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Naturalistic Inquiry
Naturalistic inquiry is an approach to understanding the social world in which the researcher observes, describes, and interprets the experiences and actions of specific people and groups in societal and cultural context. It is a research tradition that encompasses qualitative research methods originally developed in anthropology and sociology, including participant observation, direct observation, ethnographic methods, case studies, grounded theory, unobtrusive methods, and field research methods. Working in the places where people live and work, naturalistic researchers draw on observations, interviews, and other sources of descriptive data, as well as their own subjective experiences, to create rich, evocative descriptions and interpretations of social phenomena. Naturalistic inquiry designs are valuable for exploratory research, particularly when relevant theoretical frameworks are not available or when little is known about the people to be investigated. The characteristics, methods, indicators of quality, philosophical foundations, history, disadvantages, and advantages of naturalistic research designs are described below.
Characteristics of Naturalistic Research
Naturalistic inquiry involves the study of a single case, usually a self-identified group or community. Self-identified group members are conscious of boundaries that set them apart from others. When qualitative (naturalistic) researchers select a case for study, they do so because it is of interest in its own right. The aim is not to find a representative case from which to generalize findings to other, similar individuals or groups. It is to develop interpretations and local theories that afford deep insights into the human experience.
Naturalistic inquiry is conducted in the field, within communities, homes, schools, churches, hospitals, public agencies, businesses, and other settings. Naturalistic researchers spend large amounts of time interacting directly with participants. The researcher is the research instrument, engaging in daily activities and conversations with group members to understand their experiences and points of view. Within this tradition, language is considered a key source of insight into socially constructed worlds. Researchers record participants' words and actions in detail with minimal interpretation. Although focused on words, narratives, and discourse, naturalistic researchers learn through all of their senses. They collect data at the following experiential levels: cognitive, social, affective, physical, and political/ideological. This strategy adds depth and texture to the body of data qualitative researchers describe, analyze, and interpret.
Naturalistic researchers study research problems and questions that are initially stated broadly then gradually narrowed during the course of the study. In non-naturalistic, experimental research designs, terms are defined, research hypotheses stated, and procedures for data collection established in advance before the study begins. In contrast, qualitative research designs develop over time as researchers formulate new understandings and refine their research questions. Throughout the research process, naturalistic researchers modify their methodological strategies to obtain the kinds of data required to shed light on more focused or intriguing questions. One goal of naturalistic inquiry is to generate new questions that will lead to improved observations and interpretations, which will in turn foster the formulation of still better questions. The process is circular but ends when the researcher has created an account that seems to capture and make sense of all the data at hand.
Naturalistic Research Methods
General Process
When naturalistic researchers conduct field research, they typically go through the following common sequence of steps:
- Gaining access to and entering the field site
- Gathering data
- Ensuring accuracy and trustworthiness (verifying and cross-checking findings)
- Analyzing data (begins almost immediately and continues throughout the study)
- Formulating interpretations (also an ongoing process)
- Writing up findings
- Member checking (sharing conclusions and conferring with participants)
- Leaving the field site
Sampling
Naturalistic researchers employ purposive rather than representative or random sampling methods. Participants are selected based on the purpose of the study and the questions under investigation, which are refined as the study proceeds. This strategy might increase the possibility that unusual cases will be identified and included in the study. Purposive sampling supports the development of theories grounded in empirical data tied to specific local settings.
Analyzing and Interpreting Data
The first step in qualitative data analysis involves transforming experiences, conversations, and observations into text (data). When naturalistic researchers analyze data, they review field notes, interview transcripts, journals, summaries, and other documents looking for repeated patterns (words, phrases, actions, or events) that are salient by virtue of their frequency. In some instances, the researcher might use descriptive statistics to identify and represent these patterns.
Interpretation refers to making sense of what these patterns or themes might mean, developing explanations, and making connections between the data and relevant studies or theoretical frameworks. For example, reasoning by analogy, researchers might note parallels between athletic events and anthropological descriptions of ritual processes. Naturalistic researchers draw on their own understanding of social, psychological, and economic theory as they formulate accounts of their findings. They work inductively, from the ground up, and eventually develop location-specific theories or accounts based on analysis of primary data.
As a by-product of this process, new research questions emerge. Whereas traditional researchers establish hypotheses prior to the start of their studies, qualitative researchers formulate broad research questions or problem statements at the start, then reformulate or develop new questions as the study proceeds. The terms grounded theory, inductive analysis, and content analysis, although not synonymous, refer to this process of making sense of and interpreting data.
Evaluating Quality
The standards used to evaluate the adequacy of traditional, quantitative studies should not be used to assess naturalistic research projects. Quantitative and qualitative researchers work within distinct traditions that rest on different philosophical assumptions, employ different methods, and produce different products. Qualitative researchers argue among themselves about how best to evaluate naturalistic inquiry projects, and there is little consensus on whether it is possible or appropriate to establish common standards by which such studies might be judged. However, many characteristics are widely considered to be indicators of merit in the design of naturalistic inquiry projects.
