Soc: 2.1-2.4 Book Notes
Key Concepts
The field: Empirical social science research in sociology aims to meet basic scientific standards as outlined by King et al. (1994). This foundational principle insists that sociological inquiry, despite its focus on complex human behavior, should adhere to rigorous methodologies akin to those in natural sciences, emphasizing objectivity and systematic investigation.
Core goal: Inference. When specific observations are made within a particular setting or group (e.g., studying a small community or a specific social interaction), the ultimate aim is to generalize findings beyond that immediate entity to similar phenomena or broader populations. This allows researchers to make wider claims about social processes that cannot be directly observed in their entirety. For instance, Diane Vaughan’s seminal work Uncoupling would not be considered a significant sociological analysis if its insights were limited solely to the 103 interviews conducted; its generalizable contribution stems from extending these specific findings to illuminate universal patterns in how individuals form and dissolve romantic relationships within society.
Public and reproducible procedures: All research procedures, encompassing both data collection and analysis methods, must be made public and transparent. This open methodology allows other researchers to meticulously retrace the steps, scrutinize the logic, and potentially reproduce the results. Such transparency is crucial for several reasons: it facilitates collective learning from successful methods, allows for the identification and addressing of methodological limitations, and enables the comparability of findings across different researchers and over extended periods, fostering cumulative knowledge.
Uncertainty as a core feature: Scientific conclusions, by their very nature, are inherently uncertain and probabilistic rather than absolute. Integrity in sociological research demands clear and unambiguous disclosure of all identified sources of uncertainty. This includes limitations of the data, potential biases, and the scope of generalizability. The highest status in the scientific community is often accorded to researchers who demonstrate intellectual honesty by clearly articulating the degree of certainty in their conclusions, rather than presenting them as definitive truths.
Reflexivity (especially emphasized in social science, particularly qualitative research):
The investigator is intrinsically part of the social world being studied and therefore cannot achieve complete detachment from it. Unlike in many natural sciences where the observer ideally does not affect the observed, social science recognizes the researcher's presence as an influence.
Power dynamics between researchers and subjects, alongside the researcher's personal values, biases, and social identities (e.g., class, race, gender, nationality), inevitably influence both the formulation of research questions and the subsequent interpretation of collected data. For example, a male researcher interviewing women about gender inequality might encounter different responses or interpret them differently than a female researcher.
Researchers are ethically and methodologically obligated to critically reflect on how their own social position (e.g., being a middle-class researcher studying poverty, or a dominant-group member researching a marginalized community) actively shapes their sociological arguments, their interactions with subjects, and the overall research process.
Shared standards across disciplines: The first three standards (inference, public and reproducible procedures, and uncertainty) are fundamental to both natural and social sciences and are equally applicable to quantitative and qualitative research methodologies. Reflexivity, however, represents a distinctive and paramount emphasis within social science research, acknowledging its unique challenges and ethical considerations.
Practical ideal vs. reality: While these four principles represent an ideal for rigorous scientific work, it is important to acknowledge that no single study might perfectly exemplify all of them simultaneously. The overarching goal for researchers is to maximize alignment with as many of these principles as possible, striving for the highest degree of scientific rigor achievable within the practical constraints of their research.
In addition to these guiding principles, sociological research systematically proceeds through a defined, sequential process, conventionally delineated into seven stages, to thoroughly investigate and answer research questions.
The Four Core Principles of Scientific Social Science (in more detail)
Inference: This principle mandates that researchers move beyond simply describing empirical observations to developing broader theoretical conclusions. The aim is to generalize specific findings from a limited dataset or selective observations to understand larger social patterns, structures, or populations that are not directly observed. This process of drawing robust, warrantable conclusions about the unobserved from the observed is central to building sociological knowledge.
Public and reproducible procedures: For research to be credible and contribute to a cumulative body of knowledge, all methods used for data collection (e.g., survey design, interview protocols, observational strategies) and data analysis (e.g., statistical tests, coding schemes for qualitative data) must be fully explicit and transparent. This openness allows for critical peer review, potential replication by other researchers, and facilitates the ongoing refinement of research techniques within the discipline.
Uncertainty: Acknowledging uncertainty is a hallmark of scientific integrity. Researchers must honestly identify and disclose all potential sources of uncertainty inherent in their study design, data, and analytical methods. This includes, but is not limited to, sampling error, measurement error, researcher bias, and limitations in the scope of data. Being upfront about the probabilistic nature of conclusions strengthens the credibility of the research rather than diminishing it.
Reflexivity: This principle necessitates critical self-awareness from the researcher. It involves a systematic examination of one’s own social position, personal biases, theoretical predispositions, and the power dynamics at play between the researcher and the researched. The goal is to understand how these factors might consciously or unconsciously influence the research process, from the questions asked and the data collected to its subsequent interpretation, ensuring a more nuanced and ethically sound analysis.
The Research Process: Seven Stages (an overview)
Sociological research is often conceptualized and taught as a systematic seven-stage process, initiating with the careful definition of a research question and culminating in the dissemination of research findings.
These seven stages are specifically designed to provide a structured framework for organizing both the design and the execution of research projects. However, it is crucial to recognize that real-world research endeavors often deviate from this rigid, linear sequence, as detailed in the "Reality Intrudes" section.
The seven stages, in their conventional order, are:
Stage 1: Define the Research Problem
Stage 2: Review the Literature
Stage 3: Make the Problem Precise
Stage 4: Work Out a Design
Stage 5: Carry Out the Research
Stage 6: Interpret the Results
Stage 7: Report the Findings
Stage 1 — Define the Research Problem
All research originates from a clearly articulated research problem. This problem typically stems from areas of factual ignorance within existing knowledge about social institutions, complex social processes, cultural phenomena, or demographic trends. It involves pinpointing what is unknown or poorly understood.
Example questions that illustrate the nature of a research problem:
What proportion of the population currently holds strong religious beliefs in a particular region, and how has this proportion changed over time?
To what extent are people in contemporary society expressing disaffection with “big government,” and what are the underlying sociological factors contributing to this sentiment?
How significant is the economic gap between the position of women and that of men in various sectors of the economy, and what mechanisms perpetuate this disparity?
Are there discernible differences in levels of self-esteem between LGBTQ+ and straight teenagers, and if so, what social factors account for these differences?
Analogy: Interpreting a complex painting, such as Pieter Brueghel the Elder’s Netherlandish Proverbs (1559), highlights this point. While one can observe countless details—the number of people, their actions, the artistic styles—these raw "data points" convey little meaning without a guiding theory, a conceptual framework, or historical context to interpret their significance and interconnections within the whole.
The most fruitful sociological research begins not merely with questions, but with puzzles. Puzzles invite deeper intellectual curiosity; they seek to understand why events happen as they do, rather than just what is happening. Examples include understanding changing patterns of religious belief, the rapid rise in American political polarization, the persistent underrepresentation of women in science and technology jobs, or identifying factors contributing to high-bullying schools.
