Diving into the world of research, one is quickly introduced to a vast sea of numbers and information. Central to this intricate web of data is the principle of empirical research, which is founded on observation. This means that in science, we draw conclusions based on what we observe, rather than relying solely on traditional knowledge, authority, or “common sense”. However, collecting vast amounts of raw data poses its own set of challenges. These extensive datasets, full of numbers, might seem overwhelming and indigestible.
Enter the realm of statistics, a discipline that offers tools to simplify, summarize, and interpret this information. This section explores the core aspects of empirical research, distinguishing between descriptive, relational, and experimental research types, and delving into the fundamental principles of descriptive and inferential statistics. By the end of this section, readers will gain an understanding of how empirical research is designed, how statistical methods aid in the objective interpretation of data, and the importance of statistics in informing decisions in our increasingly data-driven world. As we navigate these topics, we’ll remember that while science offers objective tools and methods, the interpretation of data remains an art, influenced by the values, ethics, and perspectives of those making the decisions.
One of the most important things to remember about science is that the scientific method is empirical. Empirical means based on observation. In addition to observing, scientists make systematic observations. By systematic, I mean that the scientist observes with a plan, and the plan is designed to ensure objectivity. Objectivity means that the scientist takes steps to record facts that are not colored by emotion and personal prejudice. Recording observations as numerical measurements greatly aid the researcher in maintaining objectivity.
Empirical means based on observation rather than other methods of knowing, such as reliance on authority or “common sense.”
This method presents a problem. All of these observations result in huge amounts of numbers. We can organize those numbers into a table form, such as a spreadsheet, but there will still be pages and pages of them. Information (data) presented in this way is meaningless! The human mind simply cannot wrap itself around large amounts of numbers like that and draw anything meaningful from them. We need to organize and simplify the data. Organizing, simplifying, and summarizing data is a primary function of descriptive statistics.
Descriptive statistics are a family of statistical methods that organize, simplify, and summarize data.
Types of Empirical Research
Within the scope of empirical research, there are several strategies that the social scientist can use to answer questions. An important distinction is between whether the research is descriptive, relational, or experimental.
Descriptive Research
Descriptive research stands as a fundamental approach in the expansive field of scientific inquiry, aiming primarily to elucidate the characteristics of specific social phenomena. At its core, this method revolves around capturing the existing conditions or the “state of things” in a given context. Researchers engaged in this type of study aren’t necessarily probing for underlying causes or intricate relationships among variables. Instead, they focus on portraying an accurate and detailed snapshot of the situation or phenomenon in question.
Public opinion polls exemplify the essence of descriptive research. When organizations or researchers conduct these polls, their primary intent is to gauge how individuals feel about distinct issues, ranging from political stances to societal trends. Each response, each opinion, is treated as a unique entity, providing valuable insight into the collective mindset of a community or population. However, it’s crucial to note the boundaries of descriptive research. While it meticulously records these opinions, it doesn’t dive into understanding their interconnectedness or exploring potential root causes. In essence, it offers a comprehensive picture without delving into the underlying narrative or causative factors.
Relational Research
Relational research acts like a detective in the world of information, aiming to uncover connections or patterns between two or more things. Suppose you’ve ever wondered if one thing might influence another, like if people’s favorite colors might be related to their food preferences or if the music someone listens to might hint at their favorite activity. That’s where relational research comes into play. It dives into these questions and looks for patterns, helping us understand if and how certain things are connected.
One of the best ways to imagine this is like putting together a puzzle. Let’s take the example of religious beliefs and voting choices. If a researcher finds that a lot of people who have a specific religious belief often vote for a certain political party, it’s like they’ve found two puzzle pieces that fit together perfectly. They’ve uncovered a relationship or connection between these two aspects. Through relational research, we’re continually finding these “puzzle pieces” in our world, which help us see the bigger picture and understand how different pieces of information relate to one another.
Experimental Research
Experimental research seeks to make causal statements about how the social world works. That is, the researcher wants to make cause-and-effect statements. Did a university’s alcohol awareness program (cause) reduce incidents of alcohol-related crime on campus (effect)? Note that the confidence with which a researcher can make such statements relies heavily on good experimental design, which is beyond the scope of this text. We will focus strictly on the statistical concerns of good research design.
