence interval is a range of values (an interval) that describes a level of uncertainty around an estimated observed score. A confidence interval shows how good an estimated score might be. A confidence interval of 95%, for example, indicates that 95 out of 100 times the observed score will fall in the range of values. An effect size identifies the strength of the conclusions about group differences or the relationships among variables in quantitative studies. It is a descriptive statistic that is not dependent on whether the relationship in the data represents the true population. The calculation of effect size varies for different statistical tests: it can be used to explain the variance between two or more variables or the differences among means for groups. It shows the practical significance of the results apart from inferences being applied to the population.

· The final step is to draft a discussion section where you discuss the implications of the results in terms of how they are consistent with, refute, or extend previous related studies in the scientific literature. How do your research findings address gaps in our knowledge base on the topic? It is also important to acknowledge the implications of the findings for practice and for future research in the area. It may also involve discussing theoretical and practical consequences of the results. It is also helpful to briefly acknowledge potential limitations of the study, and potential alternative explanations for the study findings.

Example 8.1
 is a survey method plan section that illustrates many of the steps just mentioned. This excerpt (used with permission) comes from a journal article reporting a study of factors affecting student attrition in one small liberal arts college (Bean & Creswell, 1980, pp. 321–322).

Example 8.1 A Survey Method Plan

Methodology

The site of this study was a small (enrollment 1,000), religious, coeducational, liberal arts college in a Midwestern city with a population of 175,000 people. [Authors identified the research site and population.]

The dropout rate the previous year was 25%. Dropout rates tend to be highest among freshmen and sophomores, so an attempt was made to reach as many freshmen and sophomores as possible by distribution of the questionnaire through classes. Research on attrition indicates that males and females drop out of college for different reasons (Bean, 1978, in press; Spady, 1971). Therefore, only women were analyzed in this study.

During April 1979, 169 women returned questionnaires. A homogeneous sample of 135 women who were 25 years old or younger, unmarried, full-time U.S. citizens, and Caucasian was selected for this analysis to exclude some possible confounding variables (Kerlinger, 1973).

Of these women, 71 were freshmen, 55 were sophomores, and 9 were juniors. Of the students, 95% were between the ages of 18 and 21. This sample is biased toward higher-ability students as indicated by scores on the ACT test. [Authors presented descriptive information about the sample.]

Data were collected by means of a questionnaire containing 116 items. The majority of these were Likert-like items based on a scale from “a very small extent” to “a very great extent.” Other questions asked for factual information, such as ACT scores, high school grades, and parents’ educational level. All information used in this analysis was derived from questionnaire data. This questionnaire had been developed and tested at three other institutions before its use at this college. [Authors discussed the instrument.]

Concurrent and convergent validity (Campbell & Fiske, 1959) of these measures was established through factor analysis, and was found to be at an adequate level. Reliability of the factors was established through the coefficient alpha. The constructs were represented by 25 measures—multiple items combined on the basis of factor analysis to make indices—and 27 measures were single item indicators. [Validity and reliability were addressed.]

Multiple regression and path analysis (Heise, 1969; Kerlinger & Pedhazur, 1973) were used to analyze the data. In the causal model . . . , intent to leave was regressed on all variables which preceded it in the causal sequence. Intervening variables significantly related to intent to leave were then regressed on organizational variables, personal variables, environmental variables, and background variables. [Data analysis steps were presented.]

Components of an Experimental Study Method Plan

An experimental method plan follows a standard form: (a) participants and design, (b) procedure, and (c) measures. These three sequential sections generally are sufficient (often in studies with a few measures, the procedure and measures sections are combined into a single procedure section). In this section of the chapter, we review these components as well as information regarding key features of experimental design and corresponding statistical analyses. As with the section on survey design, the intent here is to highlight key topics to be addressed in an experimental method plan. An overall guide to these topics is found by answering the questions on the checklist shown in 
Table 8.4
.

Participants

Readers need to know about the selection, assignment, and number of participants who will take part in the experiment. Consider the following suggestions when writing the method section plan for an experiment:

· Describe the procedures for recruiting participants to be in the study, and any selection processes used. Often investigators aim to recruit a study sample that shares certain characteristics by formally stating specific inclusion and exclusion study criteria when designing their study (e.g., inclusion criterion: participants must be English language speaking; exclusion criterion: participants must not be children under the age of 18). Recruitment approaches are wide-ranging, and can include random digit dialing of households in a community, posting study recruitment flyers or e-mails to targeted communities, or newspaper advertisements. Describe the recruitment approaches that will be used and the study compensation provided for participating.

· One of the principal features distinguishing an experiment from a survey study design is the use of random assignment. Random assignment is a technique for placing participants into study conditions of a manipulated variable of interest. When individuals are randomly assigned to groups, the procedure is called a true experiment. If random assignment is used, discuss how and when the study will randomly assign individuals to treatment groups, which in experimental studies are referred to as levels of an independent variable. This means that of the pool of participants, Individual 1 goes to Group 1, Individual 2 to Group 2, and so forth so that there is no systematic bias in assigning the individuals. This procedure eliminates the possibility of systematic differences among characteristics of the participants that could affect the outcomes so that any differences in outcomes can be attributed to the study’s manipulated variable (or variables) of interest (Keppel & Wickens, 2003). Often experimental studies may be interested in both randomly assigning participants to levels of a manipulated variable of interest (e.g., a new treatment approach for teaching fractions to children versus the traditional approach) while also measuring a second predictor variable of interest that cannot utilize random assignment (e.g., measuring whether the treatment benefits are larger among female compared to male children; it is impossible to randomly assign children to be male or female). Designs in which a researcher has only partial (or no) control over randomly assigning participants to levels of a manipulated variable of interest are called quasi-experiments.