No single piece of research exists in isolation. A well-executed study should not only answer its initial research question but also often raise new questions and puzzles, thereby actively contributing to and connecting with the ongoing dialogue among other researchers and broader societal trends within the discipline.
Example prompting new questions: The historical shift from institutional to community-based treatment for mental illness represents a significant social change. This shift immediately raises new sociological questions about its causes (e.g., changes in policy, medical understanding, public perception) and its far-reaching consequences for patients, their families, and the communities involved.
Stage 2 — Review the Literature
Following the identification and initial definition of a research problem, the critical next step is to rigorously consult and review all related prior research. This involves systematically examining whether other scholars have previously explored the same puzzle and, importantly, how they addressed it.
Key questions to guide a thorough literature review:
Have previous researchers specifically identified and investigated this same puzzle or very similar research gaps?
What theoretical frameworks did they employ, and what specific methods did they use in their attempts to solve it? What were their main findings and conclusions?
Which facets or nuances of the problem continue to remain unanalyzed, overlooked, or inadequately explained by existing research?
Did prior research focus exclusively on narrow segments of the population (e.g., studies confined to a single age group, a specific gender, or a limited geographical region)? If so, are there limitations to their generalizability that need to be addressed?
Purpose: The primary purpose of this comprehensive literature review is twofold: first, to clarify all relevant theoretical, methodological, and empirical issues pertinent to the research problem; and second, to identify appropriate research methods and theoretical perspectives by building upon, critiquing, and extending the ideas and findings of others. This process ensures that the research is well-informed and positioned within the existing academic discourse.
Stage 3 — Make the Problem Precise
If a substantial body of literature exists addressing the research problem, the researcher will typically have developed a more refined sense of how to approach the puzzle and what insights can be gained from prior work.
This stage most often involves transforming initial, often vague hunches or broad research questions into concrete hypotheses. Hypotheses are educated guesses or tentative statements about the relationship between two or more variables, which are derived from existing theory or prior observations.
Crucially, a hypothesis must be formulated with enough specificity and clarity so that empirical data can be gathered to either definitively support or logically refute it. This testability is fundamental to scientific inquiry, allowing for a structured empirical investigation.
Stage 4 — Work Out a Design
In this critical stage, the researcher must make strategic decisions about how to systematically collect the necessary research materials (data). The selection of appropriate research methods is dictated by the specific objectives of the study, the nature of the behaviors or social phenomena under investigation, and the practical resources available.
Possible methods, each with its own strengths and limitations, include:
Survey research (typically employing questionnaires): Ideal for gathering data from large populations to identify patterns, attitudes, or prevalence of specific behaviors. They can be administered via interviews, mail, phone, or online.
Interviews (in-depth, semi-structured, or structured): Used to collect rich, detailed qualitative data from individuals, exploring their experiences, perspectives, and motivations in greater depth than surveys allow.
Observational studies (participant or non-participant): Involve direct observation of social behavior in natural settings, often used to understand social interactions and cultural practices as they unfold organically.
Experiments: Controlled methods used to establish cause-and-effect relationships between variables, often involving manipulation of an independent variable and measurement of its impact on a dependent variable.
Historical analysis: Examination of past events and historical documents to understand social change over time.
Comparative research: Involves comparing findings from different societies or groups to identify similarities and differences in social phenomena.
Stage 5 — Carry Out the Research
This stage involves the practical execution of the meticulously planned research design. While a thorough plan is essential, researchers must be prepared to encounter unforeseen practical difficulties and unexpected challenges that may necessitate adaptive adjustments to the original plan. These could range from difficulty gaining access to a population, logistical issues with data collection tools, or unexpected refusals to participate.
A significant concern during this stage is the potential for biases to arise, particularly if certain populations, groups, or institutions are unintentionally or deliberately omitted from the study. Such omissions can lead to a systematically incomplete or distorted picture of social reality. For example, if a study researching corporate compliance with affirmative action programs only manages to secure participation from companies that are already highly compliant, the findings will be inherently biased, yielding an overly positive and unrepresentative view of overall compliance rates. Addressing such biases often requires careful sampling strategies and diligent efforts to achieve representativeness.
Stage 6 — Interpret the Results
Once data collection is complete, the researcher proceeds to rigorously analyze the collected data. This involves identifying significant patterns, statistical trends (for quantitative data), recurring themes (for qualitative data), and then systematically testing the initial hypotheses formulated in Stage 3.
The overarching goal of interpretation is to construct a clear, coherent, and compelling narrative that addresses the initial research puzzle stated in Stage 1. This narrative should integrate findings into a meaningful whole.
It is important to recognize that not all investigations will yield fully conclusive answers or provide definitive evidence to either fully support or refute a hypothesis. Therefore, a critical aspect of interpretation is to explicitly connect the findings back to the original puzzle, delineate the scope of the conclusions, and discuss their broader implications for existing social theory, public policy, or professional practice. This stage often involves wrestling with ambiguities and acknowledging the complexity of social phenomena.
Stage 7 — Report the Findings
The final product of the research process is typically an academic journal article, a chapter in an edited book, a complete book, or a detailed research report. This formal document meticulously describes every aspect of the research: the origins of the research question, the theoretical framework, the methods employed, the key findings, and their implications for social theory, public policy development, or practical application in various fields.
Importantly, these reports generally do not present findings as final or exhaustive. Instead, they often explicitly identify unanswered questions, point out limitations of the current study, and propose concrete directions for further research, thus perpetuating the scholarly dialogue.
By reporting findings openly and transparently, the research becomes an integral part of an ongoing, cumulative process within the broader sociological community. Each study builds upon previous ones, contributes to the collective understanding of society, and helps refine theoretical models and methodological approaches.
Reality Intrudes! (Limitations of the seven-stage model)
While the seven-stage sequence provides an invaluable conceptual guide and an idealized framework for approaching research, it is crucial to understand that actual research endeavors rarely proceed in such a perfectly neat, linear, or sequential order. The complexities and contingencies of real-world research often demand a more fluid and iterative approach.
Deviations from this strict sequence are not only common but can often be necessary and even beneficial, allowing researchers to adapt to unforeseen circumstances, new insights, or practical constraints. Insisting on a rigidly fixed scheme can be unduly restrictive, hindering creativity and adaptability in the face of complex social phenomena.
Analogy: Consider the distinction between following a highly prescriptive cookbook recipe and the dynamic, adaptive process of actual cooking by an experienced chef. While a recipe provides a foundational structure, a skilled chef will often adjust ingredients, techniques, and timings based on intuition, available resources, and the specific context—much like an experienced researcher adapts the "stages" to fit the unique context and constraints of their study, moving back and forth between stages as needed.
Practical Implications and Examples (Connections to real-world research)
The Uncoupling study (Diane Vaughan): This ethnographic study serves as a prime example of how a seemingly limited dataset (in-depth interviews with 103 individuals) gains profound societal relevance. Its integration into a broader theoretical argument about the universal processes of relationship formation and dissolution transcends the specific cases, thereby making a generalizable, enduring contribution to sociological understanding.
