what is quantitative research in political science

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8 Chapter 10: Methods

Understanding political methodology requires us to return to a few key concepts from previous chapters. The basics of social science inquiry is to explain causation—what causes what—in political, social, or economic phenomena.

There are two variables in this causal relationship: the dependent and independent variables. The dependent variable is the outcome we seek to isolate and study in order to determine what caused it. Independent variables are the potential causes of the dependent variable in question. Once we determine the dependent variable as the focus of the study and a number of independent variables that could potentially be the cause, we need tools, or methods, to observe and draw conclusions. Key to this inquiry is determining the right methods that best explain the phenomenon in question. Once we establish the methods then we can begin the process of data collection, observation, analysis, and inference.

A fundamental divide in methods for political science is the distinction between quantitative and qualitative methods. Quantitative methods principally combines statistics, mathematics, and formal theory as tools for positive research in political science. It is a data-driven approach in which collection, analysis, interpretation, and presentation of numerical data provides inferences and insights into key political questions. Positive research, as previously mentioned, seeks to describe and explain what is, and is in contrast with normative research that seeks prescriptions of what ought to be. Qualitative methods entail a set of tools for explaining political phenomena that are not numerical or statistical and does not seek to count or measure data. Instead, a qualitative approach uses description and observation of non-numerical data to draw inferences. Not all data can be quantified in a way that is useful, particularly human-related data such as behavior or belief, and qualitative methods help us fill the gap. As a sub-field in political science, political methodology is principally the study of how methods are used in the discipline. It is a practical, hands on sub-discipline that gives students direct access to the tools of political inquiry.

Let’s review a few key concepts and approaches in political methodology. First, we will consider some terms and approaches in quantitative methods. Second, we will look at some core principles and dominant approaches to qualitative methods. Lastly, we will discuss the basics of developing a research project that will serve as a template for students to create their own research agenda.

Correlations

Statistical correlations are the most common tool in quantitative methods. Correlations measure the relationship between two variables. A positive correlation implies a relationship in which an increase or decrease in numerical value of one variable corresponds to a similar increase or decrease in the other variable. As an example, let’s look at the relationship between wealth and voting participation: several studies have found a positive correlation between these variables such that higher levels of wealth correspond to a higher likelihood of voting. [1] Moreover, lower levels of wealth tend to correspond to a lesser likelihood of voting. A negative correlation implies a relationship in which one variable increases when the other variable decreases, or vice versa. Let’s consider the variable of voter turnout—what are some variables that can be negatively correlated to voter turnout? We may think of a number of variables that could be tested, such as bad weather, a more stringent registration process, high levels of poverty, or low levels of education. We can test these variables and hopefully gain some insight into what drives higher voter turnout and what obstacles there are to voting.

A correlation, it has often be said, does not necessarily imply causation, but correlations are an indication that there may be causation or some meaningful relationship that can provide insight into political inquiry. Suppose we just collected and analyzed data, maybe for years, gathering mountains of information. Assume further that we organize this information and present it in an accessible and attractive way. What’s missing in this research project? Data can be collected, organized, analyzed, and presented, but at the end of the day the political scientist must ask, “okay, what does all this mean ?” What conclusions can be drawn from the analysis of data? What questions remain? The methodological work of a political scientist is not done once a statistical regression is run and a correlation between two variables is determined. An important next step is the task of inference—drawing conclusions based on the correlation and perhaps other observations and correlations as well. Drawing inferences is an essential scientific activity that directly probes the meaning of data and analysis.

Let’s return to the example of a positive correlation between wealth and voting participation. What does this really mean? We may logically infer one thing it does not mean: that voting makes you more wealthy. Voter turnout is the dependent variable in this analysis—an outcome for which we seek causal explanation. It may be that individuals who are wealthy are more likely to volunteer, participate in other civic and political endeavors, run for office, and the like. In this case, wealth may not be such a powerful explanation for voter turnout, but rather a variable that increases the likelihood of many different forms of political participation. On the other hand, wealth may be a more direct cause of voter turnout: the correlation between these two variables may be noticeably higher than between wealth and volunteerism or wealth and running for office. Additionally, we may draw an inference that voting participation increases with wealth because individuals may feel as though they have a larger stake in the political process or are at risk of paying more taxes, etc. This inference suggests a tighter link between wealth and voter participation.

Here is an example of a correlation represented with a scatter plot:

This scatter plot shows the correlation between child-dependency ratio and the UN Human Development Index. A child-dependency ratio is derived by taking the number of dependents (14 years of age and younger and 65 years of age and older) and dividing it by the total population. The N in this statistic is 176: the number of nations in the study. What this statistic suggests is that there is a negative correlation between these values—higher child-dependency ratios correspond to lower human development.

Key Terms in Mathematical Modeling

Doing the work of political science often involves statistics to gather, observe, and organize data, and so it is necessary to understand some basic elements of statistical work. Typically, one begins with a population , the universe of event numbers associated with your study. Out of this population, a researcher can derive a sample that can be observed. Random samples have the advantage of being free from any presumptions a researcher might have and are thus likely to be unbiased. The overall number in a sample is referred to as N. If you survey a random sample of 1,500 people asking them whether they approve or disapprove of a particular politician, the N in this survey is 1,500. A statistic, a numerical measure that describes some property of the population, can be pulled from this sample and analyzed. This statistic will include some form of numerical, or quantitative, data.

There are broadly two types of quantitative data: discrete data, which are typically integers which cannot be divided further or be made more precise, and continuous data, which can be divided into smaller and more precise measurements. An example of discreet data would be the number of representatives in Congress who voted for a particular bill. This will be a whole number that cannot be divided—you cannot have a half or quarter of a representative who voted, the number may be 212 or 213, but cannot be 212.5. An example of continuous data would be the average number of representatives in Congress who voted for appropriations bills over a 10-year period of time. This number could be 212 or 213, but it could also be 212.5 or 212.275.

Data can also be derived from surveys or experiments. Surveys derive data from responses by a group of participants. This group is a sample from the overall population. Survey results can be generalized to the larger population but they are less than precise in predicting causation. Experiments are controlled observations of a particular phenomena and provide experimental data that is not easily generalized but can more precisely predict causation. In political science, conducting experiments can sometimes be impossible, whereas researchers often rely on surveys. The result is that causation is harder to predict in political science, as well as the other social sciences, compared to the natural or so-called hard sciences, where experiments are much more common.

A particular statistic may give us a probability—the likelihood of an event or outcome happening. Further, we may get a probability distribution, which indicates a scale of possible outcomes based on the likelihood of occurring. Probability distributions may be discrete (only certain values, such as whole numbers) or continuous (a range of possible values), along the lines described above. The distribution of data across a scale will provide a mean, median, and mode. A mean is a measure of central tendency, the average of the numbers on the scale, which can be achieved by adding up the value of all the numbers and dividing by how many numbers there are. The median is not an average but the central value on a scale. The mode is the value that occurs most frequently in the scale. If your data scale is the following: 2, 4, 5, 9, and 9, then the mean would be (2+4+5+9+9=29/5=) 5.8, whereas the median would be the value in the middle of this scale (5), and the mode would be 9, the most frequently occurring number.

Lastly, we may present data in a number of ways that will be helpful for analysis and drawing inferences. A bar and whisker plot is a representation of groups of numerical data based on quartiles. The box in a box and whisker plot is the area of the inner two quartiles, whereas the whiskers (lines extended out from the boxes) are the highest and lowest quartiles respectively. A bar chart will show the frequency in each value by the height of a bar that represents that value and typically shows the relationship between two variables. A histogram will represent the frequency of values in intervals or “bins” which should be adjacent to one another but do not have to be equal. Histograms typically represent only one variable. A pie chart is a circular graph that shows portions of the total with wedges that represent the size of that proportion. A pareto chart contains both bars and a line graph, the bars representing descending frequency for each value and the line graph representing the cumulative total of frequencies. Finally, a scatter plot locates values (represented as points) along a plot typically determined by two variables, one along the X axis and the other along the Y axis, and can contain a third variable if the points are coded (by color or size, for example).

Qualitative methods

As previously mentioned, not all data can be numeric. Typically, human-related data that is subjective cannot be meaningfully quantified but may nonetheless be important to your research. The meaning of why or the description of how may be essential for answering your research, particularly why and how questions related to the human experience. Quantitative approaches can only count and measure, not give us the why or the how. Qualitative methods seeks to fill in the gap by providing a set of tools that allows for data collection, analysis, organization, and presentation. The typical qualitative approach is the case study—a focused, in-depth account of a single individual, group, organization, action, or event. Researchers who seek more context, depth, and detail of a single case are best suited to the qualitative method, where the absence of large amounts of numerical data make quantitative data collection and analysis impossible. Case studies in themselves are not confined to qualitative data, however, and may employ a mix of both qualitative and quantitative methods and data. A qualitative case study will provide a “thick description” of the case, focusing on the why and how of various phenomena that occur. [2]

In selecting a case, typical or average cases often do not reveal rich detail of information or are meaningful in their context and characteristics. Unique or outlier cases often prove more interested to explain. Because of this, random-sampling of cases, while useful to the quantitative method, are less useful in the qualitative approach. Cases may be selected based on the inherent and unique characteristics of the case, the context that surrounds it, or because the researcher has the prior depth of knowledge of the characteristics or culture of the case that would allow them to immerse themselves in the environment and provide descriptions or accounts that are meaningful. A qualitative case study is therefore less generalizable than quantitative research—if you are providing a thick description of a single, unique case, it makes sense that this case will not tell you very much about other cases. In contrast, quantitative research that includes large amounts of numerical data affords researchers better opportunities to generalize and make claims across cases.