· Conduct and report a power analysis for sample size determination (for a helpful resource, see Kraemer & Blasey, 2016). The procedures for a sample size power analysis mimic those for a survey design, although the focus shifts to estimating the number of participants needed in each condition of the experiment to detect significant group differences. In this case, the input parameters shift to include an estimate of the effect size referencing the estimated differences between the groups of your manipulated variable(s) of interest and the number of groups in your experiment. Readers are encouraged to review the power analysis section earlier in the survey design portion of this chapter and then consider the following example:

· Previously we introduced a cross-sectional survey design assessing the relationship between number of overtime hours worked and burnout symptoms among nurses. We might decide to conduct an experiment to test a related question: Do nurses working full time have higher burnout symptoms compared to nurses working part time? In this case, we might conduct an experiment in which nurses are randomly assigned to work either full time (group 1) or part time (group 2) for 2 months, at which time we could measure burnout symptoms. We could conduct a power analysis to evaluate the sample size needed to detect a significant difference in burnout symptoms between these two groups. Previous literature might indicate an effect size difference between these two groups at d = .5, and as with our survey study design, we can assume a two-tailed alpha = .05 and beta = .20. We ran the calculation again using the G*Power software program (Faul et al., 2007; Faul et al., 2009) to estimate the sample size needed to detect a significant difference between groups:

Test family: t tests

Statistical test: Means: difference between two independent means (two groups)

Type of power analysis: A priori: Compute required sample size

Tails: Two

Effect size d: .5

α err prob: .05

Power (1 – β err prob): .8

Allocation ratio N2/N1: 1

· With these input parameters, the power analysis indicates a total sample size of 128 participants (64 in each group) is needed in order to detect a significant difference between groups in burnout symptoms.

· At the end of the participants section, it is helpful to provide a formal experimental design statement that specifies the independent variables and their corresponding levels. For example, a formal design statement might read, “The experiment consisted of a one-way two-groups design comparing burnout symptoms between full-time and part-time nurses.”

Variables

The variables need to be specified in the formal design statement and described (in detail) in the procedure section of the experimental method plan. Here are some suggestions for developing ideas about variables in a proposal:

· Clearly identify the independent variables in the experiment (recall the discussion of variables in 
Chapter 3
) and how they will be manipulated in the study. One common approach is to conduct a 2 × 2 between-subjects factorial design in which two independent variables are manipulated in a single experiment. If this is the case, it is important to clarify how and when each independent variable is manipulated.

· Include a manipulation check measure that evaluates whether your study successfully manipulated the independent variable(s) of interest. A manipulation check measure is defined as a measure of the intended manipulated variable of interest. For example, if a study aims to manipulate self-esteem by offering positive test feedback (high self-esteem condition) or negative test feedback (low self-esteem condition) using a performance task, it would be helpful to quantitatively evaluate whether there are indeed self-esteem differences between these two conditions with a manipulation check measure. After this self-esteem study manipulation, a researcher may include a brief measure of state self-esteem as a manipulation check measure prior to administering the primary outcome measures of interest.

· Identify the dependent variable or variables (i.e., the outcomes) in the experiment. The dependent variable is the response or the criterion variable presumed to be caused by or influenced by the independent treatment conditions. One consideration in the experimental method plan is whether there are multiple ways to measure outcome(s) of interest. For example, if the primary outcome is aggression, it may be possible to collect multiple measures of aggression in your experiment (e.g., a behavioral measure of aggression in response to a provocation, self-reported perceptions of aggression).

· Identify other variables to be measured in the study. Three categories of variables are worth mentioning. First, include measures of participant demographic characteristics (e.g., age, gender, ethnicity). Second, measure variables that may contribute noise to the study design. For example, self-esteem levels may fluctuate during the day (and relate to the study outcome variables of interest) and so it may be beneficial to measure and record time of day in the study (and then use it as a covariate in study statistical analyses). Third, measure variables that may be potential confounding variables. For example, a critic of the self-esteem manipulation may say that the positive/negative performance feedback study manipulation also unintentionally manipulated rumination, and it was this rumination that is a better explanation for study results on the outcomes of interest. By measuring rumination as a potential confounding variable of interest, the researcher can quantitatively evaluate this critic’s claim.

Instrumentation and Materials

Just like in a survey method plan, a sound experimental study plan calls for a thorough discussion about the instruments used—their development, their items, their scales, and reports of reliability and validity of scores on past uses. However, an experimental study plan also describes in detail the approach for manipulating the independent variables of interest:

· Thoroughly discuss the materials used for the manipulated variable(s) of interest. One group, for example, may participate in a special computer-assisted learning plan used by a teacher in a classroom. This plan might involve handouts, lessons, and special written instructions to help students in this experimental group learn how to study a subject using computers. A pilot test of these materials may also be discussed, as well as any training required to administer the materials in a standardized way.

· Often the researcher does not want participants to know what variables are being manipulated or the condition they have been assigned to (and sometimes what the primary outcome measures of interest are). It is important, then, to draft a cover story that will be used to explain the study and procedures to participants during the experiment. If any deception is used in the study, it is important to draft a suitable debriefing approach and to get all procedures and materials approved by your institution’s IRB (see 
Chapter 4
).

Experimental Procedures

The specific experimental design procedures also need to be identified. This discussion involves indicating the overall experiment type, citing reasons for the design, and advancing a visual model to help the reader understand the procedures.

· Identify the type of experimental design to be used in the proposed study. The types available in experiments are pre-experimental designs, quasi-experiments, and true experiments. With pre-experimental designs, the researcher studies a single group and implements an intervention during the experiment. This design does not have a control group to compare with the experimental group. In quasi-experiments, the investigator uses control and experimental groups, but the design may have partial or total lack of random assignment to groups. In a true experiment, the investigator randomly assigns the participants to treatment groups. A single-subject design or N of 1 design involves observing the behavior of a single individual (or a small number of individuals) over time.

· Identify what is being compared in the experiment. In many experiments, those of a type called between-subject designs, the investigator compares two or more groups (Keppel & Wickens, 2003; Rosenthal & Rosnow, 1991). For example, a factorial design experiment, a variation on the between-group design, involves using two or more treatment variables to examine the independent and simultaneous effects of these treatment variables on an outcome (Vogt & Johnson, 2015). This widely used experimental design explores the effects of each treatment separately and also the effects of variables used in combination, thereby providing a rich and revealing multidimensional view. In other experiments, the researcher studies only one group in what is called a within-group design. For example, in a repeated measures design, participants are assigned to different treatments at different times during the experiment. Another example of a within-group design would be a study of the behavior of a single individual over time in which the experimenter provides and withholds a treatment at different times in the experiment to determine its impact. Finally, studies that include both a between-subjects and a within-subjects variable are called mixed designs.

· Provide a diagram or a figure to illustrate the specific research design to be used. A standard notation system needs to be used in this figure. As a research tip, we recommend using the classic notation system provided by Campbell and Stanley (1963, p. 6):

· X represents an exposure of a group to an experimental variable or event, the effects of which are to be measured.

