Comparative and Historical Sociology: Lecture Notes

Mathieu Deflem

This is a copy of the lecture notes of my graduate seminar in Comparative and Historical Sociology (SOCY 729) which I first taught at the Unversity of South Carolina in 2007.

Please cite as: Deflem, Mathieu. 2007. "Comparative and Historical Sociology: Lecture Notes." Unpublished paper. Available online via  


1) Pre-Sociological Social Science (Marx)
2) Max Weber & Emile Durkheim

A. Science, Theory, and Research
B. Research Design, Measurement, and Operationalization
C. Causal Modeling
D. Sampling Procedures
E. Observation Methods

A. On the History of Historical Sociology
B. Historical Research
C. Comparative Analysis (The Case)
Note: Research Proposals (NSF)

1) Comparisons, Explanation, and Theory
2) Path Dependence and Explanation
3) Sociology and History




See my notes on the sociological classics.


This section draws heavily on The Practice of Social Research by Earl Babbie.
This section is slightly revised from my notes: An Introduction to Research Design.


Research starts with the researcher, the position where you stand, your ethics, etc. The conceptions of the researcher influence the research topic and the methodology with which it is approached. Research is not just a matter of technique or methods. What is specific to social-science research, as compared to say journalism, is the quest to examine and understand social reality in a systematic way. What is observed is as important as how it is observed.

General outline of a research: theory, conceptualization of theoretical constructs into concepts, formalization of relationships, operationalization, measurement or observation, data analysis or interpretation, report.

1. Science and Reality

Science, as a system of propositions on the world, is a grasp of reality; it is systematic, logical, and empirically founded. Epistemology is the science of knowledge (what is knowledge?), and methodology is the science of gathering knowledge (how to acquire knowledge?). The inferences from science can be causal or probabilistic, and/or it seeks to offer understanding of social processes. Factors that intervene in the process of scientific inquiry include the available tradition of research and the status of the researcher. Scientific inquiry should reduce errors in observations and avoid over-generalizations.

Mistakes include: a) ex-post facto reasoning: a theory is made up after the facts are observed, which is not wrong as such, but the derived hypothesis still needs to be tested before it can be accepted as an hypothesis; b) over-involvement of researcher (researcher bias); c) mystification: findings are attributed to supernatural causes; in social-science research, while we cannot understand everything, everything is potentially knowable.

The two necessary pillars of science are logics and observation (to retrieve patterns in social life, i. e. at the aggregate level). Social-science research studies variables and the attributes that compose them. A variable is a characteristic that is associated with persons, objects or events, and a variable’s attributes are the different modalities in which the variable can occur (e.g. the attributes male and female for the variable sex). Theories explain relationships between variables, in terms of causation or understanding. Typically, this leads to identify independent and dependent variables (cause and effect), or situation, actor, and meaning (interpretation).

2. From Theory to Research

Different purposes of social-science research can be identified: 1) to test a theoretical hypothesis, usually a causal relationship (e.g. division of labor produces suicide); 2) to explore unstructured interests, which usually involves a breaking through of the empirical cycle, shifting from induction to deduction (e.g. what is so peculiar about drug-abuse among young black females); 3) applied research, for policy purposes (e.g. market-research).

The basic model of research is: 1) theory, theoretical proposition, 2) conceptualization of the theoretical constructs, and formalization of a model, the relationships between variables; 3) operationalization of the variables stated in the theory, so they can be measured (indicators) and 4) observation, the actual measurements. The inquiry can be deductive, from theoretical logic to empirical observations (theory-testing), or inductive, from empirical observations to the search for theoretical understanding of the findings of the observations (theory-construction).

The wheel of science! Deduction: the logical derivation of testable hypotheses from a general theory Induction: the development of general principles on the basis of specific observations


1. Research Design

Research design concerns the planning of scientific inquiry, the development of a strategy for finding out something. This involves: theory, conceptualization, formalization, operationalization of variables, preparations for observation (choice of methods, selection of units of observation and analysis), observation, data analysis, report (and back to theory).

a) Purposes of Research

The purposes of research are basically three-fold: 1) Exploration: to investigate something new which little is known, guided by a general interest, or to prepare a further study. The disadvantage of most exploratory studies is their lack of representativeness and the fact that their findings are very rudimentary. 2) Description: events or actions are observed and reported (what is going on?). Of course, the quality of the observations is crucial, as well as the issue of generalizability. 3) Explanation: research into causation (why is something going on?). This is extremely valuable research of course, but note that most research involves some of all three types.

b) Units of Analysis

The units of analysis refer to the what or who which is being studied (people, nation-states). Units of analysis can be (and often are) the units of observation, but not necessarily (e.g. we ask questions to individuals about their attitudes towards abortion, but analyze the religious categories they belong to). Units of analysis in social-science research typically include: individuals within a certain area at a given period of time; groups (e.g. the family); organizations (e.g. social movements); products of human action (e.g. newspapers in a content-analysis); and so on.

Two common problems are: the ecological fallacy, i. e. making assertions about individuals on the basis of findings about groups or aggregations (e.g. higher crime rates in cities with a high percentage of blacks are attributed to blacks, but could actually be committed by the whites in those areas); and reductionism, i. e. illegitimate inferences from a too limited, narrow (individual-level) conception of the variables that are considered to have caused something broader (societal).

c) Focus and Time of Research

The focus in a research can be on: 1) characteristics of states of being (e.g. sex of an individual, number of employees in a company); 2) orientations of attitudes (e.g. prejudice of an individual; the political orientation of a group), and 3) actions, what was done (e.g. voting behavior of individuals; the riot participation of a group).

Research, considered in its time dimension, can be 1) cross-sectional at any given point in time; 2) longitudinal over a period of time to trace change or stability (e.g. panel study of the same people after two elections to see if and how their voting behavior changed); 3) quasi-longitudinal by investigating certain variables in a cross-sectional study (e.g. a comparison of older and younger people indicates a process over time).

2. Conceptualization and Measurement

a) Conceptualization

Theories are comprised of statements that indicate relationships between constructs, i. e. particular conceptions which are labeled by a term. These constructs should be conceptualized, i. e. the meaning of the constructs must be specified, as a working agreement, into clearly defined concepts (which are still mental images). Then we can operationalize those concepts, i. e. specify indicators that measure the concept in terms of its different dimensions (e.g. the action or the ideas that are referred to by the concept of crime). Note that this process reminds us that terms should not be reified into things. Concepts, then, should be defined in two steps: first, a nominal definition of the concept gives a more precise meaning to the term, but it can not yet be observed as such, therefore, second, the operational definition of the concept spells out how it is to be measured or observed, so that the actual measurement can be undertaken. Example: theoretical construct = social control; nominal definition of concept = social control as the individual’s bonding to society; operational definition = attachment to primary institutions, which can be high or low; measure = years of education. Note that these specifications are absolutely necessary in explanatory research.

b) Measurement Quality

Reliability and validity refer to the relationship between measure and concept.

1) Reliability: does the replication of a measurement technique lead to the same results? This refers to the consistency of the measurement techniques. Reliability can be achieved through the test-retest method, i. e. the replication of a method on a phenomenon that could not, or should not, have changed, or of which the amount of expected change is known (e.g. asking for age, and asking again the next year, should lead to a difference of one year). Another technique for reliability check is the split-half method, e.g. if you have ten indicators for a phenomenon, then use five randomly chosen in one questionnaire, and the other five in the other one, apply to two random-samples, then their should be no differences in the distribution of attributes on the measured variable between the two. Other reliability techniques are the use of established methods, and training of researchers.