Brueghel painting as an illustration: The analogy of interpreting Pieter Brueghel the Elder's Netherlandish Proverbs (1559) powerfully illustrates that raw data—mere observations of facts or behaviors—are insufficient on their own. They fundamentally require a guiding theory or conceptual framework to yield meaningful insights, connecting discrete observations into a coherent and interpretable whole. Without theory, data remain a collection of isolated facts.
Reflexivity example: The scenario of a middle-class researcher conducting a study on poverty vividly highlights the importance of reflexivity. Such a researcher must openly disclose how their own class position, and the associated life experiences and perspectives, could potentially influence the types of questions asked, the interpretation of subjects' narratives, and the rapport established or hindered with subjects. This transparency enhances the study's validity and ethical rigor.
Affirmative action research example: Examining corporate compliance with affirmative action programs underscores how methodological decisions directly impact the completeness and validity of data. If companies that are less compliant are also more likely to refuse participation in the study, the resulting data will be skewed. This biased sample would create a misleadingly positive picture of compliance, thereby undermining the validity of any conclusions drawn about the broader corporate landscape. Researchers must actively anticipate and strategize to mitigate such selection biases.
Check Your Understanding (Sample Questions)
Question 1 (1 of 8): Rhonda is a researcher deciding between measuring intelligence in urban vs rural youth or studying how exercise affects intelligence. What step of the research process is she in?
A. Review the evidence
B. Define the research problem
C. Make the problem precise
D. Carry out the research
Answer: B Define the research problem
Question 2 (2 of 8): After the researcher has worked out a design for their research, what are the next three steps of the research process in order?
A. Report the findings, carry out the research, and interpret the results
B. Report the findings, interpret the results, and carry out the research
C. Carry out the research, report the findings, and interpret the results
D. Carry out the research, interpret the results, and report the findings
Answer: D Carry out the research
Park vs Ogburn: Historical Context
Timeframe and lifespans
Robert Park: 1864\text{--}1944
William Ogburn: 1886\text{--}1959
The critical period for the shift to empirical sociology occurred in the 1920s, primarily centered at the burgeoning sociology department of the University of Chicago. This marked a significant departure from earlier, more philosophical or social-reform-oriented approaches to sociology.
Timeline anchors: Park's advocacy for a fieldwork-based approach to understanding social life; Ogburn's strong emphasis on rigorous science and quantitative measurement as the future of the discipline; Ogburn's influential presidential address in 1930 to the American Sociological Society.
Park’s perspective: building sociology from the lives of people
Background and influences
Park's intellectual journey was shaped by his extensive studies in philosophy in Europe, particularly in Germany, and his prior career as an investigative reporter for the Minneapolis Star. These experiences instilled in him a profound appreciation for firsthand observation and a critical understanding of social structures.
He firmly believed that sociological theories should not be abstract constructs but must relate directly to actual human lives and be rigorously grounded in empirical evidence derived from direct observation.
Core methodological stance
Park emphasized the production of sociological writing that was both systematic and evidence-based, yet fundamentally tied to the lived experiences and narratives of people. This meant moving beyond armchair speculation to gather real-world data.
His famous instruction to students encapsulated this ethos: "to do real research, you must get the seat of your pants dirty and wear out your shoe leather." This was a call for embodied research, urging direct engagement with research subjects and their environments.
Fieldwork and the city as laboratory
Park strongly advocated for sociologists to immerse themselves in urban environments, to literally "go around all city neighborhoods" to discover the authentic social processes and interactions by meeting and conversing with the people who were the subjects of sociological theories.
The University of Chicago sociology department, under his influence, conceptualized the city itself as a vast social laboratory. Researchers were encouraged to become embedded in communities, conducting extensive interviews, life histories, and firsthand observations to construct detailed sociological accounts.
Subject matter and goals
Park's research interests often focused on complex social phenomena like immigration, urbanization, social disorganization, and the intricate dynamics of city life.
The reports generated from his school of thought tended to be highly systematic, meticulously written, and often explicitly oriented toward understanding and ultimately improving social conditions within the city and the broader United States. His work frequently carried an implicit or explicit commitment to social reform grounded in empirical knowledge.
Ethical and philosophical stance
Park's approach aimed to develop comprehensive explanations about the social world that seamlessly connected with actual lived experiences. This involved a delicate balance of personal narratives, emotional insights, and rigorous scientific analysis, recognizing the subjective dimensions of social reality.
Quotations that capture Park’s ethos
The imperative "to get the seat of their pants dirty, to wear out their shoe leather" summarized his belief that genuine sociological understanding came from sustained, direct engagement with the social world, moving beyond superficial analysis to discover deeper truths.
Ogburn’s perspective: sociology as a science grounded in measurement
Background and critique of Park’s path
William Ogburn, while a colleague of Park at Chicago, held a fundamentally different vision for the future of sociology. He did not believe that the discipline's advancement lay in qualitative, immersive fieldwork or findings that relied heavily on subjective interpretation.
He controversially argued that domains such as ethics, religion, journalism, and propaganda, which often involved subjective values or non-quantifiable phenomena, fell outside sociology's legitimate scientific purview.
Core argument and goal
In his highly influential presidential address to the American Sociological Society (now the American Sociological Association) in 1930, Ogburn made a forceful argument that sociology needed to definitively become a mature science, shedding its earlier associations with social reform and philosophy.
He explicitly stated that the goal of scientific sociology was not to "make the world a better place," nor to provide "impressions of life," nor to "[guide] the ship of state." Instead, the paramount objective was singular: to "discover new knowledge" through objective, verifiable means.
Emphasis on quantification and scientific form
Ogburn asserted that sociology could scientifically study any social phenomenon that could be measured with numbers, advocating for a strict dedication to quantifiable data.
He promoted the rigorous pursuit of quantitative data, statistical methods, and analytical techniques that mirrored the precision and objectivity found in the natural sciences, both in their execution and in the presentation of findings.
The Park–Ogburn contrast and its long-run influence
Park’s influence today
The robust legacy of Robert Park lives on in the continued academic and practical value placed on understanding the personal, emotional, and intricate social dimensions of life, alongside systematic explanations of social processes.
The field of sociology, particularly in sub-disciplines like urban sociology, ethnography, and qualitative methodology, continues to learn profoundly from immersive, qualitative, and community-centered approaches, including participatory research and oral history, which emphasize context and lived experience.
Ogburn’s influence today
Ogburn's methodological emphasis on statistics, advanced data analysis, and scientific rigor remains absolutely central to contemporary sociology, especially with the exponential growth of "big data" analytics and the rise of highly quantitative social science.
The modern demand for data literacy and statistical proficiency across various sectors, including business, government, and public policy, directly echoes Ogburn’s pioneering vision of sociology as a field deeply engaged with large-scale empirical evidence and precise measurement.
Check Your Understanding (conceptual)
Question: William Ogburn would most likely agree with which statement?
