Business Research Reporting

Dorinda Clippinger

• Publisher: Business Expert Press
• Year: 2017
• ISBN:
  9781631574207
• Topic: Business
• Subtopic: Business Communication
• Index: Business

Business Research Reporting addresses the essential activities of locating, collecting, evaluating, analyzing, interpreting, and reporting business data. It highlights the value of primary and secondary research to making business decisions and solving business problems. It aims to help business managers, MBA candidates, and upper-level college students boost their research skills and report research with confidence. This book discusses primary data collection, sampling concepts, and the use of measurement and scales in preparing instruments. Also, this book explores statistical and non-statistical analysis of qualitative and quantitative data and data interpretation (findings, conclusions, and recommendations). The author shows how to locate, evaluate, and extract secondary data found on the web and in brick-and-mortar libraries, including optimized searching, evaluating, and recording. Plus, the book demonstrates how to avoid copyright infringement and plagiarism, use online citation software, and cite sources when writing and presenting. Two glossaries—one each for primary and secondary research—round out the content. Business Research Reporting can be your go-to guidebook for years to come. Reading through it in a couple of hours, you can pick up ample information to apply instantly. Then keep it handy and refer to it in your ongoing research activities.

CHAPTER 1

Gathering Primary Research Data

Alan R. Belcher, PhD

Ashford University

Welcome to the world of business research. You may be reading this book to find out how to do research methodically; you may be reading to find out how to solve problems; you may be reading just to find out what “research” is all about. This book will provide the basics of the research process and will give you a strong starting point on building your own research projects. This chapter covers a wide range of things to consider as you get started. Some readers will already know much of this, while others are just getting started. In either case, you will find information about the terms and processes involved in research.

Business people are continuously solving problems. You have likely identified one or more business problems or opportunities that you think require some research to reach the right answer. You may ask yourself, what is involved in research? Generally, there are three steps to any research project.

1. You have to ask the right question.
2. You gather data.
3. You analyze the data and make sense of it.

Once you have defined the problem or opportunity, Step 1 may be done, although there is possibly more to do with defining the problem. Undoubtedly, you need to define an actual Problem, not just symptoms of a problem. Defining a research problem will become clearer as we go through this chapter.

We will talk about data collection. Collecting data can be easy or complicated, depending on the approach you take. Making it easy requires us to know about different types of data, ways to acquire data, and how to ensure that the data accurately answer the questions we have asked.

When we talk about different types of data, one major distinction separates primary data and secondary data. Primary data is data that you generate yourself. Secondary data is data that someone has collected previously and you are reviewing it for your own purpose.

A second way of distinguishing data is by the terms of quantitative and qualitative. Quantitative data is typically represented by a set of numbers while qualitative data is represented in words, ideas, and thoughts. We will consider these different types of data as we work through examples of data collection.

Case 1.1

A Business Research Scenario

Assume you own a small, independent grocery store. You sell typical groceries: dairy, meat, produce, deli, canned goods, baking items, cleaning products, paper goods, pet food, sodas, beer, wine, and other products. Most of your customers are regulars in the store, and you pride yourself on knowing them individually, many by name. Your customers are of all types; your community is fairly diverse in age, income, and ethnicity. One of the things you believe you need to do is to be more proactive in stocking shelves with products your customers want. Of course, one of your biggest concerns is inventory.

From time to time you think to yourself: It would be nice to know what goods and services my customers think they are going to want or need in the next few months or year. That is your basic research question. The work now is for you to go about finding out from your customers what their anticipated needs are. If you only had a handful of customers, you could call them and ask the questions directly; but you have too many customers for that. In fact, you have several hundred customers who rely on you on a regular basis for groceries, so how are you Going to find out what those people are thinking? You also have several occasional shoppers, and you know you want to expand your customer base as well.

Basically, you are hoping to do a few things to improve your operation of the grocery store. You want to:

• Anticipate seasonal buying trends.
• Determine what opportunities you are missing.
• Compare your store to other markets.
• Find out if your customers are satisfied with service, cleanliness of the store, and so on.

Case 1.1 will be our example as we continue this discussion of gathering primary data. Feel free to substitute your own business venture as appropriate.

You are probably already thinking about ways you can ask and find answers to these questions, and that constitutes research. We will discuss the research tools that will help to ensure that your effort in research provides quality results.