2) Validity: does the method of measurement measure what one wants to measure? This means different things: first, face validity is based on common-sense knowledge (e.g. the number of children is an invalid measure of religiosity); second, criterion or predictive validity is based on other criteria that are related to the measurement (e.g. racist actions should be related to responses to racist attitude scales); third, construct validity is based on logical relationships between variables (e.g. marital satisfaction measurements should correlate with measurements of marital fidelity); finally, content validity refers to the degree to which a measure covers all the meanings of a concept (e.g. racism as all kinds of racism, against women, ethnic groups, etc. ). Note that reliability is all in all an easier requirement, while on validity we are never sure. Note also the tension between reliability and validity, often there is a trade-off between the two (e.g. compare in-depth interviewing with questionnaire surveys).

3. Operationalization

Operationalization is the specification of specific measures for concepts in a research (the determination of indicators). Some guidelines: be clear about the range of variation you want included (e.g. income, age), the amount of precision you want, and about the dimensions of a concept you see relevant. In addition, every variable should have two qualities: 1) exhaustive: all the relevant attributes of a variable must be included (e.g. the magical ‘other’ category is best not too big), and 2) attributes should be mutually exclusive (e.g. whether a person is unemployed or employed is not exclusive, since some people can be part-time employed and part-time unemployed).

Variables are 1) nominal, when there attributes indicate different, mutually exclusive and fully exhausted qualities (e.g. sex: male or female); 2) ordinal, when the attributes can also be ranked in an order (e.g. type of education); 3) interval, when the distance between attributes in an order is precise and meaningful (e.g. IQ test); and 4) ratio, when, in addition, these attributes have a true zero-point (e.g. age). Note that variables do usually not in and by themselves indicate whether they are nominal, ordinal, etc. , or that you can convert them from one type to another (e.g. dummy-variables, from nominal to metric). Finally, note that you can use one or multiple indicators for a variable; sometimes even, a composite measurement is necessary.

4. Indexes, Scales and Typologies

An index is constructed by accumulating scores assigned to individual attributes. The requirements of scales are: face validity (each item should measure the same attribute), unidimensionality (only one dimension should be represented by the composite measure). Then you consider all the bivariate relationships between the items, the relationship should be high.

A scale is constructed by accumulating scores assigned to patterns of attributes. The advantage is that it gives an indication of the ordinal nature of the different items, one item is in a sense included in the other (higher ranked).

A typology is a break-down of a variable into two or more. As dependent variables this is a difficult thing, since any one cell in the typology can be under-represented (it’s best then to undertake a new analysis, making sure each cell is well represented).


1. Assumptions of Causal Inquiry

The first step in causal modeling involves conceptualization: what are the relevant concepts, and, second, how to operationalize these concepts. The next step is formalization, i. e. specification of the relationships between the variables. This seems to destroy the richness of the theory, but it helps to achieve comprehensibility and avoids logical inconsistencies. Note that this model is ideally based on a deductive approach, but it does not exclude a more dynamic approach which moves back and forth (from theory to data).

The causal model itself specifies not only the direction (from X to Y) but also the sign of the relationship (positive or negative). A positive relationship means that when X goes up, Y goes up; a negative relationship between X and Y means that as X goes up why goes down. between different paths, the signs should be multiplied to determine the net-effect. A causal system is consistent when all the causal chains push the relationship in the same direction (indicated by the fact that all the signs are the same). When some signs are positive, others negative, the system is inconsistent (suppressors).

Please note that the causality is not in reality (perhaps it is), but it is above all put into the model by virtue of the theory. This involves a notion of determinism (for the sake of the model), and that we stop some place in looking for any more causes or effects. Also note that the variables in a causal model are all at the same level of abstraction (ideally).

Causal explanations can be idiographic or nomothetic: 1) idiographic explanations seek to explain a particular events in terms of all its caused (deterministic model); 2) nomothetic explanations seek to explain general classes of actions or events in terms of the most important causes (probabilistic model).

2. Causal Order: Definitions and Logic

Prior variables precede the independent variable. Intervening variables are located in between the independent and dependent variable. Consequent variables are all variables coming after the dependent variable (unknown or not considered). Note that the identification of prior, independent, intervening, dependent, and consequent variables is relative to the model at hand.

The causal order between a number of variables is determined by assumptions that determine the causal system that determines the relationship between those variables. The following possibilities can be distinguished: - X causes Y - X and Y influence each other - X and Y correlate Variable X causes variable Y, when change in X lead to change in Y, or when fixed attributes of X are associated with certain attributes of Y. This implies, of course, that we talk about certain tendencies: X is a (and not the) cause. And this implies correlation as a minimum, necessary condition (the causation itself is theoretical).

3. Minimum-Criteria for Causality

Rule 1: Covariation - Two variables must be empirically correlated with one another, they must co-vary, or one of them cannot have caused the other. This leads to distinguish direct from indirect effects.

Rule 2: Time-order - When Y appears after X, Y cannot have caused X, or in other words, the cause must have preceded the effect in time.

Rule 3: Non-Spuriousness - When the observed correlation between two variables is the result of a third variable that influences both of those two separately, then the correlation between the two is spurious. This is indicated by a variable having a causal path to the two variables that correlate.

Basic to causality is the control of variables. Most ideally, this is done by randomization in experiments, then the attributes of any prior variables are randomly distributed over the control and the experimental group. We can also purposely control for prior variables when we select the ones we consider relevant. In bivariate relationships, no variables are controlled, while in partial relationships, one or more of the prior and intervening variables, that might interfere, are controlled. It is better still to identify the necessary and sufficient causes of certain effects but usually we are pleased with either one.


Sampling refers to the systematic selection of a limited number of elements (persons, objects or events) out of a theoretically specified population of elements, from which information will be collected. This selection is systematic so that bias can be avoided. Observations are made on observation units. A population is theoretically constructed and is often not directly accessible for research. Therefore, the study population, the set of elements from which the sample is actually selected, can (insignificantly) differ from the population.

The sampling procedures are designed to best suit the collection of data, i. e. to measure the attributes of the observation units with regard to certain variables. Depending on theoretical concerns and choice of method, probability or non-probability sampling designs are appropriate in research.

1. Probability Sampling

Probability sampling is based on principles of probability theory which state that increasing the sample size will lead the distribution of a statistic (the summary description of a variable in the sample) to more closely approximate the distribution of the parameter (the summary description of that variable in the population). These conditions are only met when samples are randomly selected out of a population, i. e. when every element in the population has an equal chance of being selected in the sample.