A. Sociology should focus on matters related to urban life.
B. The best kinds of studies would focus on small communities.
C. Science is an important part of the field of sociology. ← Correct
D. Qualitative research is just as valuable as quantitative research.
Question: A sociology department led by a disciple of Robert Park would view their surrounding city as
A. a laboratory where research could be conducted. ← Correct
B. a place where scientists should observe, but not interfere.
C. populated by antagonists, believing they would try to prevent sociologists from conducting research.
D. largely irrelevant to their field of study.
Metaphors, examples, and scenarios highlighted by the transcript
Park’s enduring metaphor of fieldwork: the act of physically going into neighborhoods, engaging directly with people through conversation and observation, and accumulating firsthand evidence to provide a solid foundation for sociological theories.
The city as a laboratory: this powerful concept posits that urban life, with its complex social dynamics and diverse populations, can be systematically studied with the same empirical seriousness and methodological rigor as a natural science experiment, employing systematic observation and in-depth interviews.
Connections to foundational principles and real-world relevance
Foundational principles
Empiricism and evidence-based research: both Park and Ogburn, despite their differences, championed the fundamental principle of grounding sociological theories in observable data rather than abstract speculation.
The scientific ideal: both contributed to the broader ideal of sociology as a science, committed to the use of systematic, transparent, and rigorous methodologies to produce reliable and valid knowledge.
Real-world relevance
The contemporary increase in data collection across all societal domains (e.g., big data, social media analytics) significantly amplifies the importance and application of quantitative approaches, directly reflecting Ogburn’s legacy.
Modern social research frequently exemplifies a powerful synthesis, blending Park’s deep-seated commitment to understanding real-life contexts and subjective experiences with Ogburn’s rigorous emphasis on objective measurement, giving rise to robust mixed-methods approaches that provide comprehensive insights.
Ethical, philosophical, and practical implications
Ethical considerations
Park’s approach inherently emphasizes direct engagement with communities and often carries a strong undercurrent of social betterment, suggesting that research has a vital public-facing and practical dimension, serving societal needs.
Ogburn’s stance foregrounds the pure discovery of knowledge, which can raise important questions about the appropriate balance between scientific objectivity, immediate policy relevance, and broader social impact.
Philosophical implications
The ongoing debate between understanding the social world through rich lived experience and narrative accounts versus abstract, objective measurement reflects enduring tensions within the philosophy of science itself, as well as core disagreements about the ultimate aims and nature of sociological inquiry.
Practical implications for researchers
Sociologists continually face the practical challenge of deciding when and how to prioritize deep field immersion and qualitative insight (per Park) versus large-scale measurement and quantitative rigor (per Ogburn).
The strategic integration of both approaches, often through carefully designed mixed-methods studies, can significantly enhance both the reliability, validity, and real-world relevance of sociological findings, providing a more complete picture of social phenomena.
Images and captions (contextual note)
Left: William Ogburn (1886–1959). Right: Robert Park (1864–1944). These visual anchors serve to ground the discussion.
The captions serve to visually symbolize the two distinct yet enduring visions that shaped, and continue to influence, the discipline of sociology.
Quick reference: key dates and terms (for quick study)
Park: 1864\text{--}1944; primary emphasis on fieldwork, qualitative understanding, and the city-life as a central focal subject and laboratory.
Ogburn: 1886\text{--}1959; strong emphasis on sociology becoming a rigorous science and on the central role of quantification and objective measurement.
1920s: the pivotal decade characterized by the empirical turn in U.S. sociology, especially prominent and influential at the University of Chicago.
1930: the year of Ogburn’s highly significant presidential address, in which he forcefully advocated for sociology's transformation into a purely scientific endeavor.
Check Your Understanding items: derived directly from the provided transcript, with the indicated correct options highlighted.
Summary takeaway
Robert Park and William Ogburn offered not only rival but ultimately complementary visions for the emergent field of sociology. Park foregrounded the crucial role of fieldwork, deep dives into lived experience, and the comprehensive study of city life as a natural laboratory. Ogburn, conversely, foregrounded the paramount importance of the scientific method, precise measurement, and rigorous quantitative analysis. The inherent tensions and subsequent collaborations between their approaches were instrumental in shaping the modern discipline’s evolving balance between qualitative depth and quantitative breadth—a dynamic balance that remains central to sociological method and inquiry today.
Ethnography
Definition and scope: A widely used qualitative method involving firsthand studies of people using observations, interviews, or both.
Investigator may socialize, work, or live with members of a group, organization, or community.
In participant observation, researchers actively participate in the activities studied; others may observe from a distance and not participate directly.
An ethnographer cannot simply be present; she must explain and justify her presence to members and gain sustained cooperation from the community to achieve worthwhile results.
Strengths (from Table 2.1):
Usually generates richer and more in-depth information than other methods.
Can provide a broader understanding of social processes.
Limitations (from Table 2.1):
Can study only relatively small groups or communities.
Findings might apply only to the groups studied; not easy to generalize from a single fieldwork study.
Perspectives on fieldwork and reflexivity:
Traditional ethnography often presented field accounts as objective reports with little information about the observer.
More recent ethnographies discuss how the researcher’s race, class, gender, and power differences affect the work and the dialogue with those studied (reflexivity).
Practical fieldwork considerations:
Requires skill in gaining confidence of participants.
Fieldworkers may experience loneliness and frustration, particularly when group members refuse to talk frankly.
Some fieldwork can be physically dangerous (e.g., studying gangs) or entail conflicts(
e.g., researcher could be mistaken for a police informer).
A recent example of ethnography:
Forrest Stuart’s five years of fieldwork in Skid Row (Los Angeles), documenting how the criminal justice system affects lives of people experiencing homelessness.
Concept of “therapeutic policing”: police use threats of arrest to coax the urban poor off the streets into welfare services, while acknowledging costs (families separated, loss of possessions).
Implications for policy: emphasize housing solutions and social supports rather than policing or welfare narrowly.
Ethnography takeaway:
Successful ethnography yields rich behavioral insights and helps illuminate how people understand their own actions within broader social processes.
However, generalizability remains a challenge; results are context-specific and depend on researcher-object dynamics.
Surveys
Purpose and scope:
Surveys enable efficient data collection from large numbers of individuals and allow precise comparisons among respondents.
They can generalize findings to a population when samples are representative.
Strengths (from Table 2.1):
Efficient data collection on large samples.
Allow for precise cross-group comparisons.
Limitations (from Table 2.1):
Responses may be superficial if questions are standardized; important viewpoint differences can be glossed over.
What respondents say they believe may differ from what they actually believe or do.
Types of questions in surveys:
Standardized/Fixed-choice questions: fixed set of responses (e.g., Yes/No/Don’t Know; Very Likely/Likely/Unlikely/Very Unlikely).
Advantage: easy to count and compare; simple categorization.
Disadvantage: may miss subtleties and context; potentially misleading if over-simplified.
Open-ended questions: allow respondents to express views in their own words; enable probing through follow-ups.