Qualitative research can collect data in a variety of ways, such as interviews, storytelling, analysis of narratives, participant observations, or focus groups, among others. Interviews are a common form of qualitative data collecting in which a researcher asks questions to subjects that are important to the case. Interviews may be highly structured, in which questions are determined beforehand and there is no deviation from the list of questions, or unstructured, in which the researcher and subject engage in open-ended dialog. Narratives and storytelling can be important to understanding a particular culture or community, since stories can form a kind of discursive foundation on which common knowledge is shared and common action is determined. Participant observations can be a good way for a researcher to collect data through simply observing a group interact with one another. Such observations can be passive in the sense that the researcher attempts to remove themselves from the dynamic as much as possible so as not to influence the outcomes, or it can be active, in which a researcher is part of the group interactions and makes observations from within the context of the dynamic. Finally, focus groups allow for more controlled observations of specific interactions and allow a researcher to gather more contextualized data (such as reactions, agreement, or disagreement) than would be possible in isolated interviews.

Field research is a broad term we use to describe data collection and observation on the ground, removed from the academic setting. It is in your field research that you would conduct interviews, focus groups, or participant observations. As discussed in the comparative politics context in Chapter 7, researchers should determine which case is best to study given practical considerations on the ground, the most appropriate form of data collection (interviews, etc.), how long the field research should be conducted (short stays may be more directed, long stays may yield more data), and what sort of resources and skills would be necessary to conduct the research successfully.

Research Design

Designing a research project can be daunting, but it is also an exciting, hands-on way for students to learn more about issues they care about, understand the work of political science and its relevance, and gain insight into how political action and change might make a better world. Outlined below are the basic elements required to begin a research project, a brief description of each of those elements, and a rubric for each element that can give teachers and students a guide as to how a research project assignment may be evaluated. Keep in mind, this outline is not the research itself, only a template. No data will actually be gathered, analyzed, and assessed, and no inferences are drawn.

• Research question (RQ).
• Identify your dependent variable (DV), ie, the focus of your study.
• Potential answers (IVs) to the RQ, ie, the explanation for your DV outcome.
• Why does this question matter (SFW)? What relevance does this have? Why is it important?
• Choose method of data collection and analysis: quantitative (QN), qualitative (QL) or both (BQ)
• Determine form of method for data collection and the ideal data (D). For QN, identify specific statistics and different representations of variables (scatter plot, pie chart, bar graph, etc). You do not actually have to find this data or compile it, so its best to think of this as the ideal data possible for you to answer your question. In the best possible world, what numerical data would I need to best answer the RQ? For QR, determine a unique or outlier case that makes for interesting study. Determine the basic framework of your field research (short or long stay, resources and skills needed, etc.), and identify at least two forms of data collection (interviews, participant observations, etc).
• Identify which political science sub-field is the best fit for this research project.
• An annotated bibliography (BIB) of at least 6 scholarly sources (books or articles, including online sources) that provide some overview or analysis of your topic and can serve as sources for a literary review or extensive background information. This BIB is not a list of your ideal data, but rather scholarly or reputable sources that pertain to the issues surrounding your RQ.

Political methodology is the tool box we use to put theory (ideas about our political world) into practice. Methods allow us test theories, ideas, and assumptions we have, refining our understanding of politics and drawing out meaningful insights and inferences. The vast majority of political inquiry is an inquiry into causation on one level or another, and so research in political science requires a structure that can explain the causes of political phenomena. The first step is designing a research question—developing a starting point of inquiry that is centered on change or variation of some kind. What explains this particular change we see? Why do we get x and not y? Why are two seemingly similar cases produced different outcomes? The explanations or causes are independent variables in social science inquiry, and the dependent variable is the outcome of this change. Typical research in political science will center their study on the dependent variable and seek to explain how this outcome came about by identifying and analyzing independent variables that have potentially caused this outcome. Methods are the tools used to collect and analyze data, scrutinize the independent variables in question, and draw inferences that best explain causes of the dependent variable in question.

Broadly, there are two approaches in political methodology, quantitative and qualitative. Quantitative research typically entails large amounts of numerical data that require mathematical modeling—statistics—to analyze the variables in question. Correlations are statistical indicators that measure the mutual dependence or association between two variables and are commonly used in political science research. These correlations may indicate causation, but not necessarily—the researcher must draw inferences and analyze the strength of the association in order to make claims of causation. Qualitative research entails the collection of non-numerical data, often human-related experiences that are difficult to quantify. Such qualitative data can include interviews, participant observations, and focus groups conducted in field research. Both quantitative and qualitative research should be driven by a research question—a precise, non-banal question that directly centers on explaining some kind of political phenomena we observe in the world.

Media Attributions

• Plot-of-Human-Development-Index-2007-and-Child-Dependency-Ratios-2005-for-176-countries © Fm122 is licensed under a CC BY-SA (Attribution ShareAlike) license
• CNH_Figure_2 © Conhegarty is licensed under a Public Domain license
• 1920px-Yale_climate_US_public_opinion_2018_bar_chart © Yale Program on Climate Change Communication is licensed under a All Rights Reserved license
• Labeled_Box_and_Whisker_Plot © KStrileckis
• Histogram_of_Palestinian_rocket_attacks_on_Israel_per_day,_2014 © Kozrty is licensed under a CC BY-SA (Attribution ShareAlike) license
• RootCauseParetoChart © KellyLawless is licensed under a CC BY-SA (Attribution ShareAlike) license
• World Economic Forum, "Link Between Voting in Elections and Income." 2018: https://www.weforum.org/agenda/2018/07/low-voter-turnout-increasing-household-income-may-help (accessed on July 28, 2019). ↵
• The phrase "thick description" comes from Clifford Geertz, "Thick Description: Towards an Interpretive Theory of Culture," in The Interpretation of Cultures. Basic Books: 1973 ↵

In statistics, a population is a universe of event numbers under study.

Politics, Power, and Purpose: An Orientation to Political Science Copyright © 2019 by Jay Steinmetz is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Political Science Research

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Qualitative vs Quantitative Research

For decades, there has been a raging debate among scholars regarding the differences between and advantages of qualitative and quantitative methods. In fact, this has probably been one of the largest and longest methodological debates in all of social science research. Perhaps it can be briefly summarized by the following two famous and opposing quotations: Donald Campbell says, “All research ultimately has a qualitative grounding”; and Fred Kerlinger says, “There’s no such thing as qualitative data. Everything is either 1 or 0” (in Miles & Huberman, 1994, p. 40). Although it is not necessarily critical to determine which—if either—of these approaches can be described as the better one, it is imperative to have a thorough understanding of these methods in order to be able to conduct sound political science research. After all, for a study to be of value to scholars and other individuals interested in the topic, it is necessary for one to choose the correct research approach, ask suitable questions, use appropriate research methods and statistical analyses, correctly deduce or induce inferences, and have suitable general goals driving the research.

The questions under consideration and the answers obtained by any particular study will depend on whether the study uses quantitative or qualitative approaches. The purpose of this article is to differentiate between these two types of research. First, the literature available on this topic is briefly summarized, focusing specifically on how qualitative and quantitative research is defined, as well as the different assumptions on which these types of research are based. Next, a summary of the similarities and differences in each stage of the research process is provided. Then, the different methods that these two types of approaches use are discussed. Next, since this is a book examining political science in the 21st century, current and future research directions are examined. In particular, the use of what are called mixed methods approaches is discussed. The article ends with a brief summary and conclusion of the information that has been presented. Finally, suggested books and articles for further reading are provided, including some material for individuals interested in conducting advanced statistical studies, which are beyond the scope of this article.

Definition of Quantitative Research

Assumptions of quantitative research, definition of qualitative research, assumptions of qualitative research, the research question, research design, data collection, data analysis, reporting of results, limitations of quantitative methods, limitations of qualitative methods, future directions, quantitative and qualitative research.

The following section introduces the definitions and assumptions of quantitative and qualitative research. First, however, it is worth briefly discussing two types of political analysis in order to understand the origins of quantitative and qualitative methods. Political scientists distinguish between empirical analysis—obtaining and dealing with knowledge and information—and normative analysis— determining how to use that knowledge. Normative analysis relies on the development of subjective goals and values to apply what has been learned to reality. Empirical analysis, however, focuses on using common terms to explain and describe political reality and can be either quantitative or qualitative in nature. If something is empirical, it is verifiable through observations or experiments. Empirical analysis is the focus of this article.

