Instructions For this assignment, you first will identify a topic of interest that you might want to pursue research. You are not tied to this topic when you reach the dissertat

SAGE Research Methods Video

Correlation and Regression – Spearman

Video Title: Correlation and Regression – Spearman

Originally Published: 2016

Publication Date: Sep. 30, 2016

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks, United States

ISBN: 9781506359229

DOI: https://dx.doi.org/10.4135/9781506359229

(c) SAGE Publications Inc., 2017

HERSCHEL KNAPP: Welcome to Practical Statistics for Nursing Using SPSS. This video shows how to process the Spearman correlation. You can watch the entire video or use the time slider to navigate directly to any time point. The Spearman correlation is quite similar to the Pearson

HERSCHEL KNAPP [continued]: correlation. Before proceeding, it's recommended that you first view the video Ch 11 -Correlation and Regression – Pearson.mp4. In terms of setup and results, the Pearson and Spearman correlations are quite similar. The Spearman correlation has two primary uses.

HERSCHEL KNAPP [continued]: First, remember that for a Pearson correlation, three pretest criteria must be satisfied, normality, linearity, and homoscedasticity. If there's a substantial violation among any of these, then the Spearman correlation is the better choice. Second, the Spearman correlation can be used

HERSCHEL KNAPP [continued]: to compare two ranked lists. For example, suppose a dietitian and a patient are each given five dietary cards and asked to arrange them from most to least preferable foods. The dietitian sorts the cards this way. And the patient sorts the cards this way. The Spearman's rho of 0.900 reveals a strong positive

HERSCHEL KNAPP [continued]: similarity among the orders of these two sorted lists. This example uses the data Ch 11 – Example 02 – Correlation and Regression.sav. This data set contains two variables, the dietitians food

HERSCHEL KNAPP [continued]: sequence and the patients food sequence. To process a Spearman correlational analysis, click on Analyze, Correlate, Bivariate, move Dietitian and Patient to Variables, uncheck Pearson and check Spearman.

HERSCHEL KNAPP [continued]: Click OK and it will process. The correlations table shows a strong positive correlation of 0.900 between dietitian and patient. We also see that the P value is less than 0.05, suggesting that this is a statistically

HERSCHEL KNAPP [continued]: significant correlation. This concludes this video.

SAGE (c) SAGE Publications Inc., 2017

SAGE Research Methods Video

Page 2 of 2 Correlation and Regression – Spearman

SAGE Research Methods Video

Mann-Whitney U Test

Video Title: Mann-Whitney U Test

Originally Published: 2016

Publication Date: Sep. 30, 2016

Publishing Company: SAGE Publications, Inc.

City: Thousand Oaks, United States

ISBN: 9781506359120

DOI: https://dx.doi.org/10.4135/9781506359120

(c) SAGE Publications Inc., 2017

HERSCHEL KNAPP: Welcome to Practical Statistics for Nursing Using SPSS. This video shows how to process the Mann-Whitney U test. You can watch the entire video, or use the time slider to navigate directly to any point. [Mann-Whitney U Test Video] The Mann-Whitney U test is quite similar to the t test.

HERSCHEL KNAPP [continued]: Before proceeding, it's recommended that you first view the video CH05-tTest.mp4. [Mann-Whitney U Test Overview] In terms of setup and results, the Mann-Whitney U test and the t test are quite similar. Remember that the t test has three pretest criteria,

HERSCHEL KNAPP [continued]: normality, n quota, and homogeneity of variance. If there are any violations among these criteria, the Mann-Whitney U test should be used instead of the t test. [ANOVA Prestest Checklist] To reiterate, when any of the pretest criteria for the t test

HERSCHEL KNAPP [continued]: are not satisfied, use the Mann-Whitney U test instead. [Mann- Whitney U Test Test Run] This example uses the data set Ch05-Example01-tTest.sav. To run the Mann-Whitney U test, click on Analyze,

HERSCHEL KNAPP [continued]: nonparametric tests, legacy dialogs, two independent samples. Move systolic BP to the test variable list, and move group to the grouping variable. Click on group, question mark, question mark,

HERSCHEL KNAPP [continued]: then click on define groups. For Group 1, enter 1. For Group 2, enter 2. This is because we define Drug A as Group 1 and Drug B as Group 2. Click Continue. Click OK, and it will process.

