briefly compare the uses of the research designs employed in the studies.

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·         Introduction

 

Quantitative research methodology uses a deductive reasoning process (Erford, 2015, p. 5). It is based on philosophical assumptions that are very different from those that support qualitative research. Quantitative studies fall under what is broadly described as a positivist perspective. Epistemologically, knowledge is something that is believed to be objective and measurable, and the nature of reality (that is, ontology) is such that there is one fixed, observable, and definable reality. Quantitative approaches to research emphasize the objectivity of the researcher, and because a goal is to uncover the one true reality, values (axiological assumptions) and the subjective nature of experience are not likely to be examined.

 

Quantitative Research Designs

 

Quantitative research can be categorized in different ways. Brief descriptions of some designs appear below. The chosen research design is determined by the nature of the inquiry, that is, what the researcher wants to learn by conducting the study. Counseling Research: Quantitative, Qualitative, and Mixed Methods thoroughly describes several major reseach.

 

Experimental Research

 

Experimental research, one of the quantitative designs, involves random selection and random assignment of subjects to two or more groups over which the researcher has control. This is what distinguishes experimental studies from the other designs. Experimental studies in counseling are not that common, because many research questions do not lend themselves to random selection and assignment for ethical reasons. Experimental studies compare the effect of one or more independent variables on one or more dependent variables. Independent variables fall into two broad categories. One type of independent variable involves measuring some characteristic inherent in the study’s participants, such as their age, gender, IQ, personality traits, income, or education level. These demographic or blocking variables are not something which the researcher can manipulate, though the researcher can statistically control for them. The treatment or experimental conditions that the researcher sets up is the other type of independent variable, which is unique to experimental designs. The element of control is what permits researchers to conclude that one variable has caused a change in another variable.

 

Quasi-Experimental Research

 

Quasi-experimental research designs come in many different forms. Like experimental research, the researcher aims to compare the effect of the independent variable under their control on the dependent variable. However, the researcher does not or cannot randomly assign individual participants to treatment and control groups, so cause-and-effect relationships cannot be as strongly inferred from the results. Pre-existing conditions of one group in comparison to the other may confound the findings. An example might be a study to examine the potential effects of a new curriculum aimed at reducing bullying in a school district. You provide the training to the fourth through sixth grades in one school but not in another, assuming a large school district in which there are two or more middle schools. You could randomly select which school receives the curriculum (treatment group) and which does not (control group), but you cannot assign individuals to either group. With quasi-experimental studies, it is particularly important for the researcher to carefully consider the threats to validity in the interpretation of the results.

 

Factorial Designs

 

Quantitative studies which have the large sample sizes required to maintain sufficient statistical power may be used to examine the interactive effects of more than one independent variable. For instance, one might examine whether or not people with different personality types, as measured on the Myers-Briggs Type Indicator, respond differently to different types of counseling treatments, while also examining whether or not men and women respond in the same ways to various treatments. When previous research suggests that there may be differential effects on people due to some demographic factor, then one would need to adopt a factorial design to control for these differential effects. Otherwise, the validity of the study could be limited.

 

Descriptive Designs

 

Descriptive studies attempt to improve understanding of a phenomenon, either by describing it in succinct quantitative terms or by describing its underlying factors. The goal is not to establish a cause-and-effect relationship, but to use statistics (such as descriptive statistics, correlation, or multiple regression) or data reduction procedures (such as cluster analysis, factor analysis, and multidimensional scaling) to better understand a phenomenon or relationship. Causation cannot be inferred when descriptive designs are used.

 

Meta-Analysis

 

Meta-analysis is a statistical procedure which is also considered a non-experimental design (Erford, 2015, p. 139) for determining the degree to which a number of studies examining the same phenomena are in agreement. It takes the standard literature review to another level where statistics are applied in determining an overall effect size. In essence, meta-analysis combines several studies and analyzes them as though they were one big study.

 

Reference

 

Erford, B. T. (2015). Research and evaluation in counseling (2nd ed.). Stamford, CT: Cengage.

 

Objectives

 

To successfully complete this learning unit, you will be expected to:

 

1.    Summarize the methodological structure of quantitative studies.

Assignment

 

Quantitative Research Articles Summary

 

  • After studying the introduction to this unit and completing the study activities, briefly compare the uses of the research designs employed in the studies. What is each research design used to determine (for example, relationships between variables, differences among groups)? For one of the quantitative studies, summarize how the quantitative studies, summarize how the sampling, data collection, and data analysis procedures worked together to address the hypothesis. The post should be written in your own words, not direct quotes from the article. Incorporate material from the course text in a meaningful way.The suggested length for this post is 400–500 words.

