Key Terms in Empirical Social Research

Empirical social research aims to generate well-founded knowledge about social phenomena through systematic observation and analysis. Key methods such as induction, deduction, and abduction play a central role in deriving insights from observations or testing theories. The choice of the object of study and methodological appropriateness to that object—that is, selecting a suitable methodological approach—are crucial for obtaining valid results.

The population and the sample are also essential to the research process and must be selected carefully to produce representative results. A well-known example is the Gallup case from the 1930s, which vividly demonstrates the importance of sampling.

Another central aspect is the role of variables, which form the basic building blocks of every study. Independent and dependent variables define which factors are being examined and how they affect one another. Correctly formulating and testing hypotheses is also essential for obtaining robust research findings.

Overall, empirical social research provides a solid foundation for understanding social relationships through its methodological diversity and precise data analysis.

General Information on Empirical Social Research

At the heart of empirical social research lies the search for truth and understanding of our social environments. This field of research, firmly rooted in empirical evidence, draws on experience and observable reality to systematically and reflectively capture and interpret social phenomena. Gerhard Kleining, a prominent thinker in this field, emphasizes how essential this methodological approach is for generating well-founded knowledge (see Kleining 2001, p. 209).

Generating Knowledge through Empirical Research

Empirical social research uses various methods to generate knowledge. Three central approaches are induction, deduction, and abduction. Induction starts with observation and leads to general statements. It enables researchers to identify patterns in specific data and draw general conclusions. Deduction, by contrast, starts with a theory and works toward specific individual statements. This method tests existing theories through observations and experiments. Abduction is a particularly fascinating approach: it involves inferring not only a general principle from an observation, but also its underlying cause. This method opens up new perspectives and promotes a deeper understanding of social relationships.

The Object of Investigation: The Heart of Research

A crucial aspect of empirical social research is the object of study. This refers to the specific phenomenon or area being researched. Clearly defining the object of study is essential, as it determines the scope and focus of the study.

The importance of methodological fit to the object of study

Another important principle is methodological fit to the object of study. This concept emphasizes the need for research methods and approaches to be appropriate for the object of study. Researchers should approach the field of research in a way that is both appropriate and focused on understanding it. This ensures that the research findings are valid and accurately reflect reality.

In summary, empirical social research is a complex and nuanced field based on careful observation of reality. Through the use of induction, deduction, and abduction, it enables a deeper understanding of social phenomena. Choosing the object of study and applying methods that are appropriate to it are therefore crucial to the success of a research project.

Data collection

In the world of research and data collection, it is essential to develop a deep understanding of who or what is being studied. This brings us to the concept of the population, also known as the target population. The population encompasses all potential objects of investigation about which conclusions are to be drawn. It defines the scope of our study in substantive, spatial, and temporal terms. An important aspect here is the parameter, which describes the population using specific measures, such as the mean.

Another crucial step in data collection is drawing a sample. A sample represents a selected subset of the population and is at the heart of many research projects. The aim is to draw conclusions about the entire population based on the results of the sample. For a sample to fulfill this purpose, it must be representative. This is ensured through the process of sampling, in which an explicit procedure determines how elements are selected from the population.

In quantitative studies, sampling aims to achieve representativeness based on criteria such as demographic characteristics. In qualitative studies, by contrast, the focus is on substantive representativeness.

A historical example that underscores the importance of proper sampling is the case of Gallup versus Literary Digest in the United States in the 1930s. While Literary Digest, based on a huge but poorly selected sample, predicted that Landon would win the presidential election, George Gallup chose a smaller but representative sample that correctly predicted Franklin D. Roosevelt as the winner. This case impressively demonstrates how crucial the selection of the sample is.

Finally, we distinguish between primary and secondary statistics. Primary statistics are based on surveys conducted specifically for this purpose, making it possible to address specific research questions with regard to the purpose of the survey, timeliness, and data collection. Secondary statistics, on the other hand, refer to the reanalysis of existing research data.

Understanding these fundamentals can significantly improve the accuracy and reliability of research findings, ultimately leading to more informed decisions and insights.