A randomly selected sample of sufficiently large size (absolute size, not size proportionate to the population) is assumed to be more representative for the population because the relevant statistics will more closely approximate the parameters, or the findings in the sample are more generalizable to the population. Representativeness of samples, or generalizability of sample findings, both matters of degree, are the main advantages of probability sampling designs.
a) Simple Random Sampling
In simple random sampling, each element is randomly selected from the sampling frame. Example: in an alphabetical list of all students enrolled at CU-Boulder, every student is numbered and students are selected using a table of random numbers.

b) Systematic Sampling
In systematic sampling, every kth element in a list is selected in the sample, the distance k indicating the sampling interval. The systematic sample has a random start when the first element is randomly chosen (out of numbers between 1 and k). Example: in a list of students enrolled at USC, each 100th student, starting with the randomly chosen 205th, is selected. Later it turned out that every other student in the list was female, since the composer of the list though “perfect randomness” would lead to perfect probability samples.

c) Stratified Sampling
Stratified sampling is a modification to the use of simple random and systematic sampling. It is based on the principle that samples are more representative when the population out of which they are selected is homogeneous. To ensure samples to be more representative, strata of elements are created that are homogeneous with respect to the (stratification) variables which are considered to correlate with other variables relevant for research (the standard error for the stratification variable equals zero). Example (stratified & systematic): luckily we know how stupid composers of student lists are, so we stratify students by sex (taking every other student in our “perfectly randomized” list); we thus get two strata of students based on sex, and select every 40th student in each stratum.

d) Cluster Sampling
In cluster sampling, clusters of groups of elements are created, and out of each group, elements are selected. This method is advantageous since often complete lists of the population are unavailable. Cluster sampling is multi-stage when first clusters are selected, then clusters within clusters (on the basis of simple random or systematic sampling, stratified or not), and so on, up until elements within clusters. While cluster sampling is more efficient, the disadvantage is that there are sampling errors (of representativeness) involved at each stage of sampling, a problem which is not only repeated at each stage, but also intensified since sample size grows smaller at each stage. However, since elements in clusters are often found to be homogeneous, this problem can be overcome by selecting relatively more clusters and less elements in each cluster (at the expense of administrative efficiency). When information is available on the size of clusters (the number of elements it contains), we can decide to give each cluster a different chance of selection proportionate to its size (then selecting a fixed number within each cluster).

2. Non-Probability Sampling

The choice between probability or non-probability design is dependent on theoretical premises and choice of method.

a) Purposive Sampling
Purposive or judgmental sampling can be useful in explorative studies or as a test of research instruments. In explorative studies, elements can purposively be selected to disclose data on an unknown issue, which can later be studied in a probability sample. Questionnaires and other research instruments can be tested (on their applicability) by purposively selecting “extreme” elements (after which a probability sample is selected for the actual research).

b) Sampling by Availability
When samples are being selected simply by the availability of elements, issues of representativeness about the population cannot justifiably be addressed. A researcher may decide to just pick any element that s/he bumps in to. As such, there is nothing wrong with this method, as long as it is remembered that the selection of samples may be influenced by dozens of biases and cannot be assumed to represent anything more than the selected elements.

c) Theoretical Sampling
In some theoretical models, it is unwise to conceive the world in terms of probability, sometimes even not as something to be sampled. First, in field research, the researcher may be interested in acquiring a total, holistic understanding of a natural setting. As such, there is no real sampling of anything at all. However, since observations on “everything” or “everybody” can in effect never be achieved, it is best to study only those elements relevant from a particular research perspective (sometimes called “theoretical sampling” or “creative sampling”). Second, when the elements in a natural setting clearly appear in different categories, quota sampling “in the field” can be used. This is the same as regular quota sampling, but the decisions on relevant cells and proportions of elements in cells are based on field observations. Third, snowball sampling is used when access to the population is impossible (methodological concern) or theoretically irrelevant. The selection of one element leads to the identification and selection of others and these in turn to others, and so on. (The principle of saturation, indicating the point when no more new data are revealed, determines when the snowball stops).

Example (cluster and snowball): in a study of drug-users in the USA, a number of cities (clusters) is randomly selected, a drug-user is selected in each city (e.g. through clinics), is interviewed and asked for friends that use drugs too, and so on. Example (snowball): a researcher is interested in African-American HIV infected males in Hyde Park, Chicago; the research aims at in-depth understanding of this setting, and inferences about other HIV infected males are trivial (apart from being impossible). Fourth, the sampling of deviant cases can be interesting to learn more about a general pattern by selecting those elements that do not conform to the pattern.

Example: 99% of the students at USC voted for Bush, so I select those that did not. These samples are purposive samples with a theoretically founded purpose. As long as that is the case, their use may be perfectly justified and, according to some theories, even the only applicable ones. The main disadvantage of non-probability sampling designs is the lack of representativeness for a wider population. But again, based on some theories, these difficulties can precisely be advantages (as long as the methodological and theoretical positions are clearly stated, both probability and non-probability sampling designs can be equally “scientific”).


A full research design is not just a matter of determining the right methods of observation, there is always (or there better be) theory first. The following procedure can be suggested. First, there should be a theory that states what is to be researched, and how this connects to the already available body of literature (to ensure, or strive towards, cumulative knowledge). There is no “naked” or mind-less observation. Second, the theory has to be conceptualized, so that the different variables of the theory are clearly defined and identified. This may also involve acknowledgment of the limitations. Third, the research topic and methodology is formalized into observable phenomena. This involvers specification of the research topic (where, when) and the methods of observation (how) as well as the way in which the data are to be analyzed, and the anticipated findings. Finally, after the research is conducted, a report is drawn up, indicating theory, methodology, as well as findings.

1. Survey Research

a) The Questionnaire
Survey research typically involves administering a questionnaire to a sample of respondents to draw conclusions on the population from which the sample is drawn. The questionnaire is standardized to ensure that the same observation method is used on all respondents. This involves considerations of questionnaire construction, question wording, and the way in which the questionnaire is administered to the respondents.

b) The Administration of a Questionnaire
Questionnaires can be administered in a variety of ways. * Self-Administered Questionnaire In this type of survey, respondents fill out a questionnaire delivered to them by mail, taking precautions to ensure a sufficiently high response rate, or they can be delivered “on the spot”, e.g. in a factory or school. The basic problem is the monitoring of returns, which have to be identified, i. e. you have to make up a return graph to indicate the response rate (over 50%), and you have to send follow-up mailings to non-respondents. * Interview Survey In a (more time-consuming and expensive) interview survey, sensitive and complicated issues can be explored face-to-face. This method also ensures a higher response rate, and a reduction of “don’t know” answers. The interviewer has more control over the data collection process (note that observations can be made during the interview) and can clarify, in a standardized way, unclear questions. Since the questionnaire is the main measurement instrument, the interviewer must make sure that the questions have identical meaning to all respondents: interviewers should (and are trained to) be familiar with the questionnaire, dress like the respondents, behave in a neutral way during the interview, follow the given question wording and order, record the answers exactly, and probe for answers. Interview surveys typically have a higher response rate (affecting generalizability). * Telephone Survey A questionnaire conducted by telephone is a cheaper and less time-consuming method, one moreover in which the researcher can keep an eye on the interviewers, but one on which the respondents can also hang up.