     

Write Section 5 of the DAA. Discuss the conclusions of the repeated measures ANOVA as it relates to the research question. Conclude with an analysis of the strengths and limitations of repeated measures ANOVA.

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tep 1. Write Section 1 of the DAA. Provide a context of the wk5data.savdata set. Specifically, imagine that you are a clinical researcher studying a new treatment for anxiety. To determine treatment efficacy, you monitor the anxiety levels of clients over five weeks. Anxiety symptoms are quantified with a symptom checklist, and the data are entered into SPSS. Week 1 represents the baseline number of anxiety symptoms. Week 5 represents the number of anxiety symptoms at the conclusion of treatment. In Section 1 of the DAA, articulate your within-subjects factor and the outcome variable. Specify the sample size of the data set. Based on your visual inspection of the raw data in wk5data.sav, speculate on the overall trend in recorded symptoms from Week 1 to Week 5.

Step 2. Write Section 2 of the DAA. Assume that the sample is too small to assess multivariate normality. Instead, focus your analysis in Section 2 on the sphericity assumption. Provide the SPSS output for the Mauchly test. Report the results of the Mauchly Wand interpret it in terms of the sphericity assumption. If sphericity is violated, analyze the three epsilon estimates (Greenhouse-Geisser, Huynh-Feldt, and lower bound) and justify your decision for selecting one of the three epsilon corrections reported below in Section 4 Interpretation.

Step 3. Write Section 3 of the DAA. Specify a research question related to the repeated measures ANOVA. Articulate the null hypothesis and alternative hypothesis. Specify the alpha level.

Step 4. Write Section 4 of the DAA.

  • To provide context, paste SPSS output of Weeks 1–5 descriptive statistics. Report these descriptive statistics in your narrative.
  • Next, paste SPSS output of the estimated marginal means plot of Weeks 1–5. Provide an interpretation of this figure.
  • Then paste the SPSS output for the test of within-subjects effects.
  • Report F, the degrees of freedom based on your epsilon correction selected in Section 2 (if epsilon correction is not necessary, report Sphericity Assumed df), the Fvalue, the p value, the effect size, and interpretation of the effect size.
  • Interpret the results against the null hypothesis. Next, paste the SPSS output for the tests of within-subjects contrasts if the overall F null hypothesis is rejected.
  • Make sure in SPSS that the contrast is designated as “simple” with Week 1 set as the baseline comparison. Report the Ftests for the simple contrasts and interpret them.

Step 5. Write Section 5 of the DAA. Discuss the conclusions of the repeated measures ANOVA as it relates to the research question. Conclude with an analysis of the strengths and limitations of repeated measures ANOVA.

1. Briefly describe how many studies are represented in this meta-analysis and what the main findings of the meta-analysis were.

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Preliminary Reading: McCullough, M.E. (1999). Research on religion-accommodative counseling: Review and meta-analysis. Journal of Counseling Psychology, 46(1), p. 92–98. (attached)

Thread Prompt: As you have been studying this module/week, a meta-analysis is a study that statistically combines the results of several studies on a similar topic. By calculating an overall mean effect size, meta-analyses help provide information on the effects of treatments over and above the individual studies themselves. McCullough’s meta-analysis examines several studies that compare Christian counseling techniques to standard, non-religious counseling techniques. Imagine you are working in a community clinic with a variety of counselors, and you are interested in alerting the clinic supervisors to this meta-analysis and possibilities for future research that could be conducted in your clinic. Write a professional email to your supervisor and include the following required information:

1. Briefly describe how many studies are represented in this meta-analysis and what the main findings of the meta-analysis were.

2. In your opinion, why is this meta-analysis important in the context of Christian counseling, even though the results are not significant? Support your answer with information from the article.

3. Propose a study: In the meta-analysis discussion section, the subsection entitled “The Last Word” contains 4–5 suggestions for further studies in Christian counseling. Choose 1 that interests you and briefly propose a quantitative study to investigate this topic. The description of your proposed study must include:

a) Your research question;

b) A short description of the target participants (gender, age, diagnosis or problem area, etc.);

c) The independent and dependent variables, and how many levels/groups the independent variable would have;

4. The type of outcome measure to serve as your dependent variable, as well as its level of measurement (nominal, ordinal, scale?). The measure could be a questionnaire, self-report, therapist rating of illness severity, number of diagnostic criteria met, etc. This can be something you come up with yourself. You do not have to name an actual existing instrument unless you know of one.

5. Include references in current APA style for any sources, including the meta-analysis.