Terms in the dataset

In the world of research, variables play a central role because they form the building blocks of every study. Put simply, a variable is a name assigned to a particular characteristic or attribute that people, groups, organizations, or other units of analysis may possess. Examples of such variables are diverse and range from a person’s gender and educational level, through the social integration of groups, to the duration of marriages, the form of government of states, or the number of pages in a book. In data tables, variables typically appear in the columns, which makes them easier to analyze.

Independent and dependent variables

The distinction between independent and dependent variables is particularly important in research. An independent variable (IV) is considered the causal factor within a hypothesis. It is the variable that is manipulated or changed to see how these changes affect other variables. A classic example is room temperature, whose influence on a test participant’s well-being is examined. In this case, room temperature is the independent variable, and well-being is the dependent variable.

In contrast, the dependent variable (DV) represents the factor affected in a hypothesis. It is the result or outcome that is measured to determine the influence of the independent variable. Another example is using parenting style as the independent variable and career choice as the dependent variable to investigate how different parenting styles can influence individuals’ career decisions.

Cases and units of observation

In research, cases often represent the units of analysis, such as survey respondents, and are typically listed in the rows of data tables. Units of observation can be individuals with individual characteristics—such as gender, age, education, marital status, social status, and income—as well as groups with shared characteristics. An electoral district with a particular share of the vote for Party Y or a large city with a specific crime rate are examples of such units of observation.

Values and categories of variables

Every variable has at least two values, which must lie along a common dimension. These values should be disjoint, meaning that they must not overlap, and at the same time exhaustive, so that each unit of observation can be assigned unambiguously to a category. These properties ensure that the data analysis is precise and meaningful.

The careful definition and distinction of these terms is crucial for understanding and conducting scientific research. It enables researchers to formulate clear and comprehensible hypotheses, organize their data effectively, and ultimately draw well-founded conclusions from their investigations.

Terms in Data Analysis

What Is a Hypothesis?

A hypothesis is a claim about a presumed relationship between two or more variables that must be testable. It goes beyond simple descriptions, such as stating that Petra is 1.70 m tall, or classifications, such as dividing Vienna into 23 districts. Hypotheses are also not analogies (“Love is like diving into the ocean”), statements of orientation (“Being determines consciousness”), or normative statements (“If employees bear a great deal of responsibility, they should also be well paid”).

Distinguishing Between Hypotheses

There are different types of hypotheses, depending on the type of variables and the presumed relationship between them. The difference hypothesis is used when the independent variable has only two possible values. In this case, the relationship can be formulated as an if-then relationship. One example would be: “If it rains, then the road gets wet.”

A relationship hypothesis is formulated when the values of both the independent and dependent variables can be interpreted as an order of rank. In this case, the relationship is expressed as the more-the-more relationship, for example: “The more you study, the better your exam results are.”

Tautologies: Statements That Are Always True

An interesting concept in logic is tautologies. A tautology is a statement or logical formula that is true under every possible circumstance. A classic example is the sentence: “If the rooster crows on the manure, then the weather changes or stays as it is.” At first glance, this seems to be a prediction about the weather. However, upon closer examination, it becomes clear that the sentence is always true because it covers all possible outcomes—either the weather changes or it does not.

Another example of a tautology is: “Today is Wednesday or it is not Wednesday.” This statement is obviously always true, since there is no other possibility.

What is meant by a population and a sample? Give an example that illustrates these terms.

The population comprises all elements about which a statement is to be made, e.g. all students at a university.
A sample is a subset of this population that is studied in order to draw conclusions about the population.
Example: If you wanted to determine the average stress level of all students at a university, you could draw a sample of 100 students and analyze their stress levels.

What is the difference between a dependent and an independent variable? Give an example.

An independent variable (IV) is the variable that is manipulated in an experiment or whose influence is being examined.
The dependent variable (DV) is the variable measured as the outcome or effect of the IV.
Example: In a study on the effectiveness of learning strategies, the learning method (e.g., repetition or mind mapping) could be the independent variable, while the test score could be the dependent variable.