Now that we have figured out how to define, or conceptualize, our terms we’ll need to think about operationalizing them. Operationalization is the process by which researchers conducting quantitative research spell out precisely how a concept will be measured. It involves identifying the specific research procedures we will use to gather data about our concepts. Of course, this process requires that we know what research method(s) we will employ to learn about our concepts, and we’ll examine specific research methods later. For now, let’s take a broad look at how operationalization works. We can then revisit how this process works when we examine specific methods of data collection in later chapters. Remember, operationalization is only a process in quantitative research. Measurement in qualitative research will be discussed at the end of this section.

Indicators

Operationalization works by identifying specific indicators that will be taken to represent the ideas we are interested in studying. If we are interested in studying masculinity, then the indicators for that concept might include some of the social roles prescribed to men in society such as breadwinning or fatherhood. Being a breadwinner or a father might therefore be considered indicators of a person’s masculinity. The extent to which a man fulfills either, or both, of these roles might be understood as clues (or indicators) about the extent to which he is viewed as masculine.

Let’s look at another example of indicators. Each day, Gallup researchers poll 1,000 randomly selected Americans to ask them about their well-being. To measure well-being, Gallup asks these people to respond to questions covering six broad areas: physical health, emotional health, work environment, life evaluation, healthy behaviors, and access to basic necessities. Gallup uses these six factors as indicators of the concept that they are really interested in, which is well-being (http://www.well-beingindex.com/).

Identifying indicators can be even simpler than the examples described thus far. What are the possible indicators of the concept of gender? Most of us would probably agree that “man” and “woman” are both reasonable indicators of gender, but you may want to include other options for people who identify as non-binary or other genders. Political party is another relatively easy concept for which to identify indicators. In the United States, indicators likely include Democrat and Republican but depending on your research interest, you may include additional indicators such as Independent, Green, or Libertarian. Age and birthplace are additional examples of concepts for which identifying indicators is a relatively simple process. What concepts are of interest to you, and what are the possible indictors of those concepts?

Now that we’ve considered a few examples of concepts and their indicators, it is important to note that choosing indicators is not an arbitrary process. As described earlier, utilizing prior theoretical and empirical work in your area of interest is a great way to identify indicators in a scholarly manner. Theories will point you in the direction of relevant concepts and possible indicators. Empirical work will give you some very specific examples of how the important concepts in an area have been measured in the past and what sorts of indicators have been used. Often, it makes sense to use the same indicators as previous researchers, however you may find that some previous measures have potential weaknesses that your own methodological approach can overcome.

Speaking of methodological approaches, it is also important to think about your data collection strategy when considering indicators and concept measurements. A survey implies one way of measuring concepts, while focus groups imply a quite different way of measuring concepts. Your design choices will play an important role in shaping how you measure your concepts.

Operationalizing your variables

Taking your concepts through the steps of identification, conceptualization, and operationalization is a matter of increasing specificity. You begin the research process with a general interest, then you identify a few essential concepts, work to define those concepts, and then spell out precisely how you will measure them. In quantitative research, that final stage is called operationalization.

An operational definition consists of the following components: (1) the variable being measured, (2) the measure you will use, (3) how you plan to interpret the results of that measure.

The first component, the variable, should be the easiest part. At this point in quantitative research, you should have a research question that has at least one independent and at least one dependent variable. Remember that variables must be able to vary. For example, the United States is not a variable. Country of birth is a variable, as is patriotism. Similarly, if your sample only includes men, gender is a constant in your study…not a variable.

Let’s pick a social work research question and walk through the process of operationalizing variables. I’m going to hypothesize that residents of a psychiatric unit who are more depressed are less likely to be satisfied with care. Remember, this would be a negative relationship—as depression increases, satisfaction decreases. In this question, depression is my independent variable (the cause) and satisfaction with care is my dependent variable (the effect). Now we have identified our variables, we move onto the second component, which is deals with measurements.

