Unit VI Assignment

RCH 7301, Critical Thinking for Doctoral Learners 1

Course Learning Outcomes for Unit VI Upon completion of this unit, students should be able to:

4. Assess theoretical research methodologies in contemporary business scholarship. 4.1 Discuss a population and sampling frame for a given scenario. 4.2 Justify the use of a selected sample.

7. Implement a critical thinking process for business research methodology.

7.1 Describe a valid and reliable research instrument. 7.2 Compose an appropriate research design for a study.

8. Compose scholarly business research writing.

8.1 Compose a response to issues and questions surrounding quantitative research methods.

Course/Unit Learning Outcomes

Learning Activity

4.1

Unit Lesson Chapter 6 Chapter 24 Unit VI Assignment

4.2

Unit Lesson Chapter 6 Chapter 24 Unit VI Assignment

7.1

Unit Lesson Chapter 6 Chapter 24 Unit VI Assignment

7.2

Unit Lesson Chapter 6 Chapter 24 Unit VI Assignment

8.1

Unit Lesson Chapter 6 Chapter 24 Unit VI Assignment

Required Unit Resources Chapter 6: Quantitative Research Design Chapter 24: Analysing and Presenting Quantitative Data

UNIT VI STUDY GUIDE Quantitative Research Design: Exploration

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Unit Lesson

Quantitative Research Design Quantitative research measures and defines elements through the collection of data, the analyzation of data, and the application of the data to a theoretical framework. Quantitative research design can be categorized into four main types, which are listed below:

• descriptive where a subject is measured once; descriptive quantitative research establishes associations between variables;

• correlational where the relationship between study variables is investigated; • quasi-experimental where any cause-and-effect relationship is determined; and • experimental where a subject is measured before and after the treatment and where any cause-and-

effect relationship is determined (Drummond & Murphy-Reyes, 2018). The differences among the four types have to do with the amount of control that the researcher designs for the variables in the experiment or study. Quantitative research makes use of tools (e.g., graphs, linear regressions, hypothesis testing) to organize and analyze the gathered data. Researchers gather data from quantitative studies via experimentation (i.e., where an independent variable’s effects on a dependent variable are measured) or through surveys, which are designed along a rating scale. Because the focus of questions for a quantitative study is small, the quantitative study can be very narrow and limited in scope. That is both a strength and a weakness. A quantitative study on a very focused sample can yield reliable data about that group and research question, and the study can be replicated elsewhere to test a theory or hypothesis again. However, the collection of data and a focused sample size can also mean that the study’s results or conclusions are not applicable over a wider area or grouping of people, and, therefore, can have limited use unless the study is replicated repeatedly to support the findings. Data trustworthiness is determined by the credibility of the data collection, the data’s transferability, the data’s dependability, and the data’s confirmability.

Descriptive Quantitative Research A researcher who designs a descriptive study wants to know the nature of how things are as they are. Descriptive quantitative research either identifies the characteristics of a phenomenon or explores correlations among phenomena. In terms of survey research, which is the most commonly deployed type of descriptive research, the researcher seeks to describe the characteristics of a larger population. Descriptive research examines phenomena as they are and does not involve changing a situation that is being investigated. Since the researcher does not practice control over any variables in the study design, descriptive research cannot be used to determine cause-and-effect relationships. A descriptive research study might employ data collection strategies such as sampling, observing, or interviewing, which take on specific forms when the researcher wants them to yield quantitative data. Descriptive research designs include observation studies, correlational research, development studies, and survey-based research (Oakshott, 2019). All of these designs yield data that can be worked on through statistical analysis. Within the designs, survey-based research is the most commonly used type of descriptive quantitative research.

Correlational Quantitative Research According to Creswell and Creswell (2018), a correlational study can examine the extent to which differences in one characteristic or variable are related to differences in one or more other characteristics or variables. A correlation exists if the dependent variable increases (moves toward +1.0) or decreases (moves toward -1.0) in a predictable fashion when the independent variable increases. Correlational research seeks to establish a relationship between variables that do not readily lend themselves to experimental manipulation or control.