As a first step, it is necessary to define these two methods of research and examine their goals. Quantitative research can be defined as a process of inquiry examining an identified problem that is based on testing a theory measured by numbers and analyzed with statistical techniques. Thus, quantitative research involves the analysis of numerical data. A more technical definition is provided by Brady and Collier (2004), who define mainstream quantitative methods as “an approach to methodology strongly oriented toward regression analysis, econometric refinements on regression, and the search for statistical alternatives to regression models in contexts where specific regression assumptions are not met” (p. 294). The econometric refinements and statistical alternatives referred to by the authors are beyond the scope of this article but include logit and probit models, time-series analysis, and a variety of techniques to circumvent problems that can occur in regression analysis, such as heteroskedasticity and autocorrelation. Essentially, quantitative methods have played a major role in improving on commonly used research tools within the structure of regression models that are frequently used in the field of political science.

The goal of quantitative research is to examine particular instances or aspects of phenomena to determine if predictive generalizations of a theory hold true or to test causal hypotheses. As a result, there are several key assumptions underlying quantitative research methods, which are briefly outlined here. These include the following:

• Reality can be studied objectively.
• Research must remain independent of the researcher through the use of experiments, questionnaires, machines, or inventories.
• Research is value free, and the researcher does not become a part of or interfere with the research.
• Theories and hypotheses are tested in a cause effect order with research based primarily on deductive forms of logic identified a priori by the researcher.
• The purpose of research is to develop generalizations that contribute to theory and allow the researcher to predict, explain, and understand a particular phenomenon.

Qualitative research can be defined as a process of inquiry that builds a complex and holistic picture of a particular phenomenon of interest by using a natural setting. Thus, qualitative research involves the analysis of words, pictures, videos, or objects in the context in which they occur.

The goal of qualitative research is to understand social issues from multiple perspectives to have a comprehensive understanding of a particular event, person, or group. As with quantitative research, there are several key assumptions underlying qualitative research methods:

• Reality is socially constructed, and there are multiple realities.
• The researcher interacts and often works closely with the individuals or groups under study and serves as the primary instrument for data collection and analysis.
• The research is value laden, and the researchers become a part of the research, attempting to understand the lives and experiences of the people they study.
• Research is context bound and based on inductive forms of logic that emerge as a study progresses.
• The purpose of research is to find theories that help explain a particular phenomenon.

Comparing and Contrasting Quantitative and Qualitative Research Methods

The following section examines how quantitative and qualitative methods are similar to and different from each other throughout the research process, beginning with the creation of a research question and up to the reporting of the results. Although examining quantitative and qualitative methods as two separate categories is necessary for the sake of clarification throughout this section, it is important to realize that these two methods are not mutually exclusive, a topic that will be discussed in more detail shortly. As Manheim, Rich, Willnat, and Brians (2007) note, when examining the differences between quantitative and qualitative methods, “The distinctions discussed are generally more matters of degree than absolutes. The two types of methods often require only different forms of work, but are working toward similar objectives” (p. 323). This is important to keep in mind while reading this article.

The first step in conducting sound political science research is selecting a research question. An appropriate research question should fulfill either a scientific need or a societal need by helping to provide an answer to an important problem. Both quantitative and qualitative forms of research begin by creating a research question that is intended to produce knowledge of the empirical world. In terms of the research questions, the main difference between quantitative and qualitative methods typically exists in the type of questions that are being posed.

A theory is a potential explanation for events and is composed of a set of logically related propositions and assumptions. Theorizing is the actual process of stating these conceptual explanations for events that take place in the real world by proclaiming relationships among the concepts. Theories are created to help people understand phenomena. There are several characteristics that make a theory particularly useful in explaining observations. Theories should be (a) testable, (b) logically sound, (c) communicable, (d) general, and (e) parsimonious.

Theorizing is a critical phase of the research process for quantitative and qualitative researchers. However, quantitative researchers are more likely than qualitative researchers to focus on testing performed theories. Quantitative researchers base their studies on a theory that relates to their subject in an attempt to develop generalizations that contribute to theory. Thus, in quantitative research, theorizing occurs prior to the collection of data. Qualitative researchers, on the other hand, are more likely than quantitative researchers to elaborate on theories while making observations of a particular phenomenon. Many qualitative researchers argue that, as a result of this, their theories are far more grounded in reality than are those of quantitative researchers. However, quantitative researchers argue that the formulation of theory during the observation-making process can easily lead to the creation of a theory designed around those specific observations. As a result, these theories would be polluted and not testable. Furthermore, if a theory is based on observation of one particular group, the usefulness of the theory is quite limited.

Simply defined, a research design is the plan of a study. It organizes observations in a manner that establishes a logical basis for causal inference. Essentially, the research design can be viewed as the blueprint for a study. There are three main types of research designs in political science: exploratory, descriptive, and explanatory. Exploratory research attempts to discover which factors should be included when theorizing about and researching a particular subject. Descriptive research attempts to measure some aspect of reality for its own sake and not for the purpose of developing or testing some theory. Explanatory research uses observations of reality to test hypotheses and help develop an understanding of patterns of behavior in the context of a specific theory.

Regardless of the purpose of a study, every research design should have the same basic elements, which are outlined by Manheim et al. (2007): (a) a statement outlining the purpose of the research; (b) a review of the theory and any hypotheses that are going to be tested, if applicable; (c) a statement explaining the variables that will be used; (d) an explanation of the operationalization and measurement of the variables; (e) a statement of how observations will be organized, as well as conducted; and (f) a discussion of how the data that are collected will be analyzed.

Although both quantitative and qualitative researchers produce research designs for their studies, quantitative researchers are much more likely than their counterparts to base their designs on the logic of experiments. For instance, quantitative researchers often emphasize control groups, pretests, and other elements that provide them with the opportunity to hold some factor(s) constant in their attempt to make causal inferences. Qualitative research designs, on the other hand, typically focus more on who or what is being observed, where the observation will take place, how observations will be conducted, and how the data will be recorded. For qualitative researchers, more emphasis is placed on viewing people and events as they naturally occur, while for quantitative researchers there is a greater focus on establishing cause-and-effect relationships.

A sample is a small group of cases drawn from and used to represent a larger population under consideration. A representative sample is a sample in which each major attribute of the larger population occurs in approximately the same proportion or frequency as in the larger population. “In other words, a truly representative sample is a microcosm—a smaller, but accurate model—of the larger population from which it is taken” (Manheim et al., 2007, p. 119). When a sample is representative, the conclusions drawn from it are generalizable to the entire population.

In quantitative studies, sampling is based on the logic of probability to produce statistical representativeness. Additionally, in quantitative research, sampling is done before the data are collected. Qualitative researchers, on the other hand, usually create their sample once their study is already in progress. After observing, learning about, and gaining understanding from an initial case, qualitative researchers are then able to determine what they will observe next. Additionally, whereas generalizability is a chief concern for quantitative researchers, this is not the case for qualitative researchers, who are far more concerned with finding the specific information that they are looking for from their sample. Since this method is very time-consuming, qualitative findings are often based on fewer cases than quantitative findings.

Data are observations or information about reality that represent attributes of variables and result from the research process. Although data collection is an integral part of both types of research methods, data are composed of words in qualitative research and numbers in quantitative research, which results in a data collection process that differs significantly for quantitative and qualitative research. Furthermore, the data collection process is different: Although quantitative researchers have the ability to administer a previously prepared questionnaire or watch an experiment unfold behind blind glass, qualitative researchers are engaged—sometimes for long periods of time—with the people or groups under study.

As can likely be seen by now, quantitative researchers frequently have a detailed plan of action that is thought out prior to the beginning of a study’s taking place. Qualitative researchers, on the other hand, tend to take a more fluid approach to their studies. This holds true for the analysis of data, as well. Whereas in quantitative studies, the data analysis methods are planned out in advance and then occur after the data are collected, data analysis typically takes place at the same time as data collection in qualitative studies. To make appropriate future observations, analyses must often begin after studying one to several initial cases. As a result, quantitative researchers are not usually afforded the opportunity to modify their methods of data collection during a project, while qualitative researchers can do so at any point in a project after conducting the initial data analysis.

Additionally, although qualitative data are more subjective and sometimes difficult to interpret, quantitative data are easily coded into numerical formats. As a result, it is much easier to enter quantitative data into computer programs, such as Excel and SPSS, than it is to enter qualitative data. Furthermore, there are a number of programs that analyze the statistical data, such as SPSS and Stata. Although programs do exist for the interpretation of qualitative data, they are not used nearly as extensively as those used for quantitative data analysis.