How do you measure depression or satisfaction? Many students would say that depression could be measured by observing a participant’s body language. They may also say that a depressed person will often express feelings of sadness or hopelessness. In addition, a satisfied person might be happy around service providers and often express gratitude. While these factors may indicate that the variables are present, they lack coherence. Unfortunately, what this “measure” is actually saying is that “I know depression and satisfaction when I see them.” While you are likely a decent judge of depression and satisfaction, you need to provide more information in a research study for how you plan to measure your variables. Your judgment is subjective, based on your own idiosyncratic experiences with depression and satisfaction. They couldn’t be replicated by another researcher. They also can’t be done consistently for a large group of people. Operationalization requires that you come up with a specific and rigorous measure for seeing who is depressed or satisfied.

Finding a good measure for your variable can take less than a minute. To measure a variable like age, you would probably put a question on a survey that asked, “How old are you?” To evaluate someone’s length of stay in a hospital, you might ask for access to their medical records and count the days from when they were admitted to when they were discharged. Measuring a variable like income might require some more thought, though. Are you interested in this person’s individual income or the income of their family unit? This might matter if your participant does not work or is dependent on other family members for income. Do you count income from social welfare programs? Are you interested in their income per month or per year? Measures must be specific and clear.

Depending on your research design, your measure may be something you put on a survey or pre/post-test that you give to your participants. For a variable like age or income, one well-worded question may suffice. Unfortunately, most variables in the social world are not so simple. Depression and satisfaction are multi-dimensional variables, as they each contain multiple elements. Asking someone “Are you depressed?” does not encompass the complexity of depression, including issues with mood, sleeping, eating, relationships, and happiness. Asking someone “Are you satisfied with the services you received?” similarly omits multiple dimensions of satisfaction, such as timeliness, respect, meeting needs, and likelihood of recommending to a friend, among many others.

To account for a variable’s dimensions, a researcher might rely on indexes, scales, or typologies. An index is a type of measure that contains several indicators and is used to summarize a general concept. An index of depression might ask if the person has experienced any of the following indicators in the past month: pervasive feelings of hopelessness, thoughts of suicide, over- or under-eating, and a lack of enjoyment in normal activities. On their own, some of these indicators like over- or under-eating might not be considered depression, but collectively, the answers to each of these indicators add up to an overall experience of depression. In this case, the index allows the researcher to better understand the respondents’ unique experiences of depression. If the researcher had only asked whether a respondent had ever experienced depression, they wouldn’t know the depth and variety of behaviors and symptoms that were included in that respondent’s experience of depression.

The researcher could take things one step further by ranking the various behaviors that make up the concept of depression to create a scale. For example, they might weigh suicidal thoughts more heavily than eating disturbances. Like an index, a scale is a measure composed of multiple items or questions. Unlike indexes, scales are designed to account for each item’s various levels of intensity.

If creating your own scale sounds painful, don’t worry! For most multidimensional variables, you would likely be duplicating work that has already been done by other researchers. You do not need to create a scale for depression because scales such as the Patient Health Questionnaire (PHQ-9), the Center for Epidemiologic Studies Depression Scale (CES-D), and Beck’s Depression Inventory (BDI) have been developed and refined over dozens of years to measure variables like depression. Similarly, scales such as the Patient Satisfaction Questionnaire (PSQ-18) have been developed to measure satisfaction with medical care. As we will discuss in the next section, these scales have been shown to be reliable and valid. While you could create a new scale to measure depression or satisfaction, a study with rigor would pilot test and refine that scale over time to make sure it measures the concept accurately and consistently. This high level of rigor is often unachievable in undergraduate research projects, so using existing scales is recommended.

Furthermore, you can save time and effort by using existing scales. The Mental Measurements Yearbook provides a searchable database of measures for different variables. You can access this database from your library’s list of databases. If you can’t find anything in there, your next stop should be the methods section of the articles in your literature review. The methods section of each article will detail how the researchers measured their variables. In a quantitative study, researchers likely used a scale to measure key variables and will provide a brief description of that scale. A Google Scholar search such as “depression scale” or “satisfaction scale” should also provide some relevant results. As a last resort, a general web search may bring you to a scale for your variable.