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In a simple correlational study, a researcher gathers data about two or more characteristics of a study population. The numbers that are used reflect measurements of the characteristics, such as customer satisfaction ratings between two locations, employee satisfaction ratings with and without a type of employer- provided service, and so on. In a correlational study, each characteristic has two identifying numbers that are used to calculate the correlational coefficient (r). A perfect correlation is +1.0 or -1.0. If the characteristics are not related or are only remotely related, the coefficient is closer to 0. While a correlational relationship can be measured, it does not imply a cause-and-effect relationship. Researchers must be careful to avoid claiming causality, even if a correlation close to +1.0 or -1.0 is found. Influence can be present among correlating characteristics, but researchers cannot infer a cause-and-effect relationship based on correlation alone. Consider the following example: The Earth’s atmospheric temperature has demonstrably risen since pirates in tall ships stopped sailing the high seas, but the absence of pirates did not cause the rise in temperatures—even though the correlation is close, if not perfect. Correlational research can describe the homogeneity of heterogeneity of the variables; it can describe the degree to which the variables are intercorrelated by computing the correlational coefficient r.

Quasi-Experimental and Experimental Quantitative Research Experimental and quasi-experimental research is used to test a hypothesis and, even further, an intervention involved. An intervention is the main factor in experimental research. To measure the effects of an intervention, the researcher has to identify the variables and discern the comparisons that are going to be made between or within the group(s). Research must make comparisons to examine relationships between dependent and independent variables. Experimental designs have an intervention, a control group, and randomization of participants in the study’s groups. A quasi-experimental design has an intervention, but it has no randomization of participants in the experimental and control groups.

Experimental Design

Quasi-Experimental Design

Intervention X X

Control Group X

Randomization of Participants

X

Many experimental research designs measure a dependent variable before and after an intervention, with before and after measurements being the minimum. In a cross-sectional study, data is collected at the before and after points, so a cross-sectional design can work for a project such as a dissertation study. A good experimental or quasi-experimental quantitative research design can aid you in answering the study’s research question at the same time the design reduces threats to the design’s validity. As a researcher, asking and answering the following eight questions can help to address key features of an experimental or quasi-experimental research design.

• What is the research question, and will the study entail an intervention? • Rather than staging an intervention, will the researcher observe participants and take

measurements? • What are the variables? • When and how often will the researcher collect data or take measurements? • What is the setting for the study? • If the intervention study has multiple groups, how will the researcher randomly assign participants to

the groups?

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• If the study involves humans and an intervention, how will the researcher, participants, and anyone else involved in administrating the study be blinded from knowing the groups to which participants were assigned?

• What controls will be put into place to reduce the influence of variables that are not involved in the study?

Experimental research designs contain an intervention, so they seek to answer questions about differences (e.g., the difference between an outcome that is measured in both the experimental and the control group). On the other hand, correlational studies look at associations. An experimental study is valid only if the following characteristics are present:

• an intervention, where the researcher manipulates the independent variable; • control for the influence of variables not being measured in the study, such as randomization and

control groups; and • randomization, where the researcher randomly assigns each participant so that a participant has a

50/50 chance of being assigned to either the intervention or the control group. Randomization is important to deducing the result of the intervention at the end of the experiment.

Below, study two tables that present information about statistics that examine differences and associations between and among variables.

Name Test statistic Purpose Number of groups Independent samples t-test

t Test the difference between the means of 2 independent groups.

2

Paired samples t-test t Test the difference between the means of 2 paired groups (before and after measurements, which are typical paired samples t-tests).

2

One-way analysis of variance (ANOVA)

F Test the difference among means of >2 independent groups for one independent variable (that has >1 level).

> 2

Two-way analysis of variance (ANOVA)

F Test the difference among means for 2 independent variables, where each can have >1 level.

> 2

Table 1. Quantitative research design: Statistics that examine differences using an interval/ratio measurement level

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Name Test statistic Purpose Measurement of dependent variable

Pearson product- moment correlation

r Measure strength and direction of relationship between 2 variables.

Interval/ratio

Spearman rank-order correlation

ρ Measure the strength and direction of the relationship between 2 variables (nonparametric).

Ordinal, interval, or ratio

Linear regression Predict the value of a dependent variable, and measure the size of the effect of the independent variable on a dependent variable while controlling for covariates.

Interval/ratio

Logistic regression This is the same as linear, but it is used when the dependent variable is binary.

Binary/dichotomous

Table 2. Quantitative research design: Statistics that examine associations Refer to these tables in conference with your mentor and dissertation chair to make decisions about quantitative research designs.

References Drummond, K. E., & Murphy-Reyes, A. (2018). Nutrition research: Concepts and applications. Jones &

Bartlett Learning. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods

approaches (5th ed.). SAGE. Oakshott, L. (2019). Essential quantitative methods: For business, management and finance (7th ed.). Red

Globe Press.