HERSCHEL KNAPP [continued]: [Mann-Whitney U Test Results] The Mann-Whitney U test produced a p value of 0.011. Since this is less than or equal to 0.5, this tells us that Drug A statistically significantly outperformed Drug B when it comes to lowering blood pressure.

HERSCHEL KNAPP [continued]: This concludes this video.

SAGE (c) SAGE Publications Inc., 2017

SAGE Research Methods Video

Page 2 of 2 Mann-Whitney U Test

School of Business

2

First Edition. Published by the Center for Teaching and Learning, Northcentral University, 2020

Contributors: John Bennett, Mary Dereshiwsky, Robert Dodd, David Fogarty, John Frame, Raymie Grundhoefer, Larry Hughes, Sharon Kimmel, Vicki Lindsay, Edward Maggio, Gordon McClung, NCU Library Team, Susan Petroshius, Lonnie K. Stevans, Gergana Velkova, Steve Ziemba

In addition to the collaborative process that engendered this guide, it was also informed by the quantitative methods and statistics courses in the School of Business.

For comments or suggestions for the next edition, please contact John Frame: [email protected]

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TABLE OF CONTENTS Foreword

Introduction

Research Ethics and the IRB

Research Questions

Four Main Designs

Population and Sample

Sampling Method, Sample

Design, and Sample Size

Surveys and Questionnaire Design

Pilot Study

Datasets

Analyzing Secondary Data

Observational Research

Multivariate vs. Univariate Analysis

Measurement of Variables

Descriptive Statistics and Exploratory Data Analysis (EDA)

Inferential Statistics

Alpha Level (level of significance, or p-value)

Hypotheses

Hypothesis Diagrams

Hypothesis Testing

T-Test

Analysis of Variance (ANOVA)

ANOVA Examples

Correlation

Regression Analysis

Factor Analysis

Power (Statistical Power)

Power Analysis

Measuring Validity and Reliability

Internal/External Validity

Selection of Parametric vs. Nonparametric Techniques

Presentation of Statistical Results and Explaining Quantitative Findings in a Narrative Report

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Foreword

Dear School of Business Community, Welcome to the Best Practice Guide for Quantitative Research Design and Methods in Dissertations! With well over 700 doctoral students in the School of Business working on their dissertation this year, this guide serves as an important resource in helping us shape and implement quality doctoral-level research. Its primary purpose is to offer direction on quantitative research in School of Business dissertations, serving students as they craft and implement their research plans, and serving faculty as they mentor students and evaluate research design and methods in dissertations. We encourage you to explore this guide. It is filled with details on important topics that will help ensure quality and consistency in quantitative research in the School of Business.

Thank you to the faculty and staff of the School of Business and wider NCU community that worked to cre- ate this guide. It is a great contribution to our School, and each of these individuals played an important role in its development. – School of Business Leadership Team

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Introduction As an accredited university, NCU aims to have robust expectations and standards for dissertations produced by its students. This guide, developed collaboratively by NCU School of Business (SB) faculty, aims to provide guidance on best practice in quantitative research design and methods for SB dissertations.

While this guide can serve as a refresher to those less familiar with quantitative methods, it will also help ensure good practice and rigor across com- mittees and students. To that end, this document is a guide to help students, as well as faculty, when judging the merits of student dissertation prospec- tuses, proposals, and manuscripts. Students should be familiar with the best practices in this guide and apply them to their dissertation.

Additional supports related to quantitative research design and methods are available in the NCU Dissertation Center (including several webinars),

and statistics experts are available for 1-1 coaching through the NCU Academic Success Center.