Evaluating Primary Data Sources

As noted, when we collect data through our own efforts, not just reviewing data that were previously collected, we call that primary data. There are a number of factors to consider when collecting primary data:

• Qualities of data: validity, reliability, and practicality
• Sampling issues: accuracy and precision
• Strategies for sampling: probability and non-probability
• Instruments for gathering data easily and consistently

When we collect data, there are some aspects of that data that we must consider. Above all, we want to be sure that the effort, time, and expense that we put into data collection are worthwhile. The qualities of data that we will consider are validity, reliability, and practicality.

Validity

Validity is a measurement concept that refers to the extent to which differences revealed with a measuring tool represent true differences among the people or objects being measured. In a broader sense, validity can apply to primary data sources and the data obtained from those sources, as well as to the instruments used to collect data.

A data source is valid if it is able to provide objective, accurate information about the research topic. Some sources have greater degrees of validity than others. For example, assume your research requires information about the number of single-parent households in your county. The 2010 U.S. census (secondary source) has greater validity than would a heads-of-household sample in the county conducted by your staff (primary source) because the data come from a more extensive survey than you could conduct yourself. In contrast, assume your research requires data about average cost-of-living pay increases granted in your county last year. In that case, a survey of local businesses could be more valid than the census data because it is more current and specific.

For collected data to be considered valid, those data must actually answer the research question. If we think for a moment about a politician and how that politician might respond to a question, we know that, many times, the answer is really a non-answer. From a research statistical point of view, that answer would not be considered valid. In order to collect valid data in your research project, you will want to ensure that you are asking good and logical questions and that the possible answers you will receive will help you answer the research questions.

Let’s assume that one of the questions you want to ask of your customers is their household income. If you construct the question with an open-ended response possibility, customers could answer with an exact amount or with a vague descriptor of their income. To help ensure valid responses, you will probably want to create a question that forces specific answers as shown in the following example.

What is your annual household income? (Check one)

• Less than $20,000
• $20,001 to $40,000
• $40,001 to $60,000
• More than $60,000

How many are in your household? (Check one)

• More than 5
• 4 to 5
• 2 to 3
• 1
• Prefer not to answer

In this way, your customers must respond to the question by selecting one of these categories. Not only will this type of question reduce the possibilities of a wide range of answers, it will also help to ensure that you are getting the kinds of answers you need to answer your questions: valid answers. However, it is also helpful to provide opportunity for customers to opt out of responding to a question if they simply prefer not to answer any question that may be sensitive in nature to them.

Reliability

Reliability is a measure of repeatability. Data are considered reliable when a repeat of the questions to the same audience will produce the same or nearly the same responses. Reliability is important because you want to be sure the answers you receive are not merely random or chance. It means that if you surveyed a different random subset of the same audience, you would get similar results. Now, in the case of the sample question about household income, we know that household income may change over time and that’s to be expected. However, we want to be sure that the answers are consistent over time in terms of how accurate the responses are based on the questions we asked.

Reliability is also a measurement concept that refers to the consistency of results obtained with a measuring device. A reliable data collection instrument is relatively free of random or unstable error; such an instrument helps the researcher get as close to the truth as possible. For example, a steel measuring rod is more reliable than a cloth measuring tape to measure distance or height. The steel rod itself changes little with variations in temperature, moisture, or pressure; but a cloth measuring tape may stretch or shrink in response to those conditions.

Two aspects of consistency, stability and equivalence, contribute to reliability. An instrument has stability if it gives consistent measurements of the same person or thing at different times; for example, your car odometer records nearly the same mileage each time you measure the distance of the route from your home to your workplace. The instrument has equivalence if different people using the instrument at approximately the same time and for the same purpose get consistent results; both you and your spouse or a friend get the same results when measuring distance by the odometer.

The concept of reliability extends to primary data sources, and data obtained from them, as well as to data collection instruments. In your grocery store, you may want to identify services that will attract more customers. A sample of individuals in a specific income range within the geographic area you plan to serve would be a more reliable data source than would your current customers. Your current customers’ perceptions are not necessarily representative of people who do not shop at your store.

Ideally, data sources and data collection techniques are both reliable and valid. Valid instruments and sources are also reliable, but reliable sourc...

Table of contents

• Cover
• Half Title Page
• Title Page
• Copyright Page
• Contents
• Preface
• Acknowledgments
• Chapter 1 Gathering Primary Research Data
• Chapter 2 Analyzing Business Research Data
• Chapter 3 Collecting Secondary Research Data
• Chapter 4 Documenting Secondary Data Sources
• Glossary
• Notes
• References
• Index