· O represents an observation or measurement recorded on an instrument.

· Xs and Os in a given row are applied to the same specific persons. Xs and Os in the same column, or placed vertically relative to each other, are simultaneous.

· The left-to-right dimension indicates the temporal order of procedures in the experiment (sometimes indicated with an arrow).

· The symbol R indicates random assignment.

· Separation of parallel rows by a horizontal line indicates that comparison groups are not equal (or equated) by random assignment. No horizontal line between the groups displays random assignment of individuals to treatment groups.

In 
Examples 8.2
–
8.5
, this notation is used to illustrate pre-experimental, quasi-experimental, true experimental, and single-subject designs.

Example 8.2 Pre-experimental Designs

One-Shot Case Study

This design involves an exposure of a group to a treatment followed by a measure.

· Group A X_____________________O

One-Group Pretest-Posttest Design

This design includes a pretest measure followed by a treatment and a posttest for a single group.

· Group A O1————X————O2

Static Group Comparison or Posttest-Only With Nonequivalent Groups

Experimenters use this design after implementing a treatment. After the treatment, the researcher selects a comparison group and provides a posttest to both the experimental group(s) and the comparison group(s).

· Group A X______________________O

· Group B _______________________O

Alternative Treatment Posttest-Only With Nonequivalent Groups Design

This design uses the same procedure as the Static Group Comparison, with the exception that the nonequivalent comparison group received a different treatment.

· Group A X1_____________________O

· Group B X2_____________________O

Example 8.3 Quasi-experimental Designs

Nonequivalent (Pretest and Posttest) Control-Group Design

In this design, a popular approach to quasi-experiments, the experimental Group A and the control Group B are selected without random assignment. Both groups take a pretest and posttest. Only the experimental group receives the treatment.

· Group A O————X————O

· ___________________________

· Group B O—————————O

Single-Group Interrupted Time-Series Design

In this design, the researcher records measures for a single group both before and after a treatment.

· Group A O—O—O—O—X—O—O—O—O

Control-Group Interrupted Time-Series Design

This design is a modification of the Single-Group Interrupted Time-Series design in which two groups of participants, not randomly assigned, are observed over time. A treatment is administered to only one of the groups (i.e., Group A).

· Group A O—O—O—O—X—O—O—O—O

· __________________________________

· Group B O—O—O—O—O—O—O—O—O

Example 8.4 True Experimental Designs

Pretest–Posttest Control-Group Design

A traditional, classical design, this procedure involves random assignment of participants to two groups. Both groups are administered both a pretest and a posttest, but the treatment is provided only to experimental Group A.

· Group A R——–––O———X———O

· Group B R———O———————O

Posttest-Only Control-Group Design

This design controls for any confounding effects of a pretest and is a popular experimental design. The participants are randomly assigned to groups, a treatment is given only to the experimental group, and both groups are measured on the posttest.

· Group A R——————X—————O

· Group B R————————————O

Solomon Four-Group Design

A special case of a 2 × 2 factorial design, this procedure involves the random assignment of participants to four groups. Pretests and treatments are varied for the four groups. All groups receive a posttest.

· Group A R————O———X———O

· Group B R————O———————O

· Group C R———————X————O

· Group D R——————————–—O

Example 8.5 Single-Subject Designs

A-B-A Single-Subject Design

This design involves multiple observations of a single individual. The target behavior of a single individual is established over time and is referred to as a baseline behavior. The baseline behavior is assessed, the treatment provided, and then the treatment is withdrawn.

· Baseline A Treatment B Baseline A

· O–O–O–O–O–X–X–X–X–X–O–O–O–O–O–O

Threats to Validity

There are several threats to validity that will raise questions about an experimenter’s ability to conclude that the manipulated variable(s) of interest affect an outcome and not some other factor. Experimental researchers need to identify potential threats to the internal validity of their experiments and design them so that these threats will not likely arise or are minimized. There are two types of threats to validity: (a) internal threats and (b) external threats.

· Internal validity threats are experimental procedures, treatments, or experiences of the participants that threaten the researcher’s ability to draw correct inferences from the data about the population in an experiment. 
Table 8.5
 displays these threats, provides a description of each one of them, and suggests potential responses by the researcher so that the threat may not occur. There are those involving participants (i.e., history, maturation, regression, selection, and mortality), those related to the use of an experimental treatment that the researcher manipulates (i.e., diffusion, compensatory and resentful demoralization, and compensatory rivalry), and those involving procedures used in the experiment (i.e., testing and instruments).

Source:
 Adapted from Creswell (2012).

· Potential threats to external validity also must be identified and designs created to minimize these threats. External validity threats arise when experimenters draw incorrect inferences from the sample data to other persons, other settings, and past or future situations. As shown in 
Table 8.6
, these threats arise because of the characteristics of individuals selected for the sample, the uniqueness of the setting, and the timing of the experiment. For example, threats to external validity arise when the researcher generalizes beyond the groups in the experiment to other racial or social groups not under study, to settings not examined, or to past or future situations. Steps for addressing these potential issues are also presented in 
Table 8.6
.

· Other threats that might be mentioned in the method section are the threats to statistical conclusion validity that arise when experimenters draw inaccurate inferences from the data because of inadequate statistical power or the violation of statistical assumptions. Threats to construct validity occur when investigators use inadequate definitions and measures of variables.

Practical research tips for proposal writers to address validity issues are as follows:

· Identify the potential threats to validity that may arise in your study. A separate section in a proposal may be composed to advance this threat.

· Define the exact type of threat and what potential issue it presents to your study.

· Discuss how you plan to address the threat in the design of your experiment.

The Procedure

A researcher needs to describe in detail the sequential step-by-step procedure for conducting the experiment. A reader should be able to clearly understand the cover story, the design being used, the manipulated variable(s) and outcome variable(s), and the timeline of activities. It is also important to describe steps taken to minimize noise and bias in the experimental procedures (e.g., “To reduce the risk of experimenter bias, the experimenter was blind to the participant’s study condition until all outcome measures were assessed.”).

· Discuss a step-by-step approach for the procedure in the experiment. For example, Borg and Gall (2006) outlined steps typically used in the procedure for a pretest-posttest control group design with matching participants in the experimental and control groups:

0. Administer measures of the dependent variable or a variable closely correlated with the dependent variable to the research participants.