Importantly, before students plan to embark on a quantitative research design, they need to be comfortable with quantitative analysis, including data analysis computer software, such as SPSS. If students are not comfortable with their level of skill in quantitative analysis, it is recommended that they consider how qualitative methods could be used to explore their research interests. Students interested in qualitative methods should consult the SB’s Best Practice Guide for Qualitative Research Design and Methods in Dissertations, published in 2019, and available in the Dissertation Center.

Research Ethics and the IRB Research involving human participants involves certain ethical responsibilities on the part of the student and dissertation Chair. These responsibili- ties are an important part of the overall educational experience for the student, in that they learn that

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obtaining data and other information from partici- pants needs to be done in a manner that respects the rights of the participant and the wishes of other organizations that might become involved in the research. As part of the research ethics review process, the Institutional Review Board (IRB) at NCU serves as a resource to provide guidance to students and faculty to ensure the ethical principles of Respect for Persons, Beneficence, and Justice are incorporated into the research design. The IRB review process is as much a part of students’ doc- toral education as any other part of the dissertation process. The intention is not only to ensure studies are conducted ethically, but also that students un- derstand the importance of ethics in research and how to design and conduct research that is consis- tent with federal regulations.

It is important to keep in mind that recruitment and data collection can only occur after receiving NCU IRB approval. The IRB process starts with IRB Manager, an online system that facilitates the submission and management of studies for review. Students should plan ahead and be sure to leave time for the IRB review to take place, as it may take up to 15 business days after submission of the IRB application to receive notification of the IRB’s de- termination. Also, it is possible that the application will not be approved the first time through, due to the need for additional information or clarification. These factors need to be kept in mind when con- structing the study timeline. Additional variables that can impact the timeline include: securing site permission, site IRB approval (if applicable), in- ternational research, research involving sensitive topics or vulnerable populations, research in one’s place of employment, research involving the De- partment of Defense or Veterans’ Affairs, and the development of appropriate recruitment materi- als and an informed consent form, or, for studies involving minors, child assent and parental consent

forms. These and other items need to be submitted as part of the IRB application and can significantly delay the review process if not present. For exam- ple, the inclusion of an informed consent form that does not use (or where the researcher has altered) the NCU informed consent form template will result in the application being returned to the student. As indicated in the Student-Chair Engagement section of this guide, it is important for students to work closely with their Chair in the lead up to the IRB approval process.

A variety of resources are available for students and faculty as they navigate the IRB process. Guid- ance materials are available directly within IRB Manager and are easily accessed from within the application. Resources are also available via the NCU Dissertation Center. Finally, when questions come up, the IRB can be contacted at [email protected] When doing so, be sure to include the name of the student in the subject line.

Please review the IRB website for further informa- tion and resources: https://ncu.libguides.com/irb/ home

Research Questions Research Questions outline the problem to be inves-

tigated in a study, stated in the form of a question.

Research questions that describe data are called

descriptive. Descriptive research questions typically

ask “How,” or “What.” (As explained elsewhere in

this guide, descriptive research design and meth-

ods that are solely descriptive are not sufficient for

a doctoral-level dissertation. Thus, rigorous research

questions that go beyond descriptive research need

to be included in a dissertation, as explored below.)

Research questions that compare one or more

groups are called comparative. Comparative re-

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search questions typically ask, “What is/are the

difference(s)” (for example, what are the differences between X and Y?).

Research questions that examine relationships are called correlational, or relationship, questions. More specifically, these questions typically ask, “What is the strength and direction of a linear rela- tionship between the two variables in question.”

Research questions that consider predictions are called predictive research questions. These types of questions typically ask, “To what extent does X pre- dict Y?” Predictive analysis may have one or many independent variable(s), which may be expressed as predictive variables. The dependent variable may be expressed as the outcome variable.

