C.R.M.E. is a workbook for anyone needing (or wanting) to do a research project that can stand the test of claiming scientific significance. The workbook leads the reader in a logical progression from the measurement of variables in the research design

CREDIBLE RESEARCH MADE EASY

A Step by Step Path to Formulating Testable HypothesesBy DORCAS MLADENKA

iUniverse, Inc.

Copyright © 2012 Dorcas Mladenka
All right reserved.
ISBN: 978-1-4759-2030-7

Contents

Acknowledgements....................................................................viiIntroduction........................................................................ixPart 1 - A Sample Project; Defining and Measuring Variables.........................1Part 2 - Formulating Hypotheses.....................................................10Part 3 - Collecting the Data........................................................21Part 4 - Theoretical Distributions and Looking for Significance.....................25Part 5 - Completing Your Own Research Project.......................................32Appendix: Outline and Notes for a Research Report...................................37

Chapter One

PART 1

A SAMPLE PROJECT; DEFINING AND MEASURING VARIABLES

Professor M teaches the business research course. She wants all of her students to do well in the course, and she wonders what elements might account for differences in their success levels. The problem area, then, is the research course and the success (or failure) of students taking the course.

Professor M decides that "success" in the course (DEPENDENT VARIABLE—DV) would have to be determined by the final grade in the course. She also decides, upon reflection, that since all students get the same material and the same assistance in the course, there may be other elements (INDEPENDENT VARIABLES—IVs) involved. She determines to explore these variables through an analytical, investigative research project.

You will use Professor M's research project as a sample to follow in doing your own research. This first section will include the following:

-> Research Design

-> Research Questions

-> Measurement of Variables

-> Operational Definitions

THE RESEARCH DESIGN

Let's assume the following research design. Prof. M is interested in what variables influence the grade that research students made in the business research course (your dependent variable). She selects five independent variables to include in her research, and a diagram of her research model looks like this.

Gender

Major Semester Hrs Research Grade Study for the Course Interest in Statistics (IVs) (DV)

PROBLEM STATEMENT: (Ask a question about the variables)To what extent do the variables of gender, major, course load, study, and interest in statistics predict the final research grade.

THE RESEARCH QUESTIONS (Another way of explaining your research Design)

You will now make the sample project your research as you study the process. You are theorizing relationships among your variables.

You are interested in the relationship that each of the IVs had to the dependent variable: how does each IV relate to the research grade? You will indicate the kind of relationship you are interested in researching by formulating questions—the research questions. In order to do that you will of course need a measurement of each variable, and that measurement has to be a quantity—a number.

RQ 1 So lets get started, first of all, what do you mean by the variable. When you say "research grade," you mean, of course, the average (mean) research grade made by students ... and you must specify, for example, students who took the course in the last two semesters: Spring XX or Fall XX.

Your first research question of interest then is a research grade—a very quantifiable measure. The mean of all the research grades in your sample is the measure for your DV. (The next section is on Measurement Scales and Operational Definitions)

RQ 2Was there a difference between male and female research grades? (IV Gender: DV Grade)

RQ 3 Did the research grade differ according to the students' majors? (IV Major: DV Grade)

RQ 4 Did the total number of semester hours a student carried during the semester the research was taken influence the research grade? (IV Semester Hrs.: DV Grade)

RQ 5 Did the amount of Study have an influence on the research grade? First you have to "clean up" ambiguous terms: amount ... study ... By "amount" do your mean how many hours or how often? By "study" do you mean specifically studying research? (IV Study: DV Grade)

RQ 6 Was there an association between how "interested in statistics" the student was and that student's research grade? Again you have to make clear what "interest" really means. (IV Interest in Statistics: DV Research grade.)

Good news: you are getting closer to developing hypotheses for your research project.

So far you have formulated six research questions: one having to do with data for the DV, the other five having to do with the relationship of each IV to the DV. In order to learn how to do a few more statistical tests, you will also look at relationships of one IV to another IV. There is, however, a more important reason for doing this. You will have collected the data, so it will be a simple matter to do the relationships. And you might find some surprises.

In this example we will add the following research questions:

RQ 7. Were marketing majors "more interested" in learning research than other majors? (IV 2: IV 5)

RQ 8 Was there a difference in study times between male and female students? (IV 1: IV 4)

There are two things you must do before your begin stating your research questions in the form of hypotheses.

1. You must be very precise about what you mean by the variable because you have to quantify it—make it measurable—one number for each variable.

2. You must determine how you will "tap" the variable—how you will collect the data.

The next two sections will help you perform these two tasks.

MEASUREMENT SCALES

Once you have determined the variables to use in your study, the big challenge is to "tap" them—to get the data on them. The data you get has to be in quantitative form—in numbers to feed into the computer.

NOTE: You won't be having to use formulas to work the tests—computers do that for you. A common software for this type of research is SPSS—available in schools and businesses. There are several measurement scales that are used in research. The most common are

nominal: the variable has no quantitative value as such; the variable is divided into groups, such as gender MALe / FeMALe (divides sample into categories)

ordinal: no quantitative value; ranks preferences

Rank the following cereals in order of your personal preference ______ cheerios ______ bran flakes ______ corn flakes

(differences; median)

interval: no quantitative value, number scale to show importance:

How much do you agree with the company's health insurance plan?

Strongly disagree Disagree Neutral Agree Strongly Agree 1 2 3 4 5

(can yield a mean on the scale)

ratio: meaningful measurement scale-absolute zero point

(research grade: ____)

(can yield a mean)

It is important to use the measurement scale that will give you the information you seek. Thus every variable has to be "operationally" defined.

OPERATIONAL DEFINITIONS

Every variable has to be operationally defined: for you so that you know what type of test to use ... and for the reader of your research. The...