Advantage: richer, more nuanced data.
Disadvantage: harder to compare statistically; coding is required.
Survey design considerations:
Items should be clear and understandable to both interviewers and respondents; avoid jargon.
Questions usually follow a set order for consistency across large numbers of interviews.
Questionnaires should accommodate respondents’ characteristics (comprehension, sensitivity, relevance, willingness to participate).
Pilot studies: trial runs with a small sample to reveal issues before the main survey; helps iron out ambiguities, wording problems, and logistical issues.
Population, sampling, and generalizability:
Population: the entire group of interest; a sample is drawn to represent the population.
Sampling aims for representativeness; representative samples can generalize to the population.
Random sampling: every member of the population has the same probability of selection; often used in large population surveys.
The most sophisticated random-sampling method: assigning numbers to population members and using a computer to generate a random list (e.g., selecting every nth number).
For qualitative researchers focusing on a specific population (e.g., street vendors, gang members), random sampling is often inappropriate.
Practicalities of surveys:
Large-scale surveys may be conducted by government agencies or research organizations; interviews can be in person, by phone, by mail, or online.
Population-based surveys aim to infer patterns for the entire population rather than for a small sub-sample.
Representative samples and generalizability:
If properly selected, a sample of a few thousand respondents can reasonably reflect attitudes of the entire population (e.g., 2{,}000–3{,}000 voters can indicate broader voting intentions).
Fieldwork: Strengths and Limitations
Fieldwork advantages:
Provides direct, contextual insights into social life and processes.
Enables researchers to observe behaviors and social interactions in natural settings.
Fieldwork limitations:
Typically limited to small groups or communities.
Outcomes depend heavily on the researcher’s skill in gaining trust and cooperation.
Observer effects and researcher bias can distort findings; researchers may become too close to the group to maintain objectivity.
Generalizability across contexts is limited; results may not translate to other settings.
Notable example:
Forrest Stuart’s Skid Row fieldwork demonstrates how surveillance, policing, and welfare systems interact with homelessness, illustrating how research can inform policy on housing and social services rather than policing alone.
Sampling and Measurement
Random sampling and representativeness:
Random sampling assigns equal probability of inclusion to all members of the population, increasing representativeness.
In large population surveys, random sampling is common and often essential for generalizability.
In qualitative research targeting a specific group, random sampling is usually inappropriate because the focus is on depth rather than breadth.
Population vs. sample:
Population: the entire group of interest.
Sample: a subset drawn to represent the population.
Practical sampling notes:
For political attitudes or other large-scale research, properly designed samples can yield reliable generalizations about the population.
Statistical Terms and Central Tendency
Measures of central tendency:
Mean: the average of a set of values; sensitive to extreme values/outliers.
Mode: the most frequently occurring value; may not reflect the overall distribution.
Median: the middle value; robust to extreme outliers.
Illustration data set (13 values representing wealth):
Wealth values (in dollars): igl\{0,
5000,
10000,
20000,
40000,
40000,
40000,
80000,
100000,
150000,
200000,
400000,
10000000\bigr}
Summary statistics from the example:
N = 13; Sum = 11{,}085{,}000.
Mean: ar{x} = rac{11{,}085{,}000}{13} = 852{,}692.31
Mode: 40{,}000
Median: 40{,}000
Note on even vs odd samples: if there were an even number of observations, the median would be the mean of the two middle cases.
The mean is sensitive to extreme values (e.g., the $10{,}000{,}000$ observation greatly inflates the average for this set).
The mode and median do not reflect the full range of the data; they may be misleading if the distribution is highly skewed.
Standard deviation:
Purpose: a measure of dispersion around the mean.
Formula:s = \sqrt{\frac{1}{n-1} \sum{i=1}^{n} (xi - \bar{x})^2}
Correlation coefficients:
Express the strength and direction of the relationship between two or more variables.
Idealized values:
Perfect positive correlation: r = 1.0
No relationship: r = 0.0
Perfect negative correlation: r = -1.0
In the social sciences, perfect correlations are rarely found; correlations of magnitude around |r| \ge 0.6 are often considered strong.
Central tendency in practice:
Researchers may report more than one measure (mean, median, mode) to avoid a deceptive picture when distributions are skewed or contain outliers.
Related interpretation notes:
The mean reflects the entire distribution, but can be distorted by extreme values (outliers).
The mode may emphasize a common value but not the spread.
The median provides a robust central value when distributions are skewed.
Quick historical note:
Barry Wellman (1994) argues that while some studies suggest Americans are lonelier due to fewer confidants, overall social ties may be increasing due to the Internet, complicating simple loneliness interpretations.
Experimental Methods
Core idea:
Experiments attempt to determine causality by manipulating a specific variable and observing its effect on an outcome, while controlling other factors.
Typical design:
Random assignment to two groups: an experimental group that receives some treatment and a control group that does not.
Participants are often unaware of which group they belong to or the purpose of the study (though this is not universal).
Classic example:
Philip Zimbardo’s 1971 Stanford Prison Experiment: a make-believe jail where students were randomly assigned to guard or prisoner roles.
Findings: guards quickly adopted authoritarian behaviors; prisoners showed apathy and rebelliousness; study halted early due to extreme tension.
Conclusion: behavior in prisons is influenced significantly by the organizational setting, not solely by individual characteristics.
Advantages:
High degree of control over variables allows testing of hypotheses under specified conditions.
Reproducibility: experiments are often easier to replicate.
Disadvantages and limitations:
Artificiality of laboratory settings can limit generalizability to the real world.
Participants may behave unnaturally because they know they are being studied.
Ethical concerns and practical constraints may limit the scope of experiments.
Field experiments:
Real-life situations simulated as accurately as possible to enhance external validity.
Contemporary experimental approaches:
Web-based experiments have become prominent.
Example: Rivera and Tilcsik (2019) studied whether professor gender bias in student evaluations depended on the rating scale used.
Design: random sample of top-100 programs; all read the same lecture transcript; instructor listed as either a woman or a man; evaluation on either a 6-point or a 10-point scale.
Findings: bias against women emerged on the 10-point scale but not on the 6-point scale, suggesting that rating scale design can influence perceived bias.
Implications: rating scales can affect real-world evaluations and policy decisions.
Comparative Historical Research
Purpose:
Document how social behavior varies across time, place, and social group membership; often involves quantitative comparison with a consistent metric.
Illustration: divorce rates across countries and time.
U.S. divorce rate rose after World War II, peaked around 1979, and declined to about 2.3 marriages per 1,000 in 2020 (with only 45 states reporting).
Cross-national comparisons show the U.S. rate is higher than many Western countries, yet the overall trend patterns are similar.
Theda Skocpol’s States and Social Revolutions (1979): classic historical-comparative study explaining revolutions through underlying social structural conditions, not just the strength of social movements.
Three revolutions analyzed: 1789 France, 1917 Russia, 1949 China.
Key claims:
State structures are as (or more) important than class relations or revolutionary movements in explaining revolutions.