Each research question, with the exception of de- scriptive research questions, contains a minimum of two hypotheses: the null hypothesis and the alterna- tive hypothesis.

Students are encouraged to get 1-1 coaching at the NCU Academic Success Center on their research questions and/or sign up for live group sessions of- fered weekly by the NCU Academic Success Center. More in formation can be found at: https://vac.ncu. edu/resources-for-statistics/

References and/or Suggested Reading:

Cramer, D., & Howitt, D. (2004). The SAGE dictio- nary of statistics. London: SAGE Publications, Ltd.

http://dissertation.laerd.com/how-to-structure-quanti- tative-research-questions-p2.php

Four Main Designs There are four main designs that can be used with a quantitative methodology: experimental, quasi-ex- perimental, correlational, and descriptive. Students need to look at their research study to figure out which design will be most appropriate to answer their research questions (but, as indicated else- where in this guide, a descriptive design is insuffi- cient for a doctoral-level dissertation). The Methods Map Online Tool (see link below) is a fun and inter- esting interactive website that provides an overview of a number of methodological procedures.

Before researchers can begin to think about their research design, it is essential for them to begin at the foundation of the business research process: defining the problem. It is extremely important to define the problem carefully because this will deter- mine the purpose of the research and the research design.

A brief introduction to the four main research de- signs are as follows:

Experimental Research

Experimentation is conducted in order to test a causal hypothesis (that is, if a researcher wants to determine if an independent variable (X) is the sole cause of any change in the dependent variable (Y)). In an experiment, a researcher manipulates the independent variable and measures its impact on the dependent variable while, at the same time, controlling for all other variables that may have influenced the dependent variable. These are referred to as extraneous or potentially confounding variables. An experiment is internally valid if it can be shown that the independent variable is the sole cause of any change in the dependent variable. In order to do so, three pieces of evidence are need- ed: (1) for X to be a cause of Y, X must precede Y

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in time; (2) X and Y must vary together; (3) for X to be a cause of Y, other possible causes of Y (alterna- tive explanations) must be eliminated. In contrast to internal validity, external validity refers to whether the results of the experiment can be generalized to other populations, settings, etc. For instance, with respect to generalizing to the population, there would be better external validity if the sample was selected randomly from the population. This would have no impact on internal validity, however. Note that there is often a tradeoff between internal and external validity and the experimental setting (a lab vs. field experiment). A laboratory experiment is an artificial setting that allows the researcher better control over extraneous/potentially confounding variables. However, the artificiality of a lab ex- periment tends to lessen the external validity since a researcher will want to be able to generalize to a more realistic setting. Essentially, laboratory vs. field experiments represent opposite ends of a continuum having to do with the artificiality of the setting.

Quasi-Experimental Research

Quasi-experimental designs are used when it is not viable to randomly assign participants to treatment groups. In many real-life social situations, groups of interest may be naturally occurring or pre-ex- isting. There may also be ethical reasons when randomization to groups is not practical. Manipu- lation of an independent variable (also referred to as a treatment variable), comparison groups (also referred to as experimental units), and outcomes measures are present in quasi-experiment designs. Unequivalent groups are also present because of the inability to randomly assign participants to comparison groups. Because of the inability or decision to not use random assignment to groups, it is difficult to compare and infer treatment-caused changes. Quasi-experiment designs are used by researchers in these situations. Common non-time

series quasi-experiment designs include Cohort Designs, Counterbalanced Design, Non-equivalent Control Group Design, Regression-discontinuity Design, Separate-Sample Pretest-Posttest Designs, and Separate-Sample Pretest-posttest Control Group design. Correlational Research