Source:
 Adapted from Creswell (2012).

1. Assign participants to matched pairs on the basis of their scores on the measures described in Step 1.

2. Randomly assign one member of each pair to the experimental group and the other member to the control group.

3. Expose the experimental group to the experimental treatment and administer no treatment or an alternative treatment to the control group.

4. Administer measures of the dependent variables to the experimental and control groups.

5. Compare the performance of the experimental and control groups on the posttest(s) using tests of statistical significance.

Data Analysis

Tell the reader about the types of statistical analyses that will be implemented on the dataset.

· Report the descriptive statistics. Some descriptive statistics that are commonly reported include frequencies (e.g., how many male and female participants were in the study?), means and standard deviations (e.g., what’s the mean age of the sample; what are the group means and corresponding standard deviation values for the primary outcome measures?).

· Indicate the inferential statistical tests used to examine the hypotheses in the study. For experimental designs with categorical information (groups) on the independent variable and continuous information on the dependent variable, researchers use t tests or univariate analysis of variance (ANOVA), analysis of covariance (ANCOVA), or multivariate analysis of variance (MANOVA—multiple dependent measures). (Several of these tests are mentioned in 
Table 8.3
, which was presented earlier.) In factorial designs where more than one independent variable is manipulated, you can test for main effects (of each independent variable) and interactions between independent variables. Also, indicate the practical significance by reporting effect sizes and confidence intervals.

· For single-subject research designs, use line graphs for baseline and treatment observations for abscissa (horizontal axis) units of time and the ordinate (vertical axis) target behavior. Researchers plot each data point separately on the graph, and connect the data points with lines (e.g., see Neuman & McCormick, 1995). Occasionally, tests of statistical significance, such as t tests, are used to compare the pooled mean of the baseline and the treatment phases, although such procedures may violate the assumption of independent measures (Borg & Gall, 2006).

Interpreting Results and Writing a Discussion Section

The final step in an experiment is to interpret the findings in light of the hypotheses or research questions and to draft a discussion section. In this interpretation, address whether the hypotheses or questions were supported or whether they were refuted. Consider whether the independent variable manipulation was effective (a manipulation check measure can be helpful in this regard). Suggest why the results were significant, or why they were not, linking the new evidence with past literature (
Chapter 2
), the theory used in the study (
Chapter 3
), or persuasive logic that might explain the results. Address whether the results might have been influenced by unique strengths of the approach, or weaknesses (e.g., threats to internal validity), and indicate how the results might be generalized to certain people, settings, and times. Finally, indicate the implications of the results, including implications for future research on the topic.

Example 8.6
 is a description of an experimental method plan adapted from a value affirmation stress study published by Creswell and colleagues (Creswell et al., 2005).

Example 8.6 An Experimental Method Plan

This study tested the hypothesis that thinking about one’s important personal values in a self-affirmation activity could buffer subsequent stress responses to a laboratory stress challenge task. The specific study hypothesis was that the self-affirmation group, relative to the control group, would have lower salivary cortisol stress hormone responses to a stressful performance task. Here we highlight a plan for organizing the methodological approach for conducting this study. For a full description of the study methods and findings, see the published paper (Creswell et al., 2005).

Method

Participants

A convenience sample of eighty-five undergraduates will be recruited from a large public university on the west coast, and compensated with course credit or $30. This sample size is justified based on a power analysis conducted prior to data collection with the software program G*Power (Faul et al., 2007; Faul et al., 2009), based on [specific input parameters described here for the power analysis]. Participants will be eligible to participate if they meet the following study criteria [list study inclusion and exclusion criteria here]. All study procedures have been approved by the University of California, Los Angeles Institutional Review Board, and participants will provide written informed consent prior to participating in study related activities.

The study is a 2 × 4 mixed design, with value affirmation condition as a two-level between subjects variable (condition: value affirmation or control) and time as a four-level within-subjects variable (time: baseline, 20 minutes post-stress, 30 minutes post-stress, and 45 minutes post-stress). The primary outcome measure is the stress hormone cortisol, as measured by saliva samples.

Procedure

To control for the circadian rhythm of cortisol, all laboratory sessions will be scheduled between the hours of 2:30 pm and 7:30 pm. Participants will be run through the laboratory procedures one at a time. The cover story consists of telling participants that the study is interested in studying physiological responses to laboratory performance tasks.

Upon arrival all participants will complete an initial values questionnaire where they will rank order five personal values. After a 10-minute acclimation period, participants will provide a baseline saliva sample, for the assessment of salivary cortisol levels. Participants will then receive instructions on the study tasks and then will be randomly assigned by the experimenter (using a random number generator) to either a value affirmation or control condition, where they will be asked to [description of the value affirmation independent variable manipulation here, along with the subsequent manipulation check measure]. All participants will then complete the laboratory stress challenge task [description of the stress challenge task procedures for producing a stress response here]. After the stress task, participants will complete multiple post-stress task questionnaire measures [describe them here], and then provide saliva samples at 20, 30, and 45 minutes post-stress task onset. After providing the last saliva sample, participants will be debriefed, compensated, and dismissed.

Summary

This chapter identified essential components for organizing a methodological approach and plan for conducting either a survey or an experimental study. The outline of steps for a survey study began with a discussion about the purpose, the identification of the population and sample, the survey instruments to be used, the relationship between the variables, the research questions, specific items on the survey, and steps to be taken in the analysis and the interpretation of the data from the survey. In the design of an experiment, the researcher identifies participants in the study, the variables—the manipulated variable(s) of interest and the outcome variables—and the instruments used. The design also includes the specific type of experiment, such as a pre-experimental, quasi-experimental, true experiment, or single-subject design. Then the researcher draws a figure to illustrate the design, using appropriate notation. This is followed by comments about potential threats to internal and external validity (and possibly statistical and construct validity) that relate to the experiment, the statistical analyses used to test the hypotheses or research questions, and the interpretation of the results.

References
John W. Creswell, J. D. (2018). Research Design Qualitative, Quantitative, and Mixed Methods Approaches. Los Angeles: Sage.

1

Title

[Insert your name]

School of Psychology, Capella University

PSY-FP5201: Integrative Project

Literature Review

[Insert your literature review. Make sure all revisions have been addressed. Use this space to introduce your research topic and describe the significance of your topic.]