State structures are heavily influenced by international events (e.g., lost wars can undermine state authority and trigger revolutions).
Unintended consequences: revolutions often emerge without the precise anticipation of political groups.
Contemporary variation in comparative historical sociology:
Andreas Wimmer’s approach uses formal modeling and large original datasets to study wars across hundreds of cases, including Haiti, Latin America, Europe, and Africa.
Purpose: move beyond the traditional focus on Europe and famous revolutions to a broader, more representative sample of cases.
Example: Waves of War (2012) investigates war as a sociological phenomenon using large-scale data.
Critique of older historical sociology: tendency to focus on Europe and select famous cases; Wimmer broadens empirical scope to include non-European contexts for theory development.
Concept Checks and Practice (Study Aids)
Concept check questions (examples from the module):
What are the main advantages and limitations of ethnography as a research method?
Contrast the two types of questions commonly used in surveys (standardized vs open-ended).
What is a random sample, and why is it important for representativeness?
Discuss the main strengths of experiments.
Practice items (illustrative questions from the Check Your Understanding sections):
Question: Why are ethnographies considered a qualitative research method? Answer choices include: A) time-series variation, B) random sampling, C) firsthand studies using observations and interviews, D) testing hypotheses in controlled conditions.
Question: Which of the following is a method commonly used in quantitative research? Answer: A) Surveys (B. Fieldwork, C. Ethnography, D. Participant observation).
Note: These items appear in the module to test comprehension of core concepts; reflect on definitions, strengths, weaknesses, and methodological distinctions.
Real-World Relevance and Implications
Reflexivity and ethics:
Modern ethnography emphasizes researchers’ reflexivity about their own position and potential biases, including how race, class, gender, and power influence the research process and dialogue with participants.
Policy implications from ethnography and fieldwork:
Ethnographic insights into everyday life can illuminate how institutions (policing, welfare, housing) actually impact people’s lives, guiding more humane and effective policy design.
Method selection and research goals:
Ethnography is best for deep, contextual understanding of small groups;
Surveys are best for breadth and generalizability but may sacrifice depth;
Experiments are best for causal inference under controlled conditions but may sacrifice ecological validity.
Theoretical integration across methods:
Comparative historical research complements quantitative cross-sectional work by situating findings within historical trajectories and structural forces; mixed-methods approaches can enrich theories about social change and conflict.
Key Formulas and Numerical Highlights
Mean (example wealth set):
ar{x} = rac{ ext{Sum of values}}{n} = rac{11{,}085{,}000}{13} = 852{,}692.31
Range (illustrative):
From 0 to 10{,}000{,}000 in the wealth example; range = 10{,}000{,}000 - 0 = 10{,}000{,}000
Standard deviation (definition):
s = \sqrt{\frac{1}{n-1} \sum{i=1}^{n} (xi - \bar{x})^2}
Correlation coefficients:
Perfect positive: r = 1.0, Perfect negative: r = -1.0, No relation: r = 0.0, Strong relationship commonly considered at |r| ≥ 0.6.
Connections to Foundational Principles
Triangulation across methods can strengthen inferences about social processes by combining depth (ethnography) with breadth (surveys) and causal testing (experiments).
Reflexivity and ethics are essential in social research, especially in fieldwork, to avoid distortions arising from the researcher’s own positionality.
Historical and comparative approaches complement cross-sectional analyses by revealing how institutions, norms, and state structures shape social outcomes over time and across contexts.
Practical Tips for Exam Preparation
Be able to describe ethnography’s strengths, weaknesses, and reflexive practices; recognize when fieldwork is appropriate for studying a social process.
Compare standardized vs open-ended survey questions, including their implications for data analysis and interpretation.
Understand the difference between random sampling and purposive sampling; know why random sampling supports generalizability in quantitative research.
Recall key statistical concepts: mean, median, mode; when each is appropriate; how outliers affect the mean; basic ideas about standard deviation and correlation coefficients.
Remember classic experiments (e.g., Zimbardo) and their implications for understanding how context shapes behavior.
Be able to summarize Skocpol’s and Wimmer’s approaches to comparative historical research and their contributions to understanding social revolutions and wars.
Table Reference (Table 2.1 – Main Methods in Sociological Research)
Ethnography: Strengths – richer, in-depth information; broader understanding of social processes. Limitations – small groups, limited generalizability.
Surveys: Strengths – efficient data collection on large numbers; precise comparisons. Limitations – superficial data with fixed responses; may reflect professed beliefs rather than actual beliefs.
Experiments: Strengths – control over variables; easier replication. Limitations – laboratory artificiality; limited generalizability; potential observer effects.
Reading Tables in Sociological Literature
Tables are a compact, information-dense way to present data; do not skip them because they can speed up understanding and help assess the validity of conclusions.
Key steps to read a table:
Read the title in full: a good title explains the subject, that the table is for comparison, and that data are available for a limited number of countries.
Look for explanatory notes/footnotes:
Source notes indicate where data come from (e.g., Pew Research Center) and whether data are missing for some nations/years.
Footnotes may explain data collection methods or display choices.
If the researcher did not collect the data themselves, a source will be cited; this helps assess reliability.
In the example Table 2.2, notes show data are from a single international organization and may have gaps.
Read headings on the top (columns) and left-hand side (rows): these tell what type of information is in each row/column.
Identify the units used in the body (cases, percentages, averages, etc.). If units aren’t given, consider converting to a useful form (e.g., calculate percentages).
Consider the conclusions drawn by the author, and also ask what additional questions the data raise (e.g., causes of declines or sudden drops).
Table 2.2 (OPINION OF THE UNITED STATES: COMPARISON OF SELECTED NATIONS) demonstrates several important points:
Data show substantial variation in favorable views of the United States across countries.
There are national and regional patterns, but also notable variation within countries over time.
Example notes:
Data source: Pew Research Center, a large international survey organization.
Data not available for all nations for all years.
How to interpret the Table 2.2 data in practice:
Read the column headings to see the years and the metric (favorable vs. unfavorable opinions).
Read the row headings to see which countries are included.
If a country-year cell is missing, note the data gaps.
If needed, transform the numbers (e.g., compute percentages if not already given).
Use the table to compare cross-country trends and to generate new questions or hypotheses about opinion dynamics over time.
How tables contribute to methodological critique:
Tables allow readers to verify the writer’s conclusions by re-checking the data presentation, units, and any gaps or footnotes.
Context on data provenance:
The Pew Research Center is the data source for Table 2.2; understand that their sampling, timing, and methods shape the interpretation.
Reading guidance reminder:
When data are “not available” or incomplete, note how that may affect cross-country comparisons or trend analyses.
Practical takeaway:
Always start with the title and notes, then inspect headings and units, and finally draw or check conclusions and potential follow-up questions.
Can Sociology Identify Causes and Effects?
Sociology aims to identify causes and effects, but causal analysis in social contexts is tricky and increasingly questioned.