Note that these types of research are not mutually exclusive. In the practical world of research, they tend to work in concert, building on each other. A researcher conducting an experiment to demonstrate that one variable causes another will no doubt want to describe both variables and explain how they are related as well. In other words, these divisions are somewhat arbitrary, but they are useful because each has a set of statistical techniques that goes along with it. Descriptive statistics are heavily used in descriptive research, and inferential statistics are used heavily in experimental research. Relational research uses a family of correlational statistics that can be both descriptive and inferential depending on how they are treated.
Inferential Statistics
Descriptive statics, then, seek to summarize and explain the characteristics of a variable. The other major branch of statistics, inferential statistics, lets the researcher make generalizations about a population given information from a sample. Generalizations are general statements obtained by inference from specific cases.
Generalizations are general concepts that come from inferences from specific cases.
An inference is a conclusion that is based on facts and reasoning. We can gather from these definitions that the purpose of inferential statistics is to let us make general statements about populations based on information that we have gathered from samples.
Inferential statistics is the branch of statistics that uses sample data to reach conclusions (make inferences about) populations.
Populations and Samples
To understand the jargon of inferential statistics, it is helpful to understand how social scientists answer most questions. It is important to understand that scientists are generally interested in populations. A population is the entire group of people that a researcher is interested in making statements about. A population can be very small, such as female Supreme Court justices, or it can be very large, such as every registered voter in the United States.
A population is the entire group of interest in a research study.
A number that describes the characteristics of a population is called a parameter.
When a researcher collects information about an entire population, it is called a census. Numbers that describe the characteristics of populations are known as parameters. For example, when the Bureau of the Census reports the median household income of Americans, that is a parameter because it is a number that came from data collected on the entire population.
A census is information collected from the entire population of interest.
When populations are large, it becomes unfeasible to collect data for every person. There simply are not enough resources (human resources, time, money, etc.) to conduct a census. When this is the case, researchers use samples. A sample is a subset of a population that is used to answer questions about the population from which it was drawn. Numbers that describe the characteristics of samples are called statistics.
A sample is a subset of a population that is used to answer questions about the population.
A number that describes the characteristics of a sample is called a statistic.
Social scientists using inferential statistics start with a hypothesis and look to see if data gathered from systematic observations are consistent with the hypothesis. Many of these techniques are complex and are best left to computers. For now, we will define a hypothesis as an educated guess as to how some social phenomenon occurs. (We will delve more deeply into hypotheses when we get to the sections on inferential statistics).
A hypothesis is an educated guess as to how some social phenomenon occurs.
Practical Relevance
Even if you do not plan to conduct research, you must be an intelligent consumer of research to assume a leadership role in today’s data-rich world. The social science professions, even the applied ones such as social work and criminal justice, depend on social scientific research to determine best practices, prepare grant applications, conduct program evaluations, and many other management tasks. In every arena of public management, there is a growing demand for accountability. Accountability in a narrow but important sense means that tax dollars are spent on programs that work and work well. The public demands objective evaluations of programs and policies. Such evaluations are performed using the same techniques that social scientists use to answer other questions.
Never forget, then, that the ultimate goal of statistics is to explain real-world events. This is a two-part process that calls for the objective application of the statistical methods that we’ll learn in this little book, and it requires subjective judgments as to what is good, what is right, and what is meaningful. Ultimately, science, in general, and statistical methods, in general, are only useful in informing the subjective judgments of decision-makers. Science is not a tool for making ethical decisions, but it is a tool for informing ethical decision-makers. Statistics is a scientific tool, but the interpretation and application of that objective information are often more of an art than a science. That is why a major focus of this book is understanding why we compute a particular statistic and what exactly the results mean.
Key Terms
Statistics, Descriptive Statistic, Average, Empirical, Objectivity, Data, Systematic Observation, Inferential Statistic, Sample, Population, Percentage, Margin of Error, Census, Parameter, Generalization
This work is licensed under an Open Educational Resource-Quality Master Source (OER-QMS) License.
Last Modified: 09/11/2023