Theoretical Orientation for the Research Concept

[This section identifies the theories or models that provide the foundation for the research. This section should present the theories or models and explain how the research problem under investigation relates to the theories or models. The theories or models guide the research questions and justify what is being measured (variables) as well as how those variables are related. Use models or theories from seminal sources that provide a reasonable conceptual framework or theoretical foundation to use in developing the research questions, identifying variables, and selecting data collection instruments.]

Review of the Literature

[The overall literature review reflects a foundational understanding of the theory or theories, literature, and research studies related to the topic. For this assignment, the literature review should reflect an effective understanding of the current state of research and literature on the topic. Discuss and synthesize studies related to the topic. Include studies describing and/or connecting the studies on related research such as factors associated with the themes; studies on the instruments used to collect data; studies on the broad population for the study; and/or studies similar to the proposed study. The themes presented and research studies discussed and synthesized in the review of literature demonstrate a working understanding of all aspects of the research topic.]

Synthesis of the Research Findings

[This section requires that you take the research findings discussed in the review of literature above and integrate the findings so that they overlap and create a new understanding of the issues that relate to your research topic.]

Critique of Previous Research Methods

[Synthesize and critique the various methodologies and designs that have been used in prior empirical research related to the study. Discuss why and how your research method and design is the most appropriate for addressing your research problem. You must include supporting examples with proper citation.]

Summary

[This section restates what was written above and provides supporting citations for key points. The summary section reflects that the learner has done due diligence to become well-read on the topic and can demonstrate that this research concept might add to the existing body of research and knowledge on the topic. It synthesizes the information from the chapter to define the gaps or identified research needs arising from the literature and the theories or models to provide the foundation for the study.]

Title

[Insert your title. Follow current edition guidance for creating a title. The title should be a statement and not a question. It should summarize the main idea of the paper. It should be concise and include the variables in the research and their relationship.]

 [Include an introduction before the background of the topic. Restate the topic and address the significance of the study from the literature review. Then, provide a road map covering the primary sections of the concept paper. This should be no longer than a paragraph (5–7 sentences). It does not need a heading.]

Background

[The background of your study is a modified version of your literature review. It reflects the most important points from the main ideas of the literature review. Provide a summary of those main points that highlight the gaps in the literature.]

 

Research Problem

[The research problem is an extension of the research background. This section should include an explicit statement of the research. It needs to address what the research literature states we know, what the literature indicates we know, and what we don’t know, based on the literature. This should be a succinct yet detailed paragraph of 5–7 sentences.]

Research Question

[Your research question should be stated as a question. It should address the research problem addressed in the previous section. State it as a question. Include the hypothesis, null hypothesis, and alternative hypothesis.]

 

Goals and Objectives

[State the goals of this study as if you were going to conduct it or pursue a research proposal. Think about the significance of conducting a study like this and the impact it could have. State the goals in a succinct paragraph of 4–6 sentences. List at least three objectives (what the purpose of the study would be) in numerical form. You may add more if needed.]

1. Goal one.

2. Goal two.

3. Goal three.

 

Population and Sample

[Discuss the setting, general population, target population, and study sample. The discussion of the sample includes the research terminology specific to the type of sampling for the study. When describing the general population consider this as an example:

· the general population (such as students with disabilities).

· target population (such as students with disabilities in one specific location).

· the study sample (students with disabilities in the district who will participate in the study—actual study sample).

When describing the sample size, provide evidence that the sample size is adequate for the research design. If you used the statistical flowchart from the media piece, you should have an appropriate statistical test. As a rule of thumb, consider the following:

· Absolute minimum: 50 cases or participants applicable to studies that use frequencies or descriptive statistics and parametric statistical tests (t-tests, ANOVA, correlation, regression analysis). Additional requirements related to the use of certain statistical analysis procedures may increase that number. Survey research = 10 subjects per survey question.

· An a-priori Power Analysis is required to justify the study sample size based on the anticipated effect size and selected design. Include this in addition to using the literature to support your choices.]

Methodology and Procedures

Quantitative Research

[Define and describe quantitative research. Identify, define, and describe the research design.]

Instruments.
[Provide a detailed discussion of the instrumentation and data collection that includes validity and reliability of the data. Describe the structure of each data collection instrument and data sources (tests, questionnaires, interviews, observations data bases, media, and so on). Specify the type and level of data collected with each instrument.]

Data Collection.
[Describe the procedures for the actual data collection that would allow replication of the study by another researcher, including how each instrument or data source would be used, how, and where data would be collected and recorded.]

Data Analysis.
[Address the what, why, and how of data analysis. Identify
what
statistical nonstatistical analysis would be used. Discuss
why
the statistical analysis is the best selection. Demonstrate
how
the statistical analysis selected aligns with the research question and design.]

 

References

 

LITERATURE REVIEW 2

LITERATURE REVIEW 2

Rebecca Faino

School of Psychology, Capella University

PSY-FP5201: Integrative Project

Howard Fero

May 6, 2022

The selected problem is a mental illness which is a problem that affects the normal health and the well-being of the individuals. This condition affects all age groups, and the management of the symptoms plays an important role in enabling patients to resume their normal health. Even though various medications such as antidepressants help manage the symptoms, the role played by cognitive-behavioral therapy (CBT) cannot be ignored. Addressing the issue of mental illness through CBT as an intervention has a significant role in improving the well-being and health of the population. It helps change the automatic negative thoughts that lead to or worsen the emotional challenges, depression, and anxiety (David et al., 2018). It helps remove or reduce the negative thoughts that have a detrimental impact on the mood.

Addressing the mental illness using CBT therapy enables individuals to learn to be their therapists. They get the opportunity to develop coping skills, thus allowing them to change their thoughts, problematic emotions, and behavior. By developing coping strategies, individuals can deal with challenges hence effective for persons with depression, panic disorder, and different mental health conditions. Apart from the patients benefiting from the CBT, community members benefit by experiencing a low rate of mental illnesses resulting from full recovery and improvement in the symptoms (David et al., 2018). The families experience a reduction in healthcare spending due to the early recovery of the sick family members. The healthcare providers such as psychiatrists get satisfied with the services they deliver to the patients.