A central problem: distinguishing cause from effect in associations between social contexts and outcomes.
Example: Living in a low-income neighborhood is associated with higher unemployment or health issues (e.g., diabetes or obesity).
Core question: Is the neighborhood causing these outcomes, or do people with a higher likelihood of unemployment or ill health tend to reside in certain neighborhoods? Could both influence each other (reverse causation)?
Directionality problem and confounding factors:
It is possible that unobserved variables drive both neighborhood selection and outcomes.
The same individuals may select into neighborhoods based on characteristics that also influence outcomes.
There is a crisis of confidence about the ease of establishing clear causal claims in sociology, more so than might have been believed in the past.
Key takeaway:
Identifying causes and effects in social life requires careful consideration of direction, confounding variables, and potential selection biases.
Ethical Dilemmas and Exploitation in Social Research
All research involving human beings raises ethical questions about risk versus everyday life risk for subjects.
Example: Ethnographers studying in areas with high crime risk may expose subjects to risk through publication or researchers themselves to arrest for observation/participation.
Exploitation concerns:
A central question is whether social scientists profit from their subjects at those subjects’ expense.
Many studies do not address exploitation explicitly; some researchers may not reflect on this risk.
Exploitation tends to be more salient in qualitative fieldwork than in quantitative research, but it remains relevant whenever the careers of participants are tied to the research outcomes.
Practical ethical questions:
If a scholar earns money from a book based on cooperation with research subjects, should that money be shared with those subjects?
Broader ethical emphasis:
Researchers must consider not only consent and confidentiality but also fair treatment, equitable benefit, and the rights of communities affected by the research.
Community-Based Participatory Research (CBPR)
Definition and purpose:
CBPR is a collaborative research approach that equitably involves both researchers and research subjects in the process and recognizes each group’s distinctive strengths.
Core ethos:
The call “nothing about us, without us” emphasizes that voices of those being studied must be heeded.
Participants weigh in on the research question, design, and implementation.
Practical implication:
The people and communities affected by research have a right to influence what is researched and how it is conducted (Wilson et al., 2018).
Why CBPR matters in ethics and methodology:
It helps address exploitation concerns by ensuring community benefit and involvement, promotes relevance and validity of research questions, and enhances ethical accountability.
Can We Really Study Human Social Life in a Scientific Way?
What science means in this context:
Science is the use of systematic methods of empirical investigation, the analysis of data, theoretical thinking, and the logical assessment of arguments to develop a body of knowledge about a subject.
By this definition, sociology is a scientific endeavor.
Sociology vs. natural science:
Sociology is not equivalent to a natural science because humans are self-aware and assign meaning and purpose to actions.
Describing social life requires understanding the concepts and meanings that people themselves use.
Example illustrating interpretive need:
Describing death as suicide requires understanding the person’s intent; suicide is only present if there is deliberation of self-destruction.
Advantage of studying humans:
Researchers can pose questions directly to other people, gaining access to subjective meanings and interpretations.
Key difficulties unique to social life:
People who know they are being studied may not behave normally (Hawthorne effect).
Respondents may try to please the researcher (social desirability bias) or provide responses they think are expected.
Concept Checks (Reflection Prompts)
How are the ethical dilemmas that social scientists face different from those that other researchers encounter in the physical or biological sciences?
Answer in the text emphasizes human subjects, social context, and the potential for impact on people’s lives beyond purely physical risks.
Why should sociologists be concerned about the exploitation of the people they study?
Because research can affect participants’ careers, well-being, and communities; exploitation concerns arise when benefits accrue to researchers at others’ expense, or when communities do not share in the benefits of the knowledge produced.
Check Your Understanding (Practice Questions)
Question (7 of 8) — Community-based participatory research attempts to:
A. study a community without their knowledge.
B. study a community with participants playing a role in the research process and design.
C. create a firm hierarchy between subjects and those conducting the research.
D. create an economic incentive for researchers.
Answer indicated by the source: B
Question (8 of 8) — How does this book define science?
A. The use of systematic methods of empirical investigation, combined with theoretical approaches and theories
B. The study of human behavior in contexts of small-scale face-to-face interaction
C. The factual inquiries carried out in any area of sociological study
D. The questions that sociologists pose when looking at the origins and path of development of social institutions
Answer indicated by the source: A
Table 2.2: OPINION OF THE UNITED STATES: COMPARISON OF SELECTED NATIONS (Pew Research Center data)
Title and scope:
Provides a cross-national view of favorable opinions toward the United States, with data across multiple years for several countries.
Note that data are not available for all nations for all years.
Data notes:
Source: Pew Research Center, 2022c.
Some cells are missing (denoted by “-”).
Observations highlighted in the text:
Large variation exists in favorable views of the United States across countries.
There are clear national/regional patterns, but also substantial within-country variation over time.
Example trend descriptions provided in the material include a downward trend in several countries (e.g., a decline after peaks, with some countries showing sharp drops).
Selected country data (illustrative values from the table in the transcript):
China: 34, 41, 47, 58, 44, 43, 40
Egypt: 21, 22, 27, 17, 20, 19, 16
France: 39, 42, 75, 73, 75, 69, 64
Germany: 30, 31, 64, 63, 62, 52, 53
Indonesia: 29, 37, 63, 59, 54, -, 61
Japan: 61, 50, 59, 66, 85, 72, 69
Jordan: 20, 19, 25, 21, 13, 12, 14
Kenya: 87, -, 90, 94, 83, -, 81
Mexico: 56, 47, 69, 56, 52, 56, 66
Pakistan: 15, 19, 16, 17, 12, 12, 11
Poland: 61, 68, 67, 74, 70, 69, 67
Russia: 41, 46, 44, 57, 56, 52, 51
South Korea: 58, 70, 78, 79, -, -, 78
Turkey: 9, 12, 14, 17, 10, 15, 21
United Kingdom: 51, 53, 69, 65, 61, 60, 58
United States: 80, 84, 88, 85, 79, 80, 81
Important caveat:
Data are not available for all nations for all years (hence some gaps).
Interpretive takeaway:
The table supports cross-national comparisons of attitudes toward the United States but must be read with awareness of data gaps and the different years collected per country.
Final note on sources:
Table data are drawn from Pew Research Center (2022c) and are accompanied by notes emphasizing data availability and methodological context.
Here is a list of vocabulary and their definitions from the notes:
Inference: The core goal of sociological research, aiming to generalize findings from specific observations to broader populations or similar phenomena.
Public and Reproducible Procedures: All research methods, including data collection and analysis, must be transparently documented and accessible to allow for re-tracing steps, scrutiny, and potential reproduction of results.
Uncertainty: A core feature of scientific conclusions, which are inherently probabilistic rather than absolute, requiring clear disclosure of all identified sources of uncertainty.
Reflexivity: The critical self-awareness in social science, especially qualitative research, acknowledging that the investigator is part of the social world being studied and that their position, biases, and power dynamics influence the research.
Hypotheses: Educated guesses or tentative statements about relationships between variables, derived from theory or observation, and formulated to be empirically testable.