2. Field Research

While surveys typically produce quantitative data, field research yields qualitative data. Also notice how field-research often not only produces data but also theory (alternation of deduction and induction).

a) Entering the Field
Depending on sampling procedure, a research site is selected and observations will be made and questions asked within the natural setting. * The Role of the Field Researcher 1) complete participant: the researcher is covertly present in the field and fully participates as if he is a member of the community under investigation; the problems are ethical, your mere presence might affect what goes on, and there are practical problems (e.g. when and how to leave the field?); 2) participant-as-observer: the researcher participates yet his identity is known; 3) observer-as-participant: the researcher observes and his identity is known; the latter two, since identity is known, may affect what’s going on in the field, and it could cause the researcher to be expelled from the field; 4) complete observer: the researcher merely observes and his identity is not known. * Preparing for the Field and Sampling in the Field Start with a literature review (as always), then research yourself, why are you interested?, what will you bring to the field?, etc. Then search for informants, gate-keepers, and make a good impression (or simply join the group you want to study). Establishing rapport is very important, and if your identity is known, it is important to tell them what you are there for (although you may choose to lie). Then sample in the field (see above). Remember that the overall goal of field research is to acquire the richest possible data.

b) In-Depth Interviewing
Like any interview, an in-depth interview can be defined as a “conversation with a purpose”: an interview involves a talk between at least two people, in which the interviewer always has some control since s/he wants to elicit information. In-depth interviewing takes the “human element” more into account, particularly to explore a research problem which is not well defined in advance of the observation process. The in-depth interviewer is the central instrument of investigation rather than the interview guide. The procedure of in-depth interviewing first involves establishing a relationship with the respondent: even more than is the case with questionnaires, it is crucial that the interviewer gains the trust of the respondent, otherwise the interview will hardly reveal in-depth insight into the respondent’s knowledge of, and attitudes towards, events and circumstances. Since the kind of information elicited in the interview is not pre-determined in a questionnaire, tape-recording (and negotiation to get permission) is appropriate. The role of the in-depth interviewer involves a delicate balance between being active and passive. Note that in a field research, the interview can be formal or informal: in formal in-depth interviewing the researcher’s identity is known and the respondent knows that an interview is going on, while an informal in-depth interview appears to be (to the respondent) just a conversation with someone (covert). In-depth interviewing has the advantage of being able to acquire a hermeneutic understanding of the knowledge and attitudes specific to the respondent (without an “alien”, super-imposed questionnaire). It is often called a more valid research method. During a research process involving several in-depth interviews, the “big wheel of science” can freely rotate between induction and deduction (finding new things and asking about them).

c) Making Observations in Field Research
In your observations, be sure to see as much as you can and to remain open-minded on what you see; you want to understand, not to condemn or approve. Once you have taken up your role, make sure you get along with yourself (say what???), do not get over-involved, nor completely disengaged. Very important is to record what you observe accurately, and best as soon as possible after the event occurred. Therefore, you should keep a field journal (or tape). Field notes include what is observed and interpretations of what is observed. Also, keep notes in stages, first rather sketchy and then more in detail. Finally, keep as many notes as you can (anything can turn out to be important). Apart from that, a separate file can be kept on theoretical and methodological concerns, as well as reports of the researcher’s own personal experiences and feelings. As an initial step for analysis, the notes must be kept in files (with multiple entries), to discover patterns of behavior or practices, instances of attitudes and meanings of events for the observed, encounters of people in interaction, episodes of behavior (in which a sudden event can be crucial), and roles, lifestyles and hierarchies. These analytically conceived files should keep the chaos of observation together. Be flexible about your files. The analysis itself can then proceed to discover similarities and differences: what re-appears in the field, which events seem to indicate the same pattern of behavior or thought, as well as what is “deviant” in the research site, and so on. Note, of course, that it is typical for field research that observing, formulating theory, evaluating theory, and analyzing data, can all occur throughout the research process. In writing up the report, an account of the method of observation and/or participation, as well as reflections of the researcher’s experiences and motives are inevitable.

3. Evaluation Research

Evaluation research is intended to evaluate the impact of social interventions, as an instance of applied research, it intends to have a real-world effect. Just about any topic related to occurred or planned social intervention can be researched. Basically, it intends to research whether the intended result of an intervention strategy was produced. The basic question is coming to grips with the intended result: how can it be measured, so the goal of an intervention program has to be operationalized for it to be assessed in terms of success (or failure). The outcome of a program has to be measured, best by specifying the different aspects of the desired outcome. The context within which an outcome occurred has to be analyzed. The intervention, as an experimental manipulation, has to be measured too. Other variables that can be researched include the population of subjects that are involved in the program. Measurement is crucial and therefore new techniques can be produced (validity), or older ones adopted (reliability). The outcome can be measured in terms of whether an intended effect occurred or not, or whether the benefits of an intervention outweighed the costs thereof (cost/benefit analysis). The criteria of success and failure ultimately rest on an agreement.

There are a number of problems to be overcome in evaluation research. First, Logistical problems refer to getting the subjects to do what they are supposed to do. This includes getting them motivated, and ensuring a proper administration. Second, ethical problems include concerns over the control group (which is not manipulated, and whose members may experience deprivation). It is hard to overlook what is done with the findings of an evaluation research, for instance, because the findings are not comprehensible to the subjects, because they contradict ‘intuitive’ beliefs, or because they run against vested interests.

4. Experimental Designs

a) The Structure of Experiments
1) An experiment examines the effect of an independent variable on a dependent variable. Typically, a stimulus is either absent or present (see logic of causal modeling).
2) An experiment involves pre-testing and post-testing, i. e. the attributes of a dependent variable are measured, first before manipulation of the independent variable, and second after the manipulation. The group may be aware of what is being measured..
3) Therefore, it is better to work with experimental groups and control groups. We select two groups for study, then apply the pretesting-posttesting, and thus conclude that any effect of the tests themselves must occur in both groups. There can indeed be a Hawthorne effect, i. e. the attention given to the group by the researchers affects the group’s behavior. Note that there can also be an experimenter bias, which calls for accurate observation techniques of the expected change in the dependent variable.
4) Selecting Subjects: Randomization refers to the fact that the subjects (which are often non-randomly selected from a population) should be randomly assigned to either the experimental or the control group. This does not ensure that the subjects are representative of the wider population from which they were drawn (which they usually are not), but it does ensure that the experimental and the control group are alike, i. e. the variables that might interfere with the results of the experiment will, based on the logic of probability, be equally distributed over the two groups. 

Note that randomization is related to random-sampling only in the sense that it is based on principles of probability (the two groups together are a “population”, and the split into two separate groups is a random-sampling into two samples that mirror each other and together constitute this “population”). Matching refers to the fact that subjects are purposely assigned by the researcher to either the control or the experimental group on the basis of knowledge of the variables that might interfere with the experiments. This is based on the same logic as quota sampling. Matching has the disadvantage that the relevant variables for matching decisions are often not all known, and that data analysis techniques assume randomness (therefore, randomization is better). Finally, the experiments should be conducted in such a way that the only difference between the experimental and the control group is the manipulation of a variable during the experiment. Taken together, randomization or matching, and the fact that the manipulation during experimentation is the only difference between the two groups, these techniques allow for the control of all variables, other than the manipulated one, to interfere in the outcome of the experiment (internal validity!).

b) Internal Validity and External Validity

- Internal Validity: did the experimental treatment cause the observed difference? The problem of internal validity refers to the logic of design, the fact whether other variables that may intervene were controlled, i. e. the integrity of the study. The problem can be that the conclusions of an experiment are not warranted based on what happened during the experiment. This can come about because of: a) accident: historical events can have occurred during the experiment and affected its outcome; b) time: people change, mature, during the period of experimentation; c) testing: the groups are aware of what is being researched; d) instrumentation: the techniques to measure pretest and posttest results are not identical (reliability); e) statistical regression: results are biased because the subjects started with extreme values on a variable; and f) other problems include, that the relationships are temporal but not causal, and that the control group may be frustrated or stuff. Randomization of subjects into an experimental and a control group (to ensure that only the experimental manipulation intervened, while other variables are controlled), and reliable measurements in pretest and posttest are guards against problems of internal validity.