Summary of the History of the Chosen Topic

Behavioral treatment approach for the mental disorders has been in existence since the 1900s. The key proponents of behavioral treatment, such as Skinner, Pavlov, and Watson, developed change and behavioral treatment theories. The behaviorism treatment approach is based on the idea that behaviors are measurable, can be modeled, and changed. The first wave of behavioral therapy began around 1930/and the 40s as a response to the emotional effects encountered by most of the World War II veterans returning from the war (Lange, 2021). There was a need for successful short-term therapy to manage depression and anxiety that coincided with the buildup of behavioral research on how people learned to behave and react to situations in their lives.

Watson’s theory on behavior is that individuals’ reactions in different circumstances were influenced by the manner in which the overall experiences had been programmed which caused their reaction. While carrying out his experiment, he discovered that children responded to some stimuli in a manner that was not similar to their normal response where the training process is absence. Ivan Pavlov introduced the concept of conditioning using experiments with animals (Lange, 2021). His conclusion directly impacted Watson and offered an original scientific basis for his beliefs. In his conclusion, he discovered that the conditioned behaviors would disappear in case they failed to produce the exact outcomes.

In his experiment, while developing instrumental conditioning, Thorndike came up with two laws, i.e. the law of exercise and effect. Based on the law of exercise, repetition of a response strengthens it, indicating that a stronger inclination to carry out the needed behaviors leads to an increase in proficiency within a shorter time (Lange, 2021). According to the law of effect, the developed behaviors become stronger or weaker based on whether there is a reward or punishment.

Skinner designed behaviorist theory of operant conditioning, and his perception differed from what was presented by Watson and Pavlov. He believed that it was not what is coming before the behavior that impacts it but things that originates or happen directly after the behavior. According to this theory, there is manipulation of behaviors, and this is followed by the positive or negative reinforcement (Lange, 2021). The positive reinforcement leads to an increase in the desired behaviors when the rewards follow them.

Summary of the sources applicable to the theoretical background

The behaviors and the cognitive behavior therapy works based on the principles of classical and social learning theory. It is possible to unlearn the maladaptive behaviors acquired through operant conditioning and this occurs through different deconditioning techniques (Lange, 2021). The behavioral therapy and the cognitive behavioral therapy operate according to the metacognition differences of the extensive basic animal research that includes Pavlov’s classical respondent conditioning and Skinner’s operant conditioning. The negative reinforcement leads to the rise in the desired actions by enabling subjects to escape the punishment through the use of the performance. It is also possible to alter the behaviors through extinction and punishment. The punishment of the behaviors after their occurrence discourages them from being repeated.

According to Bouton et al. (2021), the nature under which the behavior has been acquired through Pavlonian or the operant learning reduces in strength when the outcomes that reinforce it are removed. Therefore, Pavlovian and operant extinction do not depend on the erasure of the original learning. However, it is influenced by the new inhibitory learning, whose expression mainly takes place within the context under which learning occurs.

Summary of the sources applicable to the best practices for engaging in research

The data collection procedure for the research involves searching the peer review articles related to Cognitive Behavior Therapy and mental illness. The research procedure for collecting the relevant data involves the use of the relevant databases such as Capella University Database, PubMed, Scopus, and the JSTOR. The search is guided by the key terms that lead to selecting the relevant article for this research work. The keywords include behavioral, modeling, cognitive behavioral therapy, anxiety, depression, and long-term care. The search for the articles was restricted to those published less than five years, i.e., between 2018 to 2022. The selection of the articles is also made based on their authenticity, for example, the authors’ qualifications.

Summary of the sources that added significant and relevance to the knowledge base of psychology

David et al. (2018) focused on finding out why CBT is used as the current gold standard of psychotherapy. According to the study’s outcomes, CBT is an effective strategy for treating mental illness. It is proposed as the global guideline for psychotherapy treatments and is used as the first-line treatment approach. CBT is moderately effective as a treatment approach in successfully managing mental disorders such as anxiety (Carpenter et al., 2018). It also helps deal with other mental health conditions such as panic disorder and post-traumatic disorder.

CBT is helpful in the reduction of depressive symptoms as an independent treatment approach or combination with other medications such as the antidepressant. CBT assists in modifying the underlying beliefs that are important in the maintenance of depression and helping individuals overcome psychosocial problems (Gautam et al., 2020). Computer-based programs also help healthcare professionals diagnose and treat (Wright et al., 2019 ). The computer-assisted CBT, together with the support from the healthcare professionals, helps address the depressive symptoms. Therefore, the psychiatrists can adopt the procedure as an effective solution.

The guidelines of the profession applicable to the ethical conduct of a research

During research work, the vulnerability of the study participants must be considered. This is important in ensuring that the rights of the participants are protected. The protection of the dignity of the study subjects and the publication of the information in the research is the common daily work that must be followed. During research work, the researchers must cope with the three values systems, including society, nursing, and science, that might conflict with the value of the study participants, the communities, and the society, hence creating tension. Researchers are expected to promote major ethical principles in the performance of the research. These ethical issues include informed consent and beneficence. Respect for autonomy and confidentiality and showing respect for privacy. Therefore, it is important to focus on the effective care process, prevention of harm, protection of dignity, and advocating for the defense and respecting the subjects’ rights.

The practice guidelines addressed the respect for individual differences and diversity.

People exist in a social, political, economic, psychological, historical, and political context. Therefore, there is a need to appreciate the impacts of the above context on the person’s behavior. There is an emergency for the ongoing study of psychology. Guidelines have been put in place on Multicultural Education, Training, Research, Practice and Organization Change for the ongoing study of psychology. They also reflect on some of the data that are emerging, especially on the needs of the specific group of people, for example, individuals with a mental disorder, changes that occur within the society, and the individual groups who have been ignored or marginalized as a result of their racial or ethnic group (Clauss-Ehlers et al., 2019). Some specific goals guide the researchers and the psychologists. In this case, the goals are focused on the need to address the multiculturalism and the diversity in research; basic information and data support the recommended guidelines and underscoring their roles; references to encourage continuous research, training, and practices; and the paradigms that help in broadening the purview of psychology as a profession.

There are practice guidelines that promote respect for the individual’s differences and diversity during research work. It is important to acknowledge that humans are cultural beings; therefore, they might have attitudes and beliefs that have detrimental influence on their perspectives and interaction with other individuals who are ethically and racially different from them. It is also important to acknowledge the role of the multicultural sensitiveness, the awareness, and the understanding of persons based on their differences in terms of ethnicity and race (Melton, 2018). It is also recommended that there be recognition of the role of carrying out the culturally based and ethical psychological research amongst individuals from linguistic, ethnic, and racial minority origins.