Finally, whereas quantitative researchers have a variety of means to test the statistical significance and validity of the data that they are analyzing, this is not the case for qualitative researchers. Instead, qualitative researchers must do their best to present a clear, accurate, and convincing analysis of their data. As a result, a topic of much debate between quantitative and qualitative researchers is the validity and reliability of findings produced in studies. Validity is the extent to which measures correspond to the concepts they are intended to reflect. Reliability is the consistency with which a measuring instrument allows assignment of values to cases when repeated over time. Although a measure can be reliable without being valid, it cannot be valid without being reliable.

Additionally, since one of the main points of conducting quantitative research is to study causal relationships, part of the process involves manipulating various factors that could potentially influence a phenomenon of interest while at the same time controlling for other variables that could affect the outcome. For instance, if a researcher were examining if gender played a role in whether a person received a job, it would be important to control for other variables, such as education or previous work experience, since these factors may also determine why an individual would receive an employment offer. In quantitative analysis, empirical relationships and associations are typically examined by using general linear models, nonlinear models, or factor analysis to understand important information about the relationship between variables, such as the direction of a relationship. However, despite the results that may be produced by these models, it is important to note that a major tenet of quantitative research is that correlation does not imply causation. In other words, a spurious relationship is always a possible result of the data analysis.

When presenting the results of a study, qualitative researchers often have an arduous task in front of them. Since their reports typically rely on the interpretation of observations, it is necessary for them to be very careful in the selection of what stories, quotations, pictures, and so on, they will share in order to avoid bias. The reports produced by quantitative researchers tend to be more straightforward since they rely mostly on the interpretation of statistics. But here, too, it is important to make sure that bias was avoided in the sample and that appropriate data analysis methods were used in order to avoid bias in quantitative analysis.

To sum up, there are a lot of similarities among quantitative and qualitative research methods. Irrespective of which method is used, it is still necessary to create an appropriate research question, understand the theory behind what will be observed, create a research design, collect and analyze data, and create a report of the results. However, there are several key differences between quantitative and qualitative research methods. These methods differ in (a) the types of questions that they pose, (b) their analytical objectives, (c) the amount of flexibility allowed in the research design, (d) the data collection instruments that are used, and (e) the type of data that are ultimately produced. According to Mack, Woodsong, MacQueen, Guest, and Namey (2005), the fifth difference is the biggest. The authors argue that quantitative methods are generally inflexible since categories are typically closed-ended or fixed, while qualitative methods are more flexible, with a large amount of spontaneity and adaptation occurring during interaction with other people, especially in the form of open-ended questions.

To decide which research approach should be used, several things should be taken into account, including the problem of interest, the resources available, the skills and training of the researcher(s), and the audience for the research. Since there are considerable differences in the assumptions that underlie these two research approaches, as well as the collection and analysis of data, these considerations are important. The following sections provide a more detailed examination of the various types of quantitative and qualitative research methods, as well as the limitations of these methods in general.

Quantitative Methods in Political Science

Quantitative methods are essentially a variety of research techniques that are used to gather quantitative data. There are a variety of different types of quantitative methods, which are briefly outlined in this section: experiments, quasi experiments, content analysis, and surveys. First, in experiments, participants are randomly assigned to experimental conditions, as well as experimental controls. The individuals who are assigned to experimental controls are testing the independent variable. The difference between experiments and quasi experiments is the way that subjects are selected. In quasi experiments, participants are assigned to experimental conditions in a nonrandom fashion.

Next, content analysis is a systematic means of counting and assessing information in order to interpret it. For instance, scholars may count the number of times that personal characteristics, such as dress or hairstyle, are mentioned in newspaper articles to determine whether media coverage of male and female candidates differs. Finally, surveys are used to estimate the characteristics of a population based on responses to questionnaires and interviews from a sample of the population. Surveys provide five types of information: (1) facts, (2) opinions, (3) perceptions, (4) attitudes, and (5) behavioral reports. Essentially, questionnaires and surveys can serve as a means for helping scholars understand why people feel or act the way that they do, as well as measure their attitudes and assess their behaviors.

There are three key criticisms of quantitative research that are discussed here. First, since quantitative research methods were adopted from the physical sciences, critics argue that all cases are treated as though they are alike. Complex concepts are turned into numbers, and their unique elements are dissipated as a result. Furthermore, people can easily attribute different meanings to something even when they are experiencing the same phenomena. Second, and related to the first criticism, some people argue that quantitative methods are inherently biased. Since they are adopted from the physical sciences, critics argue that quantitative methods fail to take into account the unique cultural roots and other critical aspects of marginalized groups of people. Thus, according to critics, when it comes to populations that have been politically excluded, the usage of quantitative methods may not be appropriate, according to critics. Third, critics argue that quantitative research methods result in taking individuals out of their natural settings to examine very limited aspects of what a person thinks or believes. To these critics, context is very important, and by taking actions out of context, it is impossible to understand the true meaning of events or responses.

Qualitative Methods in Political Science

Just as quantitative research methods have a variety of research techniques that are used to gather data, there are also a variety of qualitative methods. This section focuses on several of these: ethnographic studies, phenomenological studies, case studies, focus groups, and intense interviews. First, in ethnographic studies, researchers examine cultural groups in their natural setting. Examples of cultural groups can include students in a dormitory, women in a crisis center, or people from a village in Asia. This type of study can provide rich, detailed information about the individuals in various groups, since it involves first-hand observation.

Second, in phenomenological studies, a small group of people is studied intensively over a long period to understand the life experience of the individuals being studied. Phenomenological studies can involve direct or indirect observation. Additionally, depending on the study, the individuals being observed may or may not know the purpose of the study or what exactly is being observed. Sometimes the researcher relies on building a trusting relationship with the subjects so the subjects act as naturally as possible even though they are being observed. As a result of this closeness, the researcher can often tell when a person is modifying his or her behavior. However, it is not always possible to establish this kind of relationship. As a result, some researchers conceal the purpose of their studies from those being observed to avoid the modifying of behavior by the subject. This process of behavior modification by the respondent is called reactivity and can greatly affect the results of a study.

Third, in a case study, a case is studied by a researcher, and detailed information about the entity or phenomenon is recorded. Sometimes information that is found in a case study can lend itself to the content analytical techniques discussed in the previous quantitative research section. Other times, newspapers, books, interviews, or other sources may be used. In content analysis, researchers are looking for specific words, phrases, or general ideas that are relevant to their study. The researchers will then count the instances of these items to learn more about a particular subject. For instance, some political scientists are interested in learning about gender bias in the media. By examining how often a female versus a male candidate is mentioned in an article or the type of coverage the candidate receives, these scholars are able to draw conclusions about gender bias in the media.

Finally, there are two other ways to collect and analyze qualitative data that are of relevance in this section—focus groups and intense interviewing. Focus groups are in-depth studies composed of small groups of people who have guided discussions. For instance, a focus group may be shown a political advertisement that a political campaign hopes to air on television. After watching the advertisement, members of the group are asked questions, and a discussion is prompted in which they can discuss their feelings about the ad, such as what they liked and did not like, as well as whether they were swayed by the ad and found it to be credible. These responses allow the advertisement’s producers to make changes that make the ad more effective.

Intense interviews are similar to survey questionnaires in that the interviewer generally has some thoughts in mind about what the respondent will be asked. However, although survey questions are planned out in their entirety in advance, this is often not the case in intense interviews where the interviewee has the ability to ask follow-up questions or a variety of other questions related to an answer provided by the respondent. Additionally, whereas survey questionnaire responses tend to be closed-ended (a particular response can be chosen from those available), intense interview responses are typically open-ended (no response categories) and can be very detailed. Thus, researchers have more flexibility when conducting an intense interview than they would if they were administering a questionnaire; however, their results are typically not quantifiable.

Just as quantitative methods have their detractors, so too do qualitative methods. Some of the biggest criticisms of qualitative methods are outlined in this section. First, some critics argue that qualitative methods focus too much on particular individuals, sometimes at the expense of seeing the bigger picture, and they fail to make their results generalizable to a larger population. Second, critics note that the quality of the results and analysis that are produced are highly dependent on the skill of the researcher. It is necessary for the researcher to have remained unbiased and provide a clear assessment of the subjects under study, or the results are essentially meaningless. Third, it is very time-consuming to conduct qualitative research studies. The amount of time spent conducting interviews and making observations is just the beginning. After these take place, the researchers still have to figure out a way to analyze the vast amounts of information that they have collected to produce results.

As can be seen from the information provided throughout this article, there has been a raging decades-long debate as to whether qualitative or quantitative research is better. Many scholars focus on qualitative versus quantitative techniques, automatically framing these methods and approaches in opposition to each other. Although it may appear that qualitative and quantitative data exist in opposition to each other, this is not necessarily the case. As King, Keohane, and Verba (1994) argue, “The two traditions appear quite different; indeed they sometimes seem to be at war. Our view is that these differences are mainly ones of style and specific technique. The same underlying logic provides the framework for each research approach” (p. 3). As a result, research does not typically fit into one particular category or another.