- External Validity: are the results of the experiment generalizable? The problem of external validity refers to the issue of generalizability: what does the experiment, even when it is internally valid, tell us about the real, i. e. non-manipulated, world? A good solution is a four-group experimental design, i. e. first an experimental and a control group with pretest and posttest, and second, an experimental and a control group with posttest only. And better than anything else is a two-group design with posttest only when there is good randomization, since randomization ensures that all variables are evenly distributed between experimental and control group so that we do not have to do a pretest. An experimental manipulation as close as possible to the natural conditions, without destroying internal validity, are the best methods to ensure external validity.


See the References section below for some of the sources used in this part.


Introduction: History and Sociology

a) Description and Analysis
Doing history is making history
Writing history is an intellectual activity directed by concepts and (more or less explicit) theoretical questions rather than a registration of chronologically ordered facts. Sociologists develop theories and methods in order to study empirical realities. Description and analysis are only analytically distinct (Bonnell 1980). b) The Unique and the General
Sociology was traditionally (represented as) nomothetic (generalizing), while history was idiographic (particularizing). Historical and/or contemporary topics of inquiry are not by themselves generalizing or particularizing. Instead, data are presented within an analytically relevant model as being unique or as representing a broader pattern. Relatedly, the tension between freedom and causality (choice and determinism) can be conceived in terms of a dialectics of history as contextualized human activity. Also, sociologists as well as historians can be interested in unique causes as they can be treated as manifestations of principles of broad developments across space and time (see Tilly 1997). Today, sociological notions of history as evolution have largely been expelled from modern sociological thought. Historical conceptions that emphasize fluidity and contradictory currents (Marx’ dialectics) appear to have survived better. But a key problem concerns deterministics versus actionism, structure v. agency (e.g., Althusser v. Thompson).

1. The Role of History in Sociology

The key is that historical sociology is not merely a sociological study of the past, but an intrinsic part of a sociology of the present: in order to explain the structures of contemporary societies, one must investigate their historical origins and development. It has been remarked that “Sociology is history with the hard work left out; history is sociology with the brains left out” (Cahnman & Boskoff 1964:1). In any case, history and sociology have historically developed into two distinct academic disciplines. Though there may be considerable overlap between both history’s and sociology’s material perspectives (human affairs, interaction, society), they differ formally in manner of approach, theoretically, methodologically, and, not least of all, institutionally. 

The classics were in varying degrees all historical, but mostly evolutionary. Much of post-war sociology was rather skeptical towards historical inquiry. Whereas Durkheim had introduced the distinction between causal explanation and functional analysis, Parsons and others had divorced causal-historical research and functional-synchronic analysis, whereby the latter was often the privileged perspective (survey research). The Chicago School likewise was not historically minded. Most historical perspectives of those days were development and modernization theories (societal differentiation). Likewise, history was dominated by the ideal of neutral-descriptive analysis of concrete historical events, and rather hostile towards abstract conceptual work in the grand theory tradition of sociology. The German historian Leopold von Ranke had delineated history as the chronological representation of the “way things really were” (“wie es eigentlich gewesen ist”). See: Stedman Jones (1976) who questions the equation of history with the past. From the late 1950s onwards, historians directed attention to sociology for conceptual clarification, while sociologists began focusing on history’s sense for empirical detail in order to complement and/or rectify grand theory. Influential, too, were the critique on modernization, inter-actionism’s anti-theoretical empiricism, and the rise of historical Marxism. So here we have the development of the so-called second wave of historical sociologists: they were anti-evolutionists, anti-modernization, and anti-quantification. The emphasis was on social change.

2. Historical Sociology 
Since the Second Wave See Adams, Clemens, and Orloff (2005). Sociology is closely associated with modernity. But modernity is changing and, hence, so is sociology. Historical sociology is one area where this changing theorizing is taking place. The second wave is changing, though there is no real third wave yet... To the Second Wave Historically, sociology was very attuned to comparative and historical thinking, see e.g., Marx, Durkheim, Weber, Tönnies, de Tocqueville. This is the first wave. But upon the institutionalization of sociology after WW II, things changed. Until the 1960s-1970s, much of US sociology was ahistorical and rooted in modernization theories of development. In reaction, however, some strong individuals emerged (Moore, Lipset, Bendix, Tilly) that led to the second wave of the 1970s and 1980s. Theoretically, much of this work was Marxian and Marxist in orientation. The works focused on political economy, the centrality of the state, the macro-structures of society, the material modes of production, not the cultural level. Modernization theory was discarded in favor of conflict-theoretical perspectives. Historical sociology then also institutionalized in the ASA and in specialized journals. Historical sociologists were accepted, but there were some difficulties. The use of small-N research and the Millean logic were questioned. Other methods were then developed. Mainstream sociologists critiqued historical sociology for not being sufficiently abstract, general and scientific, historians at once critiqued them for not being sufficiently attuned to the particularities of each case.

Beyond the Second Wave?
Theoretically there is no unifying analytical framework. 1) Institutionalism: this challenged the conception of an over-all deterministic structure, e.g., work on social policy. But still the emphasis remains on the political economy (and on politics, as in this book). 2) Rational Choice Theory: emphasis on the utilitarian assumptions of individuals and groups growing out of them. This development is not yet widespread but it will grow. 3) The Cultural Turn: this is very influential presently. The emphasis is on identity, on culturally variable categories, ‘imagined communities’, the self. 4) Feminist Thought: this has opened up many new topics of research and even challenged the center of society such as the state, rather than just the private realm of the family. Yet the masculine themes still resist. 5) Post-Colonialism: sociology is build on the European experience, which is now being challenged... What about the other peoples? So, at present, there is a certain lack of unity...


1. Historical Methods of Observation

a) Content and Document Analysis
Content analysis refers to the quantitative study of written and oral documents. This requires (probability) sampling of the units of analysis in a source, codification of the units, and classification to reveal content. (Krippendorf, K. 1980. Content Analysis. Sage.) Document analysis refers to the qualitative study of traces of the past: it involves the in-depth investigation of sources and aims at hermeneutic understanding.

b) Investigating Historical Sources (Pitt 1972)
See Pitt, David C. 1972. Using Historical Sources in Anthropology and Sociology. New York: Holt, Rinehart and Winston. Historical research is the study of the past through an examination of the traces the past has left behind, the sources of historical work, historical materials or documentary evidence, including: material remnants, written and/or otherwise recorded sources (primary and secondary), and oral history. The issue of accuracy is very relevant, though not exclusively so. Sources -- Historical research // Historical event (the past) -- Portrayal thereof (history)