If the research questions focus on a relationship between multiple variables, a correlational design will likely be used. Research is correlational when at least two, and often more, variables/conditions are observed and measured and the extent of the relationship is estimated based on tools such as the Pearson Product Moment Correlation, the Spear- man Rank Correlation Coefficient, or even Kendall’s Rank Correlation Coefficient. In fact, correlational research is often descriptive in that the associations are reported to the reader, often in the same table, as the means and standard deviations. In a pub- lished research study, a reader can use correlations and the other descriptive statistics to get a sense of the data before reading about t-tests, ANOVA, or multiple regression, whichever the author(s) used in their analysis. This causal inference is distinct from prediction or forecasting and a common error made by students and novice researchers (Cook & Stanley, 1979). A caution in correlational research is that, as the famous phrase goes, “correlation does not imply causality.” There are no “depen- dent” or “independent” variables in correlational research – we’re simply comparing the variables on the basis of association and cannot assert that the effect of one causes another.

Descriptive Research

Descriptive research (see the section, “Descriptive Statistics and Exploratory Data Analysis (EDA)” later in this guide) describes individuals in a study that was typically conducted in one of three ways: (a) observational – viewing and recording partici-

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pants (See “Observational Research” in this guide); (b) case study – in-depth study of an individual or group of individuals; and (c) survey – a brief interview or discussion with an individual about a specific topic. Descriptive research designs are common in fields related to behavioral and social sciences to observe phenomenon such as: natural behavior, consumer habits, individual morality, and ethical climate. The observations of the subject should occur in an unchanged natural environment. The weaknesses of descriptive research designs are that observational studies are not repeatable and not replicable. Descriptive research designs are often designed in a manner which allows it to be a precursor to quantitative research. Descriptive research does not involve statistical testing, thus it is considered to lack reliability, validity, and scien- tific rigor. As discussed elsewher e in this guide, a descriptive research design alone is insufficient for a doctoral-level dissertation at NCU.

References and/or Suggested Reading:

Cook, T.D. & Stanley, D.T. (1979). Quasi-experimen- tation: Design and analysis issues for field settings. Boston, MA: Houghton Mifflin.

Research Methods Knowledge Base website: https:// socialresearchmethods.net/kb/quasiexp.php

The SAGE handbook of social research meth- ods (2008). London, United Kingdom: SAGE Publications, Ltd. https://doi-org.proxy1.ncu. edu/10.4135/9781446212165

Sage Methods Map Online Tool: http://methods. sagepub.com.proxy1.ncu.edu/methods-map

Population and Sample The population represents the totality of units under study, or to whom we wish to generalize or project

the results of statistical research. These are usually, but not always, people.

Usually, it is not practical to do a census of an entire population in a single research study, due to time and cost factors. For this reason, it is neces- sary to select a sample from that population.

A sample in a dissertation needs to be a substantial number (see “Power Analysis” in this guide), and should be determined based on best practices in quantitative research. Students should be aware that quantitative research demands a suitable amount of data, and that the response rate from samples (such as the response rate for surveys), will typically be very low. Thus, a large number of persons will need to be surveyed in order to obtain an adequate amount of data.

After sampling, it is possible to generalize the sample results to the population from which it was

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selected. Here are some commonly applied ways to select a sample in quantitative research:

Simple random sample:

Every element of the target population has an equal chance of being selected for the sample. This is es- pecially valuable when doing experimental studies.

Stratified sample:

In this sampling method, it is recognized that there is not one overall homogeneous population, but, instead, subpopulations where the subgroups differ from one another. For example, a researcher may want to see if there is a significant difference in an average number of units of Product X purchased by men and women. The researcher would subdivide, or ‘stratify,’ the overall population into men vs. women (strata or subgroups), and randomly select a sample from each gender to ensure it is ade- quately represented in the overall sample.

Cluster sample:

In this sampling method, the researcher randomly draws intact groups, (‘clusters’) instead of individ- uals, for the study. For instance, a cluster could be an entire division or department of an organiza- tion. The researcher then includes all sampling units (e.g., persons, employees) in that randomly drawn cluster in our study. The idea is to simulate the randomness of a true random sample, but without having to select individuals one by one.

Systematic sample:

In this sampling method, the researcher lists the elements of the target popu

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