Additionally, King et al. (1994) note that we live in a world that changes rapidly, and to fully understand the changes that occur around us, it is necessary to be able to take into account information that can be quantified, as well as information that cannot. Furthermore, since social science requires comparison, it is important to examine both quantitative differences (such as which phenomena are more or less alike in degree) and qualitative differences (such as which phenomena are more or less alike in kind).

In recent years, scholars have been focusing a lot more on triangulation. Triangulation is essentially the idea that more than one research technique can be used to examine a research question to further verify the findings. Triangulation can help improve confidence about the results produced from a study. Quantitative and qualitative research can frequently be integrated, creating mixed-methods research that can depict a clearer picture of a social science phenomenon than one single method on its own.

Another way that quantitative and qualitative methods can exist together is by coding qualitative data into quantitative data. Just about any type of qualitative data can be assigned meaningful numerical values that can be manipulated to help condense the information and gain a different and more generalizable understanding of the data. One frequently used example is open-ended questions. Although more detailed insight is gained from an open-ended question than a categorical question, open-ended questions can typically be broken down into simple numerical categories allowing for a quantitative analysis of the data.

The Research Network on Gender Politics and the State (RNGS) serves as another good example. The researchers in RNGS had been conducting a crossnational, longitudinal, qualitative research project that explored changes in public policy processes dating back to the 1960s. Starting in 2000, however, the researchers began to code their vast qualitative data into a large quantitative data file. By using quantitative coding, additional useful information may be garnered, and a new form of data analysis is possible. As can be seen here, sometimes the line between quantitative and qualitative analysis may not be so clear after all.

On the other hand, quantitative data is inherently based on qualitative judgment because it is impossible to interpret numbers without understanding the assumptions underlying the numbers. When a person provides a numerical response to a survey question, for instance, many assumptions and judgments are present. For instance, if a person, when asked, “How satisfied are you with your life?” responds, “Very satisfied” (denoted by a value of 1), a variety of other questions could be asked. What does satisfaction mean to this respondent? Was he or she thinking only of the economic climate? Job? Family? Relationships? How does he or she define satisfaction, and how does this differ from how the next person defines satisfaction? Did the respondent even pay attention to or think about the question, or was he or she just offering quick responses? When and in what context was this question presented? The list goes on. As can be seen from this brief example, what appeared to be a simple numerical piece of information actually involved numerous judgments about the meaning of each response.

Quantitative and qualitative analysis are two general approaches to the analysis of data. Both seek to explain trends but have different means of doing this. Additionally, quantitative and qualitative research methods are each based on a basic set of assumptions. Both forms of research carefully follow each step in the research process, from formulating a research question to reporting the results of the data analysis. However, the order and ways in which this process is completed differ between quantitative and qualitative methods because of the different goals that researchers using these methods have for their studies. Essentially, though, at some level, quantitative and qualitative data are inseparable and do not exist in complete opposition to each other. Thus, it is almost self-defeating to claim that one method is better than the other. There are times when one is more appropriate to use in a given situation than another, but often, they can both be used together, whether at the same time or in different stages. As research progresses through the 21st century, it is highly probable that more scholars will use mixed-methods approaches.

References:

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• Adcock, R., & Collier, D. (2001). Measurement validity: A shared standard for qualitative and quantitative research. American Political Science Review, 95, 529-546.
• Agresti, A., & Finlay, B. (1997). Statistical methods for the social sciences (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
• Berg, B. L. (2003). Qualitative research methods for the social sciences (5th ed.). Boston: Allyn & Bacon.
• Box Steffensmeier, J.M., Brady, H. E., & Collier, D. (Eds.). (2008). The Oxford handbook of political methodology. New York: Oxford University Press.
• Brady, H., & Collier, D. (Eds.). (2004). Rethinking social inquiry: Diverse tools, shared standards. Lanham, MD: Rowman & Littlefield.
• Creswell, J. W. (2009). Research design: Qualitative and quantitative approaches (3rd ed.). Thousand Oaks, CA: Sage.
• Denzin, N. K., & Lincoln, Y. (2005). The SAGE handbook of qualitative research. Thousand Oaks, CA: Sage.
• Fink, A. (2005). How to conduct surveys: A step by step guide (3rd ed.). Thousand Oaks, CA: Sage.
• Frankfort Nachmias, C., & Nachmias, D. (1999). Research methods in the social sciences (6th ed.). New York: St. Martin’s.
• Frey, J. H., & Oishi, S. M. (2004). How to conduct interviews by telephone and in person. Thousand Oaks, CA: Sage.
• Gerring, J. (2001). Social science methodology: A critical frame work. Cambridge, UK: Cambridge University Press.
• Goodin, R. E., & Klingemann, H. D. (Eds.). (1996). A new hand book of political science. New York: Oxford University Press.
• Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Towards a conceptual framework for mixed method evaluation designs. Education Evaluation and Policy Analysis, 11, 255-274.
• Gujarati, D. N. (2002). Basic econometrics (4th ed.). New York: McGraw Hill.
• Hewson, C., Yule, P., Laurent, D., & Vogel, C. (2002). Internet research methods: A practical guide for the social and behavioural sciences. Thousand Oaks, CA: Sage.
• Hoover, K., & Donovan, T. (2004). The elements of social scientific thinking (8th ed.). Belmont, CA: Wadsworth.
• Isaak, A. C. (1975). Scope and methods of political science: An introduction to the methodology of political inquiry. Homewood, IL: Dorsey Press.
• Johnson, J. B., Reynolds, H. T., & Mycoff, J. D. (2007). Political science research methods (6th ed.). Washington, DC: CQ Press.
• Kinder, D. R., & Palfrey, T. R. (1993). Experimental foundations of political science. Ann Arbor: University of Michigan Press.
• King, G., Keohane, R. O., & Verba, S. (1994). Designing social inquiry: Scientific inference in qualitative research. Princeton, NJ: Princeton University Press.
• Leege, D. C., & Francis, W. L. (1974). Political research: Design, measurement, and analysis. New York: Basic Books.
• Levy, P. S., & Lemeshow, S. (2003). Sampling of populations: Methods and applications (3rd ed.). New York: Wiley.
• Lohr, S. L. (1998). Sampling: Design and analysis. Pacific Grove, CA: Duxbury Press.
• Mack, N., Woodsong, C., MacQueen, K. M., Guest, G., & Namey, E. (2005). Qualitative research methods: A data collector’s field guide. Research Triangle Park, NC: Family Health International.
• Manheim, J. B., Rich, R. C., Willnat, L., & Brians, C. L. (2007). Empirical political analysis: Quantitative and qualitative research methods (7th ed.). Essex, UK: Longman.
• Marshall, C., & Rossman, G. B. (1999). Designing qualitative research (3rd ed.). Thousand Oaks, CA: Sage.
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Reader Interactions

Introduction to Political Science Research Methods - 1st Edition

(3 reviews)

Josh Franco, Rancho San Diego, CA

Charlotte Lee, Berkeley, CA

Kau Vue, Fresno, CA

Publisher: Academic Senate for California Community Colleges

Language: English

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Reviewed by Hakseon Lee, Professor, James Madison University on 3/23/24

Most materials that are supposed to be taught at an introductory political science research methods are covered. Quantitative analysis section is relatively short, but considering it is an "intro" textbook, it's understandable. BTW Including Ch. 9... read more

Comprehensiveness rating: 4 see less

Most materials that are supposed to be taught at an introductory political science research methods are covered. Quantitative analysis section is relatively short, but considering it is an "intro" textbook, it's understandable. BTW Including Ch. 9 Research Ethics is very helpful for students understand research on “human subjects” more in depth.

Content Accuracy rating: 5

Overall, explanations of abstract and complex concepts are well presented. The concepts and definitions provided in the glossary are accurate as well.

Relevance/Longevity rating: 5

The textbook is written for undergraduate political science major students, and the level of complexity is quite relevant to them. Research methods materials are not fast changing subject and the textbook’s contents have longevity.

Clarity rating: 5

The textbook is written very clearly and easy to understand. After each chapter, summary of each subsection in chapters are provided in a very succinct and clear way, and I believe the summary sections are beneficial to students

Consistency rating: 5

Even though the textbook is written by several authors, they followed the same format of each chapter: providing clear learning objectives, summary, review questions, critical thinking questions, suggestions for further study, and references. Students will not be confused at all reading chapter by chapter.

Modularity rating: 5

Having total of 10 chapters, the textbook can be easily used module by module structure. Each chapter has subsections which have clear learning objectives, and this will be helpful for instructors who plan to use the textbook sequentially.

Organization/Structure/Flow rating: 5

Overall organization and structure follow conventional existing textbooks’ organization/structure. Majority of undergraduate research methods class are taught from history and development of research methods to quantitative analysis step by step, and the textbook follows the usual organization/structure.

Interface rating: 5

The book is very much reader friendly. Table of contents are very well organized and readers can have an easy overlook of the textbook.