The procedure of historical research typically involves:
1) Identification and Selection: relevant sources have to be identified and found. Types of sources: public and official archives (the state); social movements sources (cultural groups); business and company documents (economic); letters and diaries (private); literature (fictional); media sources (public opinion); maps and statistics (representations). Problems of availability (materials are lost) and access (sources are available but cannot be accessed, e.g. classified documents). Here are physical and social hurdles.
2) Registration and Classification: on the basis of formal and substantive criteria, depending on the research needs (e.g., the form of the document, time-period, the producer of the source, method of production, contents (intended and actual), audience, etc.). This is very important because it is the first step towards analysis (cf. labor-intensive aspect of much of historical research). Indicate precisely the place where the source originated and where it has been stored. Use computer programs for classifications and electronic indexing. Keep your system flexible so it can serve different purposes.
3) Critique and Confrontation: Are the sources authentic? Do they accurately portray events? Sometimes not many sources are available, so it is difficult to check. This is not very exciting because it mostly centers on details, but it is relevant nonetheless (often assumed). Differentiate fact from interpretation, translate accurately, corroborate with other evidence. If possible, “methodological marriages” with other methods are useful for corroboration to determine who said what to whom, why, how, and with what effect.
4) Analysis: The options are open: qualitative or quantitative, interpretation or explanation, structured or unstructured, within the context of theory and research strategy. The most traditional model is as follows: 1) theoretical proposition, 2) conceptualization of the theoretical constructs and formalization of a model; 3) operationalization of the variables of the theory (indicators), and 4) observation and conclusion. The inquiry can be deductive (theory-testing), or inductive (theory-construction). Also consider the various strategies of historical research relative to the objectives of historical sociology (see below).

c) Strategies (Skocpol)
See Skocpol, Theda & Margaret Somers 1980. “The uses of comparative history in macrosocial inquiry.” Comparative Studies in Society and History 22(2):174-197. 1) Parallel investigation of a theory: a theory is applied (or examined) in various historical contexts in order to demonstrate that various particular cases are but different modalities of a more general process (corresponding to natural-science conceptions of laws). 2) Interpretation of contrasting events: different, specific historical events are analyzed in their unique composition (typical for interpretive sociologies in the ‘Verstehen’ tradition). 3) Analysis of causalities at the macro level: based on Mill’s methodology (Skocpol) of the method of difference and of agreement. There is an important debate on the usefulness of Mill’s method for sociological research (see below) and the status of general theory in historical sociology.

d) Advantages and Disadvantages of Historical Methods
The unobtrusive nature of historical research is the main advantage of the method: the research cannot affect its subject matter. Also, several topics can be studied from this perspective, particularly forms of communication. The main weakness of historical research is that one can only reveal the past inasmuch as it is still present today: important documents, for instance, may be lost or destroyed (validity). And because of the often less rigid nature of this method of inquiry, the researcher can (invalidly) affect his/her picture of what has happened. Therefore, corroboration is helpful.

2. Theory and Historical Sociology

a) General Theory (Kiser and Hechter)
See Kiser, Edgar and Michael Hechter. 1991. “The Role of General Theory in Comparative-Historical Sociology.” American Journal of Sociology 97(1):1-30.
Historical sociologists emphasize the accuracy of the descriptive narratives they make about particular events. These events are unique and complex, so that the historical sociologists often taken methodological license, using vague and multiple strategies. In historical sociology, scope (generality) and analytical power have been minimized and replaced by descriptive accuracy. They are not interested in causal relations and the mechanisms that can account for producing these relations. The historical sociologists are inductive.

b) Theory in Historical Sociology (Quadagno and Knapp)
See Quadagno, Jill and Stan J. Knapp. 1992. “Have Historical Sociologists Forsaken Theory?” Sociological Methods & Research 20(4):481-507.
Historical sociologist have a deep concern for theory. Andrew Abbott, for instance, argues that historical sociology can uncover empirical regularities that can then be subjected to more conventional analysis. Also, comparative-historical sociology transcends the boundaries of time and space (temporal and national boundaries). According to Ragin, the use of macro-social units is what is distinct about comparative sociology. This parallels Kiser and Hechter’s emphasis on the mechanisms of causal chains, which lead them to focus on the actors involved. Interpretive sociologists are as theoretical as anybody. They also focus on alternative explanations, although they are not interested in general laws. Kiser and Hechter use a false dichotomy between induction and deduction. Theory can also be a starting point, a catalyst for further research. Theory can be tested, but theory can also lead to questions.


The debate on Mill’s methodology relates to the fact that sociological theories are probabilistic, not deterministic. Millean methododology also assumes that there is only one cause and that there are no interaction effects. How can the study of a case make sociological sense?

1. The Logic of Comparative Sociology (Jack Goldstone)
See Goldstone, Jack. 1999. “ASA Didactic Seminar on Comparative Sociology." Comparative and Historical Sociology 12(1) (with Bibliography).

a) What is comparative sociology?
Comparative sociology is a method, not a subject matter, applying various techniques to units. It involves the use of multiple, detailed observations on a modest number of cases, designed to uncover causal patterns. A case is a detailed understanding of a particular unit.

b) How Does It Work?
At least three issues are involved in comparative-sociological research: 1) Process tracing: the discovery of particular on-goings and their components over a period of time; 2) Combinations of causes and outcomes must be charted under varying conditions; and 3) Detective work: moving beyond correlation there should be a search for explanation, i.e. one must make sense of a link between variables. Also, attention is to be paid to an efficient picking of cases and to the formulation of a clear research question. Interestingly, a one-case study can be comparative inasmuch it implies an implicit comparison with the universe of cases that back up accepted wisdom. Universal generalization is not a necessary goal of comparative research.

c) Can Comparative Sociology Be Formalized Or Quantified?
Comparative sociology (as any other sociology) is always based on narrative, but a clear structure should also be revealed. For instance, particular combinations (x, y, f, z) of events can take place: A occurring with or without B, and A not occurring with or without B.Or various cases (1, 2, 3) can be compared in terms of certain characteristics (A, B,...). Such a structured presentation is not an explanation, but instead allows for multiple interpretations. Among the strategies of analysis are non-parametric analysis and Qualitative Comparative Analysis (designed by Charles Ragin) that uses Boolean algebra to implement principles of comparison.

d) The 3 C’s Of Comparative Sociology
The essentials of comparative sociology can be summarized as: Cases: to be designed across time, space, and units; Causes: the establishment of plausible connections and/or hypothesis testing; Comparisons: to test hypotheses, to illustrate causal connections, and/or to show variance in conditions and outcomes.

2. The Case for the Case
See Abbott, Andrew. 2001. Time Matters: On Theory and Method. University of Chicago Press.

a) Causal Devolution
In the 1950s, causality became equated with statistical research. Then causality was assigned to association. Path analysis was the crowning moment. Others, however, had attacked methods of association for lacking attention to causality. But the march of associational statistics could not be stopped (see history of statistics). Statistical methods imply theories which sociologists typically do not believe in. Instead, we should recognize that 1) description is a worthwhile objective, and 2) that causal methods are in fact descriptions (p. 123). Social events and processes are always located in concrete times and spaces.

b) What Do Cases Do?
A case can be an instance (a unit of analysis) or an exemplar of some property. In statistical pieces, narratives have variables as their subjects, not the cases. Cases do very little here, beyond a rational calculation. Cases are also undifferentiated. In single-case narratives, first the research subject has to be delineated (boundaries have to be set), such as an event, a social group, a state of affairs. The case can be transformed, e.g. an occupation can become a profession. So now the case becomes very central. After the events have been identified, they are ordered in some way that is explanatory. In multiple-cases narratives, we still have rich description as long as N is not too big (or can be summarized into categories of cases).

3. Example: Comparative History of Criminal Statistics (Deflem 1997)
See Deflem, Mathieu. 1997. “Surveillance and Criminal Statistics: Historical Foundations of Governmentality.” In Studies in Law, Politics and Society, Vol. 17, pp. 149-184.