References

Carpenter, J. K., Andrews, L. A., Witcraft, S. M., Powers, M. B., Smits, J. A., & Hofmann, S. G. (2018). Cognitive-behavioral therapy for anxiety and related disorders: A meta‐analysis of randomized placebo‐controlled trials. Depression and Anxiety, 35(6), 502-514. DOI: 10.1002/da.22728

Bouton, M. E., Maren, S., & McNally, G. P. (2021). Behavioral and neurobiological mechanisms of Pavlovian and instrumental extinction learning. Physiological reviews. https://doi.org/10.1152/physrev.00016.2020

Clauss-Ehlers, C. S., Chiriboga, D. A., Hunter, S. J., Roysircar, G., & Tummala-Narra, P. (2019). APA Multicultural Guidelines executive summary: Ecological approach to context, identity, and intersectionality. American Psychologist, 74(2), 232.

David, D., Cristea, I., & Hofmann, S. G. (2018). Why cognitive behavioral therapy is the current gold standard of psychotherapy. Frontiers in psychiatry, 9, 4.  Doi: 10.3389/fpsyt.2018.00004

Gautam, M., Tripathi, A., Deshmukh, D., & Gaur, M. (2020). Cognitive-behavioral therapy for depression. Indian Journal of Psychiatry, 62(Suppl 2), S223. Doi: 10.4103/psychiatry.IndianJPsychiatry_772_19

Lange, K. W. (2021). Task sharing in psychotherapy as a viable global mental health approach in resource-poor countries and also in high-resource settings. Global Health Journal, 5(3), 120-127. https://doi.org/10.1016/j.glohj.2021.07.001

Melton, M. L. (2018). Ally, activist, advocate: Addressing role complexities for the multiculturally competent psychologist. Professional Psychology: Research and Practice, 49(1), 83.

Wright, J. H., Owen, J. J., Richards, D., Eells, T. D., Richardson, T., Brown, G. K., … & Thase, M. E. (2019). Computer-assisted cognitive-behavior therapy for depression: a systematic review and meta-analysis. The Journal of clinical psychiatry, 80(2), 3573. DOI: 10.4088/JCP.18r12188

Annotated Bibliography

Rebecca Faino

Capella University

Integrative Project Masters in Psy

Howard Fero

May 5, 2022

Mental illness is among healthcare issues that affect the well-being of individuals in the present society. Mental illness is common in all age groups and ethnic groupings. The effective management of mental illness requires both pharmacological and non-pharmacological interventions. The treatment approaches are targeted at reducing the symptoms and improvement on the behaviors of the affected individuals. Various medications such as antidepressants are used for the management of patients. Also, the psychotherapy interventions such as family therapy, group therapy, and cognitive-behavioral therapy (CBT) help address the issue. For this discussion, the focus is on the presentation of annotated bibliography on the role of cognitive-behavioral therapy in the management of mental illness.

Nakao, M., Shirotsuki, K., & Sugaya, N. (2021). Cognitive-behavioral therapy for the management of mental health and stress-related disorders: Recent advances in techniques and technologies. BioPsychoSocial medicine, 15(1), 1-4. Doi: 
10.1186/s13030-021-00219-w

The authors of the article aimed at determining the effectiveness of the CBT in stressful situations among the clinical and the general population and identifying the advancement in the CBT-related methods. The authors of this article are qualified in the areas of study. They are employed in the department of Psychosomatic Medicine, graduate from the school of the human and social sciences, and a member of the unit of public health and preventive medicine. A literature review method was adopted to search for the studies that were performed from 1987 to 2021 and this led to the identification of the 345 articles that relates to biopsychosocial medicine. The problem of focus in this article is the mental health and stress-associated disorders. The results from this study show that CBT was suitable for various categories of mental problems. The study is, therefore, important for the selected issue of mental illness as it reveals the role of CBT in the successful management of mental and physical issues. The use of the online CBT and the self-help CBT through the use of the mobile applications. The article is relevant since it was recently published by qualified authors.

David, D., Cristea, I., & Hofmann, S. G. (2018). Why cognitive behavioral therapy is the current gold standard of psychotherapy. Frontiers in psychiatry, 9, 4.  Doi: 
10.3389/fpsyt.2018.00004

The authors aimed at looking at the reasons why CBT is considered the present gold standard of psychotherapy. The authors of the articles have qualifications for working in the department of clinical psychology and psychotherapy, the department of the health sciences and policy, and the department of the psychological and brain sciences. The authors failed to give a clear illustration of the method of the study. The findings of the study show that CBT is promoted as one of the effective approaches to dealing with mental illness. It is recognized by the international guideline for psychotherapy treatments and hence can be used as the first-line treatment approach. Even though the authors of the article are qualified in terms of their professionals, the study lacks a clear method of study thus making it hard t decide on using it to support the efforts towards dealing with mental issues.

Carpenter, J. K., Andrews, L. A., Witcraft, S. M., Powers, M. B., Smits, J. A., & Hofmann, S. G. (2018). Cognitive-behavioral therapy for anxiety and related disorders: A meta‐analysis of randomized placebo‐controlled trials. Depression and anxiety, 35(6), 502-514. DOI: 10.1002/da.22728

The authors focused on examining the effectiveness of CBT for anxiety-associated illness using randomized placebo-controlled trials. The study method involved a literature review using 41 studies with patients diagnosed with various disorders like obsessive-compulsive disorder and general anxiety disorder among others. The authors are employed in the department of

psychological and brain sciences, the department of psychology and institute for mental health research, and the department of psychology among others. The findings of the study show that CBT is a moderately effective in the treatment approach of anxiety disorders than placebo. Therefore, this study is relevant based on the method adopted and its recent publication. Therefore, the interventions such as CBT can be used to address mental illnesses like post-traumatic stress disorder and panic disorder.