Grammatical Errors rating: 5

I have not found significant or consistent grammatical errors at all.

Cultural Relevance rating: 5

Introducing diverse coauthors with cartoon images at the beginning of the textbook is helpful for students to learn about diverse authors. Also, examples used have diverse backgrounds.

Reviewed by Huei-Jyun Ye, Assistant Professor, Wabash College on 10/23/23

This textbook covers the scientific method of studying politics, theory and hypothesis building, conceptualization and operationalization, elements of research design, qualitative methods, quantitative methods, and research ethics. For the very... read more

This textbook covers the scientific method of studying politics, theory and hypothesis building, conceptualization and operationalization, elements of research design, qualitative methods, quantitative methods, and research ethics. For the very intro level (for freshmen or sophomores), this textbook will serve well. For advanced undergraduate courses, this textbook lacks an introduction to specific research methods like surveys, experiments, case comparisons, etc. This textbook touches a little bit on qualitative and quantitative approaches but does not explain the methods political scientists use. I feel this is a tradeoff for an introduction textbook. Instructors who are seeking materials explaining methods will need to find other supplements. Other than that, I would recommend using this book to explain the process of doing political science research.

The explanations of political science research methods are spot-on and comprehensible. I do not find big mistakes in the chapters.

I believe we can use this textbook for a long time as most of the concepts are standards of the field. Some examples address timely concerns that political scientists have been working on. The studies referred to in the textbook are also not obsolete.

The textbook is overall clear and easy to read. The authors make good efforts to explain the jargon in plain language. For example, when introducing conceptualization and operationalization, the author asks questions as if they were students and provides answers to explain the ideas. Different from throwing all the jargon and definitions to readers’ faces, students may find this conversation style more accessible.

The authors do a good job of setting a tone for this textbook, even though it is written by multiple authors. Each chapter starts with an outline, followed by content, glossary, summary, review questions, and suggestions for further study. Readers can expect all these elements in every chapter.

The chapters can be easily turned into sequential modules. What is even better is that the authors provide learning objectives for each section, not just for chapters. This design makes it easier for instructors to break down each chapter into smaller tasks. Students can know what to expect or take away from the subsections in each module. The summary and review questions at the end of each chapter also serve as a good wrap-up for individual modules.

The organization of the chapters is logical and straightforward. The subsections within each chapter are well-connected. Students would not have any problem building up their understanding of the research inquiry process when they read over the textbook.

The Introduction to Political Science Research Methods is reader-friendly. I have no difficulty in following the sections, and the formatting, including figures and tables, does not go off the place. Also, the PDF keeps the bookmarks so that readers can clearly see the structure on the sidebar and jump to different sections easily.

I do not notice major grammatical errors.

This book uses studies on various topics and has broad cultural implications. I appreciate that the examples and studies that the authors choose to demonstrate how to do political science research cover diversity and equity in society. The authors also present different schools of view without imposing a specific paradigm on the readers.

I recommend this book.

Reviewed by Lindsay Benstead, Professor of Politics & Global Affairs, Portland State University on 8/12/23

This textbook covers topics in a comprehensive overview of methodology used in Political Science. It is suitable for an introductory course (e.g., 100-200 level), in that it covers the 'History and Development of the Empirical Study of Politics,"... read more

Comprehensiveness rating: 5 see less

This textbook covers topics in a comprehensive overview of methodology used in Political Science. It is suitable for an introductory course (e.g., 100-200 level), in that it covers the 'History and Development of the Empirical Study of Politics," which includes basic facts about the history of the field of Politics. It then covers topics in quantitative and qualitative analysis. Importantly, it includes a section on ethics.

In my review of the textbook and use in designing a new course, I found the information presented in the textbook to be accurate.

Since this textbook covers foundational topics in research methods, it is likely to remain relevant for a decade or more.

This textbook is written in a clear way that will be understood by students in introductory political science methods courses (e.g., 100-200 level). This is not to say that more advanced students would not benefit from reading this textbook, but only if they are undergraduate or graduate students just beginning their study of research methods in the field.

This book is internally consistent. In addition to content in each chapter, it includes m/c questions, open-ended questions, and resources for further study. These are presented at the end of each chapter in such a way that they can consistently be assigned to students on a weekly basis and used in the preparation of exams and quizzes.

Each chapter is broken up into multiple sections, making it easy for instructors to present the material in modular and easily digestible ways.

The book is well organized, proceeding in a logical way from introductory material through quantitative topics, followed by qualitative methods and research ethics.

The pdf interface is easily navigated.

There are not grammatical errors in the book that I noted.

The textbook has several authors. The authors provide cartoon images of themselves. The group of authors come from diverse backgrounds, making the book more likely to help students from diverse backgrounds know that Political Science is their field of study.

Table of Contents

• About the Authors
• History of this OER
• Table of Tables
• Table of Figures
• Chapter 1- Introduction
• Chapter 2- History and Development of the Empirical Study of Politics
• Chapter 3- The Scientific Method
• Chapter 4- Theories, Hypotheses, Variables, and Units
• Chapter 5- Conceptualization, Operationalization, Measurement
• Chapter 6- Elements of Research Design
• Chapter 7- Qualitative Methods
• Chapter 8- Quantitative Research Methods and Means of Analysis 
• Chapter 9- Research Ethics
• Chapter 10- Conclusion

Ancillary Material

• Academic Senate for California Community Colleges

About the Book

Welcome to the official website for  Introduction to Political Science Research Methods  and  Polimetrics: A Stata Companion to Introduction to Political Science Research Methods  workbook!

Introduction to Political Science Research Methods,  authored by Dr. Josh Franco, Dr. Charlotte Lee, Kau Vue, Dr. Dino Bozonelos, Dr. Masahiro Omae, and Dr. Steven Cauchon, is an Open Education Resource textbook licensed CC BY-NC that surveys the research methods employed in political science. The textbook includes chapters that cover: history and development of the empirical study of politics; the scientific method; theories, hypotheses, variables, and units; conceptualization, operationalization and measurement of political concepts; elements of research design including the logic of sampling; qualitative and quantitative research methods and means of analysis; and research ethics.

Polimetrics: A Stata Companion,  authored by Dr. Josh Franco, is an Open Education Resource workbook licensed CC BY-NC and designed as a Stata companion to  Introduction to Political Science Research Methods . This workbook provides a tour of the Stata software, an introduction to cross-sectional, time series, and panel data, and an introduction to a variety of models. I review models where the outcome is linear, binary, ordinal, categorical, and count. Additionally, I have an interpretation chapter on survival models.

About the Contributors

Dr. Josh Franco, Cuyamaca College, Political Science: Josh Franco is a full-time, tenure-track Assistant Professor at Cuyamaca College in east San Diego County, California. He holds a Ph.D. and M.A. in Political Science, B.A. in public policy, and A.A. in economics and political science. Dr. Franco has five years of experience working in the California State Government and U.S. House of Representatives. Additionally, he was recently published in the peer-reviewed Journal of Political Science Education.

Dr. Charlotte Lee, Berkeley City College, Political Science: Charlotte Lee is full-time faculty at Berkeley City College. She teaches courses in political science and global studies. She has conducted fieldwork in Eastern Europe and China, culminating in several peer-reviewed publications in comparative politics, and will draw on that research in writing OER materials on qualitative research methods. Dr. Lee has participated in several Peralta district wide OER workshops. In February 2019, she co-facilitated an ASCCC OER Task Force webinar on resources in political science. Her Ph.D. is in political science from Stanford University.

Kau Vue, M.A. M.P.A., Fresno City College, Political Science: Kau Vue is an instructor of political science at Fresno City College in Fresno, California. She holds an M.A. in political science, a Master’s in Public Administration (M.P.A.) and a B.A. in political science and economics.

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Quantitative Research Methods for Political Science, Public Policy and Public Administration (Jenkins-Smith et al.)

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• University of Oklahoma via University of Oklahoma Libraries

The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models. The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publicly available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.

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The SAGE Handbook of Research Methods in Political Science and International Relations

• Edited by: Luigi Curini & Robert Franzese
• Publisher: SAGE Publications Ltd
• Publication year: 2020
• Online pub date: December 15, 2020
• Discipline: Political Science and International Relations
• Methods: Statistical modelling , Observational research , Theory
• DOI: https:// doi. org/10.4135/9781526486387
• Keywords: estimates , government , outcomes , political parties , social media , voting , war Show all Show less
• Print ISBN: 9781526459930
• Online ISBN: 9781529771077
• Buy the book icon link

Subject index

The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science - from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, and data collection, and quantitative and qualitative empirical analysis - exposited through 65 major new contributions from leading international methodologists. Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations: Part One: Formulating Good Research Questions and Designing Good Research Projects; Part Two: Methods of Theoretical Argumentation; Part Three: Conceptualization and Measurement; Part Four: Large-Scale Data Collection and Representation Methods; Part Five: Quantitative-Empirical Methods; Part Six: Qualitative and Mixed Methods.