4. The Merits of Comparative Historical Analysis See Mahoney, James and Dietrich Rueschemeyer, eds. 2003. Comparative Historical Analysis in the Social Sciences. New York: Cambridge University Press.

a) History v. Sociology Again
Abrams also argues that Moore shifts from a deterministic to a probabilistic model, adding new variables along the way that merely raise new ‘conditions favorable to...’. Events are a transformation device between past and future. According to Abrams, it also mediates structure and agency. If events would be completely unique, they would be beyond a generalizing analysis. Sociologist tend to move towards a general explanation of historical events by adopting an approach that is both logical and chronological. There is a ladder of conditions that are explanatory. There is a more fundamental general principle that underlies these stages that are held accountable for the happening of certain events.

b) Comparative Historical Analysis
CHA has been around for a long time, addresses many issues (though some such as law are understudied), and engages in theoretical and methodological controversies. CHA focuses very typically on the big questions. Big questions and comparative methods go hand in hand (see Durkheim on why). You need cases that are comparable in some sense, hopefully guided by theory. Practitioners of universalizing theoretical approaches see limits to this strategy, but not their own (ahistorical concepts and theories that are too general). CHA always consists of: 1) causal explanations; 2) processes over time; and 3) comparisons of similar and contrasting cases (such as nation states, their institutions, international constellations, etc.).

The methodological paradigm debates surrounding CHA are: 1) Quantitative versus qualitative: the small-N or large-N problem. CHA has been criticized for its small number of cases, bias in selection of the dependent variable, and bias in selection or use of secondary data. But CHA scholars have also addressed all these concerns. 2) Rational choice theory versus CHA: see Goldthorpe and Kiser & Hechter who see CHA as idiographic and anti-theoretical. 3) Interpretive/postmodern versus explanatory CHA: Recent scholars inspired by the cultural turn and postmodernism attack the explanatory approach of CHA, while the latter view that all scholarship contains explanatory elements.

c) Small-N / Large-N
Goldstone argues that large-N studies of revolutions have not been very fruitful, suggesting that the territory of revolutions is far from homogeneous. A revolution in China is not necessarily the same as a revolution in Cuba. So as Ragin says, different conditions can lead to similar outcomes in different contexts. So CHA never assumes a universal causal mechanism. Instead, CHA relies on a prior belief in a theory and then tests cases to see how they can and do shift that belief. Rueschemeyer on the value of a single case study. A single case can inspire theoretical ideas and falsify non-probabilistic propositions. But there are also positive contributions. Case studies can create, test, revise, and retest hypotheses to imply an ever-renewed questioning and testing. Also, case studies and comparative case studies have multiple goals. It makes the conditions of certain theories much more specific. It can also lead to the development of theoretical frameworks.


See the References section below for the sources used in this part.


a) Incommensurability, the Case Study, and Small N’s (George Steinmetz 2004)

Small-N studies are an important method of generating sociological understanding through “critical realism” and theoretically guided comparisons. Small-N comparisons are those research efforts that rely on a few cases (or even a single case) to develop sociological understanding, such as in ethnographic and historical research that seeks to unearth a great depth of understanding of a relatively limited target field.

The criticisms against small-N research come from two camps: 1) Methodological positivism assumes that “causal generative mechanisms are invariant across time and space”. Therefore, only very specifically controlled comparative analysis across large numbers of cases contain scientific value. 2) Nonpositivists cite three issues: Commensuration across observable social phenomena cannot be made because either: a) one cannot make assumptions about the generative mechanisms behind those observations (empiricism); or b) one cannot classify two instances as the same thing when the observed group understands themselves under a different self-description (nominalism). Also, there is an imperfect fit that comes about when making comparisons across languages and cultures. Many concepts cannot be translated perfectly. Lastly, the nonpositivist camp also challenges that some events possess a uniqueness that renders them incomparable to other events.

On the other hand, sociologists who recognize the importance of small-N comparisons defend their work against both camps: 1) Against positivists, small-N sociologists invoke critical realism to contend that the variations of social-causal structures across time and space are not universal but are instead “always and necessarily overdetermined by a plurality of conjuncturally interacting mechanisms.” Experiments are impossible to conduct in the social sciences, so that sociologists must study the generative mechanisms “in the wild.” 2) Critical realism contends that some of the issues raised by nonpositivists are conflating two different issues. If, in fact, two considered social phenomena are incomparable because they are genuinely constructed around different social mechanisms, then this does not invalidate all comparisons everywhere. Instead, it only speaks to the appropriateness of a comparison between those particular phenomena. Some fundamental similarities among the social world are always comparable and it is possible to displace one’s worldview and “see through another’s eyes,” even if in a limited fashion. In conclusion, “any social science oriented toward explanatory accounts will be necessarily involved in the study of specific cases.”

b) Realism, Rational Choice, and Relationality (Margaret R. Somers 1998)

Somers focuses on Kiser and Hechter’s criticism that “historical sociology is subverting the theoretical aims of social science”. Somers explains and places their position within a theoretical realism perspective and offers an alternative, relational realism, as a more conducive theoretical framework for historical sociological work. As Kuhn argued, scientific progress is always situated in particular historical periods. The cultural or historical circumstances that surround scientists at any given time must be taken into account when they propose knowledge-expanding theories. A theory-centric interpretation of Kuhn’s work (theoretical realism) influenced Kiser and Hechter’s view of the importance of rational choice theory, which they see as oriented at a theory-driven logic of social analysis and causal mechanisms. 

Theoretical realism assumes that theories survive to the extent that they are true pictures of what is real. Somers’s interpretation of Kuhn’s work challenges rational choice theory and “provides a renewed appreciation for problem-driven research and the centrality of history in the construction of knowledge.” Theory growth depends on facts turning into evidence only after and in response to a particular problem or research question. In doing so, historical elements of time and space are needed to help theory bloom. “Why a theory is confirmed at one time and not another”, she asserts, “requires a historical and causal explanation, rather than a strictly logical one.” 

Causal narrative and path dependency are methods in which theory can be linked successfully to real-world empirical cases in order to explain how and why phenomena occur within a particular time and space. With relational realism, “one can believe in the reality of a phenomenon without necessarily believing in the absolute truth or ultimate reality of any single theory that claims to explain it. Belief in a phenomenon or an outcome instead depends on evidence of its causal, practical, and relational significance in time and in space.” Historical sociology can complement and help to forge theory in a way that allows for causal mechanisms to be properly placed and explained in spatial-temporal contexts.


a) Initial Conditions, General Laws, Path Dependence (Jack Goldstone 1998)

Goldstone argues that both Somers and Kiser & Hechter sides are using faulty logic. The difference between these two camps lays in their emphasis on initial conditions (Somers) and general laws (Kiser and Hetcher). On the one hand, Kiser and Hetcher rightly argue that an over-reliance on initial conditions “will lead to purely narrative explanations of particularly sequences of events.” Yet, on the other hand, they also confuse issues of “general law, mechanisms, and desirable principles of explanation.” Goldstone states that good historical analysis choses “the method of explanation best suited for its explanatory goal.” A historical phenomenon that occurs often and in a variety of different settings and initial conditions would benefit from a general-theoretical approach. A historical phenomenon that occurs only occasionally and from specific initial conditions should be approached with a theory linking initial conditions to outcomes. Lastly, for the historical phenomenon that happens only once, despite similar initial conditions found elsewhere, should be approached as a path-dependent system.

b) Path Dependence in Historical Sociology (James Mahoney 2000)

Path dependence has been misconceived by historical sociologists as simply meaning that past events influence future events. Mahoney suggests a more specific definition of path dependence, which accounts for how “process, sequence, and temporality” affect social explanations of historical events.Path-dependent analyses minimally have three defining features: 1) the study of causal processes that are especially sensitive, in a sequence, to early historical events, which are more important than later events; 2) these events are contingent occurrences that cannot be explained by prior events or initial conditions; and 3) that once contingent events take place, the path dependent sequence becomes a deterministic pattern.