He, H. L., Zhang, M., Gu, C. Z., Xue, R. R., Liu, H. X., Gao, C. F., & Duan, H. F. (2019). Effect of cognitive-behavioral therapy on improving the cognitive function in major and minor depression. The Journal of Nervous and Mental Disease, 207(4), 232-238. DOI: 10.1097/NMD.0000000000000954

The article was aimed at investigating the effectiveness of the CBT on the improvement of the cognitive functions in minor depression and major depression. The author adopted the placebo-controlled single blond parallel-group randomized controlled trial. Looking at the affiliation of the authors, they are employed in the psychiatric department and department of psychiatry rehabilitation. This shows that the authors have specific qualifications in the area. The findings of the study show that CBT helps in the alleviation of the depressive symptoms of minor depression and major depression. The article is relevant and important to deal with the issue of mental disorders. Therefore, it can be advocated for or promoted to help in dealing with minor depression and preventing it from occurring. CBT is helpful in the promotion of an increased level of cognitive function.

Gautam, M., Tripathi, A., Deshmukh, D., & Gaur, M. (2020). Cognitive-behavioral therapy for depression. Indian journal of psychiatry, 62(Suppl 2), S223. Doi: 10.4103/psychiatry.IndianJPsychiatry_772_19

The authors focused on the study of cognitive-behavioral therapy for depression. The article provided is not based on a specific method. The authors are employed as consultant psychiatrists, the department of Psychiatry, the Department of MDG medical college, and consultant psychologists. The evidence from the study shows that CBT helps in the reduction of depressive symptoms as an independent treatment or when combined with other medication, helps in the modification of the underlying schemas or beliefs that assist in the maintenance of the depression, and helping in addressing different psychosocial issues. The information presented in the article is helpful for the research, however, it can be questioned since the authors failed to give clear data about the method of study adopted.

von Brachel, R., Hirschfeld, G., Berner, A., Willutzki, U., Teismann, T., Cwik, J. C., … & Margraf, J. (2019). Long-term effectiveness of cognitive-behavioral therapy in routine outpatient care: a 5-to 20-year follow-up study. Psychotherapy and psychosomatics, 88(4), 225-235. DOI: 10.1159/000500188

The authors focused on the investigation of the psychological functioning of selected outpatients who were on CBT for various mental disorders. The authors are employed at the mental health research and treatment center, the faculty of business and health, the faculty of psychology and psychotherapy, and the mental health research and treatment center. Based on these qualifications, the authors had the knowledge and information related to the information presented in the article. The author adopted the pre and post-treatment and from the pre-treatment to follow-up evaluation. The study outcomes show the long-term effectiveness of the CBT approach in addressing various groups of mental illnesses like depression, anxiety, and treating disorders. The study is important and can be adopted to tackle the presented issue of mental illness as it shows the effectiveness of the CBT.

Wright, J. H., Owen, J. J., Richards, D., Eells, T. D., Richardson, T., Brown, G. K., … & Thase, M. E. (2019). Computer-assisted cognitive-behavior therapy for depression: a systematic review and meta-analysis. The Journal of clinical psychiatry, 80(2), 3573. DOI: 10.4088/JCP.18r12188

The authors focused on evaluating the effectiveness of the computer-assisted forms of CBT for the major depressive disorder (MDD) ad examining the role played by the clinical support. The authors derived the data from databases such as Scopus with a focus on the RCT of the computer-assisted CBT for depression and the RCT of the mobile applications for CBT of depression. The outcome of the study shows that computer-assisted CBT with some support from the provider is effective in depressive symptoms. The study is, therefore, important for the selected issues since it shows the computer-assisted CBT approach as one of the interventions that can be adopted.

Yang, Z., Oathes, D. J., Linn, K. A., Bruce, S. E., Satterthwaite, T. D., Cook, P. A., … & Sheline, Y. I. (2018). Cognitive-behavioral therapy is associated with enhanced cognitive control network activity in major depression and posttraumatic stress disorder. Biological psychiatry: cognitive neuroscience and neuroimaging, 3(4), 311-319. DOI: 
10.1016/j.bpsc.2017.12.006

The author adopted the experimental research to determine how the CBT is linked to the improved cognitive control network activity with the major depression and the PTSD. The authors are employed at the department of psychological sciences, the center of neuromodulation in depression, and the department of biostatics among others. This is an indication that research is based on the expertise and skills possessed by the authors. The study outcome shows the dimensional abnormal in the process of activating the cognitive control regions that were linked to the depressive signs. The activation of the cognitive control regions was the same for patients with Major depressive disorder and PTSD under CBT. The study is important since it shows the role the in the treatment of mental conditions like PTSD.

Salomonsson, S., Santoft, F., Lindsäter, E., Ejeby, K., Ingvar, M., Öst, L. G., … & Hedman-Lagerlöf, E. (2020). Predictors of outcome in guided self-help cognitive behavioral therapy for common mental disorders in primary care. Cognitive Behaviour Therapy, 49(6), 455-474. DOI: 10.1080/16506073.2019.1669701

The study was aimed at investigating the predictors of the outcome for the guided self-help CBT for the clients with widely recognized mental illnesses within the primary care. Experimental research work was adopted. The analyses were performed utilizing logical and linear regression. The study reveals that the variables such as patient adherence to the treatment plan and the patient clinician’s estimation of the treatment response affect the overall outcome. The study is important as it reveals that the rating of high quality of life leads to remission and drop in the depression and an increased level of reliance to change. The authors are employees at the department of the centers for psychiatry and the department of neurobiology and neuroradiology among others. This is an indication of the qualification of the researchers in this area hence confirming the importance of the article when used to perform further research on the field of mental illness.

Cervin, M., Storch, E. A., Piacentini, J., Birmaher, B., Compton, S. N., Albano, A. M., … & Kendall, P. C. (2020). Symptom‐specific effects of cognitive‐behavioral therapy, sertraline, and their combination in a large randomized controlled trial of pediatric anxiety disorders. Journal of Child Psychology and Psychiatry, 61(4), 492-502. DOI: 
10.1111/jcpp.13124

The article is based on the use of the network intervention analysis (NIA) to analyze data collected from the RCT of pediatric anxiety disorder. The study outcomes show that all active treatments lead the positive outcomes. The most pronounced effect ranged from avoidance and psychological distress. Therefore, the combination of the CBT and sertraline appear to be having mechanism of action on psychological distress. The study is important for use since it reveals the effectiveness of a combined CBT and medication process. The information presented in the article is reliable since the authors have qualifications and professionals in the departments of psychiatry, department of clinical sciences, and the department of psychiatry and behavioral sciences.