Front Matter

• List of Figures
• List of Tables
• Notes on the Editors and Contributors
• An Introduction
• Preface: So You're A Grad Student Now? Maybe You Should Do This
• Part I | Formulating Good Research Questions and Designing Good Research Projects
• Chapter 1 | Asking Interesting Questions
• Chapter 2 | From Questions and Puzzles to Research Project
• Chapter 3 | The Simple, the Trivial and the Insightful: Field Dispatches from a Formal Theorist
• Chapter 4 | Evidence-Driven Computational Modeling
• Chapter 5 | Taking Data Seriously in the Design of Data Science Projects
• Chapter 6 | Designing Qualitative Research Projects: Notes on Theory Building, Case Selection and Field Research
• Chapter 7 | Theory Building for Causal Inference: EITM Research Projects
• Chapter 8 | EITM: Applications in Political Science and International Relations
• Part II | Methods of Theoretical Argumentation
• Chapter 9 | Political Psychology, Social Psychology and Behavioral Economics
• Chapter 10 | Institutional Theory and Method
• Chapter 11 | Applied Game Theory: An Overview and First Thoughts on the Use of Game Theoretic Tools
• Chapter 12 | The Spatial Voting Model
• Chapter 13 | New Directions in Veto Bargaining: Message Legislation, Virtue Signaling, and Electoral Accountability
• Chapter 14 | Models of Coalition Politics: Recent Developments and New Directions
• Chapter 15 | Models of Interstate Conflict
• Chapter 16 | Models of the Judiciary
• Chapter 17 | Wrestling with Complexity in Computational Social Science: Theory, Estimation and Representation
• Chapter 18 | Learning and Diffusion Models
• Part III | Conceptualization and Measurement
• Chapter 19 | Conceptualization and Measurement: Basic Distinctions and Guidelines
• Chapter 20 | Measurement Models
• Chapter 21 | Measuring Attitudes – Multilevel Modeling with Post-Stratification (MrP)
• Part IV | Large-Scale Data Collection and Representation Methods
• Chapter 22 | Web Data Collection: Potentials and Challenges
• Chapter 23 | How to Use Social Media Data for Political Science Research
• Chapter 24 | Spatial Data
• Chapter 25 | Visualizing Data in Political Science
• Chapter 26 | Text as Data: An Overview
• Chapter 27 | Scaling Political Positions from Text: Assumptions, Methods and Pitfalls
• Chapter 28 | Classification and Clustering
• Chapter 29 | Sentiment Analysis and Social Media
• Chapter 30 | Big Relational Data: Network-Analytic Measurement
• Part V | Quantitative-Empirical Methods
• Chapter 31 | Econometric Modeling: From Measurement, Prediction, and Causal Inference to Causal-Response Estimation
• Chapter 32 | A Principled Approach to Time Series Analysis
• Chapter 33 | Time-Series-Cross-Section Analysis
• Chapter 34 | Dynamic Systems of Equations
• Chapter 35 | Duration Analysis
• Chapter 36 | Multilevel Analysis
• Chapter 37 | Selection Bias in Political Science and International Relations Applications
• Chapter 38 | Dyadic Data Analysis
• Chapter 39 | Model Specification and Spatial Interdependence
• Chapter 40 | Instrumental Variables: From Structural Equation Models to Design-Based Causal Inference
• Chapter 41 | Causality and Design-Based Inference
• Chapter 42 | Statistical Matching with Time-Series Cross-Sectional Data: Magic, Malfeasance, or Something in between?
• Chapter 43 | Differences-in-Differences: Neither Natural nor an Experiment
• Chapter 44 | The Regression Discontinuity Design
• Chapter 45 | Network Analysis: Theory and Testing
• Chapter 46 | Network Modeling: Estimation, Inference, Comparison, and Selection
• Chapter 47 | Bayesian Methods in Political Science
• Chapter 48 | Bayesian Ideal Point Estimation
• Chapter 49 | Bayesian Model Selection, Model Comparison, and Model Averaging
• Chapter 50 | Bayesian Modeling and Inference: A Postmodern Perspective
• Chapter 51 | Laboratory Experimental Methods
• Chapter 52 | Field Experiments on the Frontier: Designing Better
• Chapter 53 | Field Experiments, Theory, and External Validity
• Chapter 54 | Survey Experiments and the Quest for Valid Interpretation
• Chapter 55 | Deep Learning for Political Science
• Chapter 56 | Machine Learning in Political Science: Supervised Learning Models
• Part VI | Qualitative and ‘Mixed’ Methods
• Chapter 57 | Set Theoretic Methods
• Chapter 58 | Mixed-Methods Designs
• Chapter 59 | Case Study Methods: Case Selection and Case Analysis
• Chapter 60 | Comparative Analyses of Foreign Policy
• Chapter 61 | When Talk Isn't Cheap: Opportunities and Challenges in Interview Research
• Chapter 62 | Focus Groups: From Qualitative Data Generation to Analysis
• Chapter 63 | Interpretive Approaches in Political Science and International Relations
• Editors’ Afterword

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Formal Theory & Quantitative Methods

Utilizes deductive, mathematical techniques and statistical methods to form and test theories, investigate political events and discover empirical generalizations.  

Mathematical and statistical methods are used widely to assess public opinion, evaluate governmental programs and institutions. In political science, mathematical tools are conventionally divided into formal theory and quantitative analysis .

Teaching in formal and quantitative methods is closely integrated with other parts of the department to maintain a close link between political ideas and mathematical assumptions. The teaching program at both the undergraduate and graduate levels is designed to build and reinforce those linkages. 

Centers & Programs

The Program for Quantitative and Analytical Political Science facilitates research and education at the interface between social science and statistics, economics, and computer science.

The Program on Race, Ethnicity, and Identity in Politics (PREIP) organizes thematic events and other initiatives linked to the study of race, ethnicity, and identity, including a department-wide speakers’ series and workshops on research issues related to the study of the politics of racial, ethnic, and other minoritized subpopulations. This inter-subfield program intends to create a supportive and generative community of faculty and graduate students working on a range of related topics including racism, colonialism, citizenship, contentious politics, social movements, and gender inequalities.

The Research Program in Political Economy (co-sponsored with the Department of Economics and the Princeton School of Public and International Affairs) supports scholarship at the intersection of economics and political science; the program sponsors workshops, conferences, fellowships, and student grants.

Germán Gieczewski

Saad Gulzar

Matias Iaryczower

Gleason Judd

John B. Londregan

Kristopher Ramsay

Arthur Spirling

Rocio Titiunik

Leonard Wantchekon

Zeyang (Arthur) Yu

Quantitative Analysis in Political Research

This course covers core ideas relevant for quantitative data analysis in the social sciences, with a focus on causal and statistical inference. Building on the material in 231A, we cover instrumental-variables analysis, non-linear regression models such as probit and logit, mediation and path analysis, and selection models. We also further discuss natural experiments and regression-discontinuity and difference-in-differences designs. We make frequent use of simulations, including the bootstrap. Students will complete a project using replication data from published work. Throughout the course, we emphasize the role of strong research designs in promoting valid causal inferences, and also the limitations of design. Model specification is a central area of concern. 

Discussion scheduled Wednesdays 4-6pm.

Political Science 231A or equivalent. Experience with R is assumed.

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8.4: Interpreting Statistical Tables in Political Science Articles

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• Josue Franco
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Learning Objectives

By the end of this section, you will be able to:

Political scientists often present their analytical results of the research in the table. In addition, quite a few articles or books often will include summary statistics as well, usually prior to presenting their analysis. The previous sections equipped you with enough information to accurately review analyzes data published in various journals. However as mentioned throughout this book, methodological advancements are a feature in political science, particularly in the advancement of quantitative approaches. Researchers will often borrow techniques from other disciplines, especially those with tangential puzzles or problems, such as economics, or psychology. Likewise, they will seek to incorporate new developments from statisticians and/or from mathematicians in formal modeling or game theory.

Again, even though some researchers in political science use mathematical models of behavior or have begun using experimental methodology, quantitative research in political science relies heavily on observational methods. Once the information has been coded and arranged into a datasets, political scientists will often use a type of regression analysis. Even though this type of quantitative analysis is the most common approach, an in-depth discussion on regression and other statistical techniques is beyond the scope of this chapter and the textbook. However, we believe that it is nevertheless important to introduce you to a basic understanding of a statistical table in a journal article, and how analytical results of quantitative research are generally presented.

To repeat, a student needs additional exposure and training in quantitative methods in order to properly interpret a table of results generated by a regression statistical analysis. However, there are some elements of a regression table that warrants a discussion in this section as political science students will be required to read such tables in the articles they have been assigned in class. However, even before a student begins the analysis of a regression table, she first needs to identify the causal relationship being examined in the article. In other words, the first task in the analysis of a statistical results table is to identify the outcome (dependent) variable(s) and the explanatory (independent) variables. In the process of identification, one also needs to understand how each variable is quantified/measured (see Section 8.1). Also, it is important to identify the statistical model being estimated. Again, all this discussion is beyond the scope of the current chapter. We merely want to make you aware that there are many things to consider when looking at a regression table.