Unfortunately, these approaches do not guarantee that you will be able to view the scale itself or get information on how it is interpreted. Many scales cost money to use and may require training to properly administer. You may also find scales that are related to your variable but would need to be slightly modified to match your study’s needs. You could adapt a scale to fit your study, however changing even small parts of a scale can influence its accuracy and consistency. Pilot testing is always recommended for adapted scales.

A final way to measure multidimensional variables is by using a typology, which categorizes concepts by theme. The most familiar version of a typology is likely the micro, meso, macro framework, whereby students classify specific elements of the social world by their ecological relationship with the person. If we consider the example of depression, a lack of sleep would be classified as a micro-level element while going through a severe economic recession would be classified as a macro-level element. Typologies require researchers to clearly define the rules for how data are assigned to specific categories, so it would be important to cite a source on ecological systems theory that provides the rules on what elements fall under each level of the ecosystem.

The final stage of operationalization involves setting the rules for how the scale works and how the researcher should interpret the results.  Earlier, we discussed how ecological systems theory will help give you the rules for interpreting your micro/meso/macro topology. Sometimes, interpreting a measure can be incredibly easy. If you ask someone their age, you’ll probably interpret the results by noting the raw number (e.g., 22) someone provides. However, you could also recode that person into age categories (e.g., under 25, 20-29-years-old, etc.).  Indexes may also be simple to interpret. If there is a checklist of problem behaviors, one might simply add up the number of behaviors checked off–with a higher total indicating worse behavior. On the other hand, indexes could assign people to categories (e.g., normal, borderline, moderate, significant, severe) based on their total number of checkmarks. As long as the rules are clearly spelled out, you are welcome to interpret in a way that makes sense to you. The categories that you choose for your study may be guided by theory, or perhaps the types of statistical tests you plan to run during data analysis.

For more complicated measures like scales, you refer to the information provided by the author for how to interpret the scale. If you can’t find enough information from the scale’s creator, look at how the results of that scale are reported in the results section of research articles. For example, Beck’s Depression Inventory (BDI-II) uses 21 statements to measure depression and respondents rate their level of agreement on a scale of 0-3. The results for each question are added up, and the respondent is put into one of three categories: low levels of depression (1-16), moderate levels of depression (17-30), or severe levels of depression (31 and over).

In sum, operationalization specifies what measure you will be using to measure your variable and how you plan to interpret that measure. Operationalization is probably the trickiest component of basic research methods, so please don’t get frustrated if it takes a few drafts and a lot of feedback to get to a workable definition. Currently, I am in the process of operationalizing the concept of “attitudes towards research methods.” Originally, I thought that I could gauge students’ attitudes toward research methods by looking at their end-of-semester course evaluations. As I became aware of the potential methodological issues with student course evaluations, I opted to use focus groups of students to measure their common beliefs about research. You may recall some of these opinions from Chapter 1, such as the common beliefs that research is boring, useless, and too difficult. After the focus group, I created a scale based on the opinions I gathered, and I plan to pilot test it with another group of students. After the pilot test, I expect that I will have to revise the scale again before I can implement the measure in a real social work research project. At the time I’m writing this, I’m still not completely done operationalizing this concept.

Qualitative research and operationalization

As we discussed in the previous section, defining the concepts used in qualitative research takes a more open-ended approach. The questions you choose to ask in your interview, focus group, or content analysis will determine what data you end up getting from your participants. For example, if you are conducting a qualitative study on depression, you would not use a quantitative scale measure like the Beck’s Depression Inventory. Instead, you should start off with a tentative definition of what depression means based on your literature review and use that definition to come up with questions for your participants. Later in the text, we will discuss how those questions fit into qualitative research designs. For now, remember that qualitative researchers measure their variables by asking questions to their participants and these questions can change as participants provide more information. Ultimately, the concepts in a qualitative study will be defined by the researcher’s interpretation of what their participants say. Unlike in quantitative research in which definitions must be explicitly spelled out in advance, qualitative research allows the definitions of concepts to emerge during data analysis.