Survey Research: A method typically using questionnaires to collect data from large populations, enabling the identification of patterns, attitudes, or prevalence of specific behaviors.
Interviews: In-depth, semi-structured, or structured conversations used to collect rich, detailed qualitative data from individuals about their experiences, perspectives, and motivations.
Observational Studies: Methods involving direct observation of social behavior in natural settings to understand social interactions and cultural practices organically.
Experiments: Controlled methods used to establish cause-and-effect relationships between variables by manipulating an independent variable and measuring its impact on a dependent variable.
Historical Analysis: The examination of past events and documents to understand social change over time.
Comparative Research: A method involving the comparison of findings from different societies or groups to identify similarities and differences in social phenomena.
Biases: Systematic errors or influences that can arise during research, particularly if certain populations or groups are unintentionally or deliberately omitted, leading to a distorted picture of social reality.
Mean: The average of a set of values, calculated by summing all values and dividing by the number of values.
Mode: The most frequently occurring value in a dataset.
Median: The middle value in a dataset when values are ordered from least to greatest.
Standard Deviation: A measure of the dispersion or spread of values around the mean.
Correlation Coefficients: Values that express the strength and direction of the relationship between two or more variables.
Ethnography: A widely used qualitative method involving firsthand studies of people, often through observations, interviews, or living with members of a group.
Random Sampling: A sampling method where every member of the population has the same probability of being selected, increasing the representativeness of the sample.
Hawthorne Effect: The phenomenon where people who know they are being studied may not behave normally, thereby affecting research outcomes.
Social Desirability Bias: The tendency of respondents to provide answers that they believe will be viewed favorably by the researcher or society, rather than their true beliefs or behaviors.
Community-Based Participatory Research (CBPR): A collaborative research approach that equitably involves researchers and research subjects, recognizing and leveraging each group’
Here is a list of vocabulary and their definitions from the notes:
Inference: The core goal of sociological research, aiming to generalize findings from specific observations to broader populations or similar phenomena.
Public and Reproducible Procedures: All research methods, including data collection and analysis, must be transparently documented and accessible to allow for re-tracing steps, scrutiny, and potential reproduction of results.
Uncertainty: A core feature of scientific conclusions, which are inherently probabilistic rather than absolute, requiring clear disclosure of all identified sources of uncertainty.
Reflexivity: The critical self-awareness in social science, especially qualitative research, acknowledging that the investigator is part of the social world being studied and that their position, biases, and power dynamics influence the research.
Hypotheses: Educated guesses or tentative statements about relationships between variables, derived from theory or observation, and formulated to be empirically testable.
Survey Research: A method typically using questionnaires to collect data from large populations, enabling the identification of patterns, attitudes, or prevalence of specific behaviors.
Interviews: In-depth, semi-structured, or structured conversations used to collect rich, detailed qualitative data from individuals about their experiences, perspectives, and motivations.
Observational Studies: Methods involving direct observation of social behavior in natural settings to understand social interactions and cultural practices organically.
Experiments: Controlled methods used to establish cause-and-effect relationships between variables by manipulating an independent variable and measuring its impact on a dependent variable.
Historical Analysis: The examination of past events and documents to understand social change over time.
Comparative Research: A method involving the comparison of findings from different societies or groups to identify similarities and differences in social phenomena.
Biases: Systematic errors or influences that can arise during research, particularly if certain populations or groups are unintentionally or deliberately omitted, leading to a distorted picture of social reality.
Mean: The average of a set of values, calculated by summing all values and dividing by the number of values.
Mode: The most frequently occurring value in a dataset.
Median: The middle value in a dataset when values are ordered from least to greatest.
Standard Deviation: A measure of the dispersion or spread of values around the mean.
Correlation Coefficients: Values that express the strength and direction of the relationship between two or more variables.
Ethnography: A widely used qualitative method involving firsthand studies of people, often through observations, interviews, or living with members of a group.
Random Sampling: A sampling method where every member of the population has the same probability of being selected, increasing the representativeness of the sample.
Hawthorne Effect: The phenomenon where people who know they are being studied may not behave normally, thereby affecting research outcomes.
Social Desirability Bias: The tendency of respondents to provide answers that they believe will be viewed favorably by the researcher or society, rather than their true beliefs or behaviors.
Community-Based Participatory Research (CBPR): A collaborative research approach that equitably involves researchers and research subjects, recognizing and leveraging each group’
Here is a list of vocabulary and their definitions from the notes:
Inference: The core goal of sociological research, aiming to generalize findings from specific observations to broader populations or similar phenomena.
Public and Reproducible Procedures: All research methods, including data collection and analysis, must be transparently documented and accessible to allow for re-tracing steps, scrutiny, and potential reproduction of results.
Uncertainty: A core feature of scientific conclusions, which are inherently probabilistic rather than absolute, requiring clear disclosure of all identified sources of uncertainty.
Reflexivity: The critical self-awareness in social science, especially qualitative research, acknowledging that the investigator is part of the social world being studied and that their position, biases, and power dynamics influence the research.
Hypotheses: Educated guesses or tentative statements about relationships between variables, derived from theory or observation, and formulated to be empirically testable.
Survey Research: A method typically using questionnaires to collect data from large populations, enabling the identification of patterns, attitudes, or prevalence of specific behaviors.
Interviews: In-depth, semi-structured, or structured conversations used to collect rich, detailed qualitative data from individuals about their experiences, perspectives, and motivations.
Observational Studies: Methods involving direct observation of social behavior in natural settings to understand social interactions and cultural practices organically.
Experiments: Controlled methods used to establish cause-and-effect relationships between variables by manipulating an independent variable and measuring its impact on a dependent variable.
Historical Analysis: The examination of past events and documents to understand social change over time.
Comparative Research: A method involving the comparison of findings from different societies or groups to identify similarities and differences in social phenomena.
Biases: Systematic errors or influences that can arise during research, particularly if certain populations or groups are unintentionally or deliberately omitted, leading to a distorted picture of social reality.
Mean: The average of a set of values, calculated by summing all values and dividing by the number of values.
Mode: The most frequently occurring value in a dataset.
Median: The middle value in a dataset when values are ordered from least to greatest.
Standard Deviation: A measure of the dispersion or spread of values around the mean.
Correlation Coefficients: Values that express the strength and direction of the relationship between two or more variables.
Ethnography: A widely used qualitative method involving firsthand studies of people, often through observations, interviews, or living with members of a group.
Random Sampling: A sampling method where every member of the population has the same probability of being selected, increasing the representativeness of the sample.
Hawthorne Effect: The phenomenon where people who know they are being studied may not behave normally, thereby affecting research outcomes.
Social Desirability Bias: The tendency of respondents to provide answers that they believe will be viewed favorably by the researcher or society, rather than their true beliefs or behaviors.
Community-Based Participatory Research (CBPR): A collaborative research approach that equitably involves researchers and research subjects, recognizing and leveraging each group’