The first number to understand in the regression table above is called the coefficient . Coefficients inform the reader of the nature of the relationship between the outcome and explanatory variables. Each coefficient has either a positive or negative sign. A negative sign indicates an inverse relationship with the outcome variable. In simpler terms, if the value of a coefficient goes up, then the value of the outcome variable goes down. Conversely, a positive sign on a coefficient means that an increase in the value of the coefficient results in an increase in the value of outcome. In terms of substantive definition of a coefficient, or what does this relationship, either inverse or positive, really mean will depend on the statistical model utilized in the study.

The second number, right below the coefficient in parentheses is the standard error . In a very useful website by Steven Miller (“Reading a Regression Table: A Guide for Students” 2014), he notes that “the standard error is [an] estimate of the standard deviation of the coefficient”. This helps us in understanding just how correlated the two variables are. And it tells us how potentially wrong the estimate is as it captures how much uncertainty we have in the model. The higher the standard error, the weaker the model is relative to variables. This means that we are not as sure if the correlation, or relationship between the variables, is as certain as it may appear. Finally, researchers use the standard error when looking to improve the certainty of the findings.

The third set of numbers to consider are at the bottom of the regression table. These are the confidence levels for each coefficient. The idea of confidence is very similar to the concept of statistical significance or alpha levels introduced in Section 8.3. Typically speaking in the social sciences, researchers use asterisks (*) to report the level of significance. A coefficient with one asterisk “*” indicates that the relationship between the outcome and that particular variable has 90% confidence. In addition, two asterisks “**” indicates 95% and three asterisks signifies 99% confidence accordingly. Most statistical software programs, including Stata, R, SPSS, and SAS, automatically report the significance level of the explanatory variables. If the coefficients do not have any asterisk at all, that means that the model was unable to distinguish if the relationship between the outcome and the variables were important. Instead, it could be a result of random or systematic factors. In this case, researchers would report that these coefficients without any asterisks were statistically insignificant.

Finally, remember that in a regression table, there could be quite a few additional reported numerical indicators. In addition, the variety of statistical figures these will change depending on the utilized models. Furthermore, a researcher may include additional diagnostic tests, often to ensure the robustness of the model. As noted above, in order for a student to feel fully equipped to confidently be a “consumer” of quantitative political research, additional quantitative method and statistic courses will be required. However, we hope that in the very least this chapter has piqued your interest in quantitative approach to political research.

Quantitative methods in political science: Research in France and the United States

• Data, Measures and Methods
• Published: 12 June 2015
• Volume 13 , pages 175–184, ( 2015 )

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• Michael S Lewis-Beck 1 &
• Éric Bélanger 2  

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We compare the recent methodological profile of political science work in France and the United States, applying a standardized content analysis of the research methods to leading political science journals in both countries over time periods of equal length. We find that, compared with the United States, qualitative work clearly dominates quantitative work in France, and there has been no apparent change over time in that regard during the period under investigation (1998–2013). We also find that French political science research does not offer a strong supply of mixed methods. Finally, and contrary to what is observed in the United States, when French political scientists do use quantitative methods they seem reticent about using ordinary least squares (OLS) or more sophisticated statistical methods. We see these results as further indication that the French and the American political scientists are locked in their own national epistemology.

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Our estimate is lower than the 34 per cent reported by Billordo (2005 , p. 180). Although, part of the reason for this difference may be because of the limited overlap between the time periods under investigation, we believe it is mostly because of the adoption of different coding schemes. The advantage of the one we used is that it allows for more direct comparisons with recent US results.

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Acknowledgements

We thank David Trotter and, especially, Quinn Albaugh for their precious assistance in preparing this review.

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Department of Political Science, University of Iowa, Iowa City, 52242, IA, USA

Michael S Lewis-Beck

Department of Political Science, McGill University, 855 Sherbrooke Street West, Montreal, H3A 2T7, Quebec, Canada

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Lewis-Beck, M., Bélanger, É. Quantitative methods in political science: Research in France and the United States. Fr Polit 13 , 175–184 (2015). https://doi.org/10.1057/fp.2015.7

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Published : 12 June 2015

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DOI : https://doi.org/10.1057/fp.2015.7

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5 Analytic Methods for Political Science Research

Analytical research is an integral part of any political science research. Knowing the different methods through which political researchers are able to analyze their research is crucial for anyone looking to develop new insight. 

For hundreds of years, political theorists, judges, lawyers, and legal scholars, have all worked to create new theories and means for understanding the ever-developing institutions that govern us.. Political science research influences real-world decision-making - from this year’s military spending budget, down to the amount of fees the post office is allowed to charge to send a letter.

There are a variety of unique tools and methodologies employed by researchers trying to tackle problems in the real-world. Today we will go over the essentials of political research, and highlight the many unique ways researchers can gather and analyze data.

What is Political Science?

Political science research is typically concerned with the theory and practice of governing, lawmaking, and politicking. Political scientists, like researchers in other disciplines, utilize a number of different methods and tools to conduct experiments, and gather new insights about a particular issue or phenomenon.

Five Methods of Political Science Research

Quantitative data analysis, qualitative data analysis, game theory models, historical analysis.

Quantitative data analysis is concerned with measuring the raw figures and numbers. This form of data analysis uses statistical models and math, to develop new theories about the world around us. 

Quantitative analysis is a form of descriptive statistics; meaning they are used to quantify the most basic features of a data set. Quantitative data can either be discrete (having to do with a particular set of numbers) or continuous - meaning that any numeric value could have a potential fit.  

Examples of quantitative data collection most commonly involve some kind of surveying or polling, and is concerned with gathering information such as:

• Test Scores
• Population Size
• Iterations of an Event
• Errors Made

These are all characteristics that can be easily picked apart and quantified using numerical data. They tell us how much of something there is in any given topic - allowing us to perform necessary calculations during our analysis. 

Conversely, qualitative data analysis is concerned with identifying and exploring those types of qualities that cannot be easily defined by numbers and figures. Qualitative data is most often composed of observations: descriptions of behaviors and phenomenon that cannot be quantified by numbers. Qualitative data analysis can be thought of as looking at the “how” or “why” of a particular issue, whereas quantitative data captures the “what”. These observations are invaluable to researchers, as they assign reason and motivation behind an action. Knowing what motivates someone to make a particular action is what drives the majority of political research projects today.

Qualitative data can be broken down into three distinct types:

• Ordinal Data
• Binary Data
• Nominal Data

Ordinal data exists on a ranging scale, and is one of the most prevalent types of questions found in a traditional survey. Questions that ask participants to share answers based on a sliding scale (such as “very unlikely” to “very likely”) are a common form of ordinal data collection.

Binary data is represented numerically, and is most often used in the creation of statistical models. These models can be used to track the likelihood of an individual to make a certain choice, among other things. 

Nominal data is used to label a subject without the use of numerical figures. These include multiple-choice survey responses, or cases where subjects are allowed to self-sort into a particular group.

Game Theory is a model for studying the decision-making process that goes on behind nearly every social interaction. Strategy, cost-benefit analysis, and optimal decision-making are all integral parts of the game theory model.

Researchers often use game theory models in order to better understand how individual actors come to a decision when faced with competition or consequence. The Prisoner’s Dilemma - where two convicts are tasked with choosing whether or not to inform on the other, therefore risking jail time, is a classic example of game theory in action. 

Historical analysis is a hugely important tool for political science researchers, as it enables them to present history as more than just a series of events that happened in succession. Overcoming this traditional and simplistic way of stating history - like the way you might see it described in a children’s textbook - is crucial for researchers looking to derive new insights from their political analysis. 

Researchers can draw upon historical inferences from a number of sources including historical texts, films, as well as first and second-hand accounts of events. Researchers will often build off of the works of prior authors in order to develop their own theories and outlooks.

  Historical analysis is a common and very effective model for deriving new insights from history. For example, judges often make determinations on matters of law by using historical and legal precedence to inform their decision-making.

Scenarios are a flexible tool that can be used to develop models; models that can be used to drive everything from policy making down to law enforcement. 

Scenarios can be as vast and unspecific - or as calculated and precise - as you need them to be. Social scientists often develop broad scenarios centered around a specific issue or problem they want to explore. These theoretical scenarios are then used to answer key questions like:

• How would (X) change if (Y) were to happen?
• What other factors could have influenced this outcome?
• Who are the key players in this scenario?
• What could we have done differently to prevent this?
• What variables exist outside of our control?

Theoretical scenarios are a cost-effective way for researchers to predict and forecast changing phenomena. They can also be used to argue for or against a particular course of action; enabling researchers to build up support for their conclusions, turning them into real-world action. 

Key Takeaways

For more information on the steps of the research process and data analysis, please visit our Helpfull research guide .

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