These three features are utilized in understanding two different types of path dependent sequences: self-reinforcing and reactive sequences:

1) Self-Reinforcing Sequences: Economists characterize self-reinforcing sequences appropriately by the feature of increasing returns. Important early events establish the sequence (feature one), which lead to contingent events which further the continuation of the sequence irrespective of initial conditions or expectations (feature two), and, finally, increasing returns lead to an accountable path that continues until an equilibrium is reached (feature three). Example: institution persistence.

2) Reactive Sequences: In a reactive sequence, each event in the sequence is both a reaction to antecedent events and a cause of subsequent events. Such sequences are characterized as “backlash processes” where important initial contingent events (features one and two) trigger reactionary events, which in turn trigger counter-reactionary events in a continuing sequence (feature three). With reference to chaos theory, Mahoney suggests that the final outcome of these sequences is extremely unpredictable. The purpose of path dependence analysis is to examine sequences of events that do not fit predictions of general social theory. As long as this divergence from theory can be established, the use of path analysis is completely appropriate. In examining such sequences of events, path analysis can provide much needed explication and inform future general theory, but otherwise it is just a fad.

c) Revisiting General Theory in Historical Sociology (James Mahoney, 2004)

Mahoney argues for the merits of general theory in empirical research. He defines general theories as “postulates about foundational causes,” involving a particular “causal agent” (unit of analysis) and “causal mechanisms” (i.e., properties of casual agents that produce outcomes and associations). Rather than referring to universal application, general theory is general in the sense of “its use of an abstract causal mechanism that exists outside space and time.” Among the benefits: 1) general theory forces analysts to be explicit about causal agent and mechanism; 2) it allows a broad scope of research agendas with a single causal mechanism; 3) it can generate new hypotheses; 4) it can explain inconsistencies of a causal mechanism by that causal mechanism (e.g., rational choice explains collectively irrational outcomes). To benefit empirical research, propositions are derived from postulates: “propositions are testable hypotheses and predictions about the occurrence of outcomes,” whereas “postulates are assertions about conditions in the world and the logical relationships that govern these conditions.” Postulates are assumptions used to generate logically deductible propositions. Historical outcomes are explained on the basis of general theory.

Five Examples of General Theory In functionalist theory, the causal agent is the social system, and the causal mechanisms are needs/requisites (equilibrium). It is a macro theory. Rational choice theory is a micro theory with the individual as the causal agent, and instrumental rationality as the causal mechanism. Power theory analyzes at the meso-level of investigation. The collective actor is the causal agent, and resources are the causal mechanism. The collective actor is seen as social groups and organizations rather than individuals. Neo-Darwinian theory is ‘radically micro’ in that the gene is the causal agent, and contribution to fitness is the causal mechanism. Cultural theory is a meso-macro level of analysis. The causal agent is collectivity and the causal mechanism is semiotic practices, which are “systems of meaning and associated practices.”


a) Narrative, Event-Structure Analysis (Larry Griffin 1993)

Griffin argues that event-structure analysis (ESA) should be used to examine causal relationships with narratives. Griffin claims that an event can be understood “both as a historically singular event and as an instance of a class of historically repeated events” and that this analysis can move from the particular to the general. Griffin points out that narratives are sequences of social action constructed by the narrator in a particular temporal order for a particular purpose that include a particular series of actions. Narratives allow for “deep theoretical knowledge about the mutually constitutive interplay of agency and social structure”, because the events are examined within their historical context. He does warn, however, that because narratives often blend both descriptions and interpretations, it is often easy to obscure causality of the events described within them. Griffin argues that one should consider counterfacts or “what if” questions. This type of analysis forces the researcher to ponder what did not happen thereby contextualizing what did, but this should be done within a historical and factual framework of objective possibilities. Causal interpretations can be arrived at by asking a series of questions based on facts and counterfacts about historical sequences.

Event-structure analysis (ESA) was developed by David Heise to analysis “cultural routines and the subjective representation reality.” The computer program ETHNO is the software application used to uncover causal and interpretative connections within the narrative. ESA is influenced by cognitive anthropology as well as rational choice theory and because of these features Griffin recommends that this analysis be used on narratives. ESA places emphasis, according to Griffin, on “temporal ordering and sequencing of action, not historical scope” (p. 1125). These he argues means neither micro or macro events limit the usefulness of ESA. ESA does not model social structure, but rather social actions are placed in logical sequence within the social structure. Griffin advocates the use of ESA in historical sociology because it uses temporal sequencing to explain social processes.

b) How Is Sociology Informed by History? (Larry J. Griffin 1995)

Sociologists disagree on the value of history. For some, history is only the “storehouse of samples” which serve as the base for the development of social theory. For others, history is important by itself. In any case, Griffin asserts that the use of history is increasingly visible in the discipline, as indicated by the rise of historical sociology articles in prestigious general journals; an increase in number and visibility of journals that specifically integrate history and social science (e.g., Comparative Studies in Society and History, the Journal of Historical Sociology, and Social Science History); the methodological incorporation of narrative, event, and biography as analytical tools; and the increase of books that integrate history and sociology. History is now more than ever used in many ways in sociology in order to enhance sociological explanation.

The author makes a case for a “historically infused sociology”, because when history is taken more seriously, time is also taken more seriously. History provides the social context of the sociological research matter: sociologists should thoroughly historicize their basic categories of analysis such as class, gender, and race, and they should fully exploit the explanatory and interpretive potential of narratives. Sociologists tend to see the analytical categories (e.g., gender, class, race): 1) in isolation from other concepts and categories, 2) fixed in meaning, and 3) positionally defined from social structure. This permits quantification and replication, but also it reduces sociologists’ “ability to discern the temporal fluidity and mutability of analytical categories”. In contrast, historians tend to see the analytical categories as historical products and processes which are elastic, ‘shifting’ and ‘unstable’ in content and definition because their meanings adapts through time. While for sociologists the analytical categories have fixed meaning and conceptual independence, for historians, the analytical categories have no fixed meaning and are conceptually tightly intertwined. Historians’ conceptualization is inconsistent with sociologists’ methodological conventions and analytical goals.

Historically infused sociology adopts narrative: First, the data “should consist of, and closely track, social actions through time”. Second, these data should be basically sequential in their “very definition and construction because social action is itself temporally sequential.” Sociologists should think about variables as actions, motives for action, and as consequences of actions. Third, sociologists should take advantage of information which conveys and carries action, such as stories, biographies, and other narratives. For Griffin, most sociological explanations do not incorporate time into the logic of explanation. Incorporating time into sociological explanations implies the use of an explanatory mode that is intrinsically temporal. Thus, narratives can be used to interpret and explain sociologically in order to distill the “logical from the chronological” and discerns the “theoretically general in the historically particular.” In sum, to take history seriously is to take time seriously and to use time as context and as narrative in sociological explanations.


Social Theory, Modernity, and the Three Waves of Historical Sociology, by Julia Adams, Elisabeth Clemens, and Ann Orloff. Russell Sage Working Paper, 2003.

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