Immersion
Good qualitative studies are time consuming. Researchers must become well acquainted with the field site and its inhabitants as well as the wider context within which the site is located. They also immerse themselves in the data analysis process, through which they read, review, and summarize their data.
Transparency and Rigor
When writing up qualitative research projects, researchers must put themselves in the text, describing how the work was conducted, how they interacted with participants, how and why they decided to proceed as they did, and noting how participants might have been affected by these interactions. Whether the focus is on interview transcripts, visual materials, or field research notes, the analytical process requires meticulous attention to detail and an inductive, bottom-up process of reasoning that should be made clear to the reader.
Reflexivity
Naturalistic inquirers do not seek to attain objectivity, but they must find ways to articulate and manage their subjective experiences. Evidence of one or more forms of reflexivity is expected in naturalistic inquiry projects. Positional reflexivity calls on researchers to attend to their personal experiences—past and present—and describe how their own personal characteristics (power, gender, ethnicity, and other intangibles) played a part in their interactions with and understandings of participants. Textual reflexivity involves skeptical, self-critical consideration of how authors (and the professional communities in which they work) employ language to construct their representations of the social world. A third form of reflexivity examines how participants and the researchers who study them create social order through practical, goal-oriented actions and discourse.
Comprehensiveness and Scope
The cultural anthropologist Clifford Geertz used the term thick description to convey the level of rich detail typical of qualitative, ethnographic descriptions. When writing qualitative research reports, researchers place the study site and findings as a whole within societal and cultural contexts. Effective reports also incorporate multiple perspectives, including perspectives of participants from all walks of life (for example) within a single community or organization.
Accuracy
Researchers are expected to describe the steps taken to verify findings and interpretations. Strategies for verification include triangulation (using and confirming congruence among multiple sources of information), member checking (negotiating conclusions with participants), and auditing (critical review of the research design, processes, and conclusions by an expert).
Claims and Warrants
In well-designed studies, naturalistic researchers ensure that their conclusions are supported by empirical evidence. Furthermore, they recognize that their conclusions follow logically from the design of the study, including the review of pertinent literature, data collection, analysis, interpretation, and the researcher's inferential process.
Attention to Ethics
Researchers should describe the steps taken to protect participants from harm and discuss any ethical issues that arose during the course of the study.
Fair Return
Naturalistic inquiry projects are time consuming not only for researchers but also for participants, who teach researchers about their ways of life and share their perspectives as interviewees. Researchers should describe what steps they took to compensate or provide fair return to participants for their help. Research leads to concrete benefits for researchers (degree completion or career advancement). Researchers must examine what benefits participants will gain as a result of the work and design their studies to ensure reciprocity (balanced rewards).
Coherence
Good studies call for well-written and compelling research reports. Standards for writing are genre specific. Postmodern authors defy tradition through experimentation and deliberate violations of writing conventions. For example, some authors avoid writing in clear, straightforward prose to express more accurately the complexities inherent in the social world and within the representational process.
Veracity
A good qualitative report brings the setting and its residents to life. Readers who have worked or lived in similar settings find the report credible because it reflects aspects of their own experiences.
Illumination
Good naturalistic studies go beyond mere description to offer new insights into social and psychological phenomena. Readers should learn something new and important about the social world and the people studied, and they might also gain a deeper understanding of their own ways of life.
Philosophical Foundations
Traditional scientific methods rest on philosophical assumptions associated with logical positivism. When working within this framework, researchers formulate hypotheses that are drawn from established theoretical frameworks, define variables by stipulating the processes used to measure them, collect data to test their hypotheses, and report their findings objectively. Objectivity is attained through separation of the researcher from participants and by dispassionate analysis and interpretation of results. In contrast, naturalistic researchers tap into their own subjective experiences as a source of data, seeking experiences that will afford them an intuitive understanding of social phenomena through empathy and subjectivity. Qualitative researchers use their subjective experiences as a source of data to be carefully described, analyzed, and shared with those who read their research reports.
For the naturalistic inquirer, objectivity and detachment are neither possible nor desirable. Human experiences are invariably influenced by the methods used to study them. The process of being studied affects all humans who become subjects of scientific attention. The presence of an observer affects those observed. Furthermore, the observer is changed through engaging with and observing the other. Objectivity is always a matter of degree.
Qualitative researchers are typically far less concerned about objectivity as this term is understood within traditional research approaches than with intersubjectivity. Intersubjectivity is the process by which humans share common experiences and subscribe to shared understandings of reality. Naturalistic researchers seek involvement and engagement rather than detachment and distance. They believe that humans are not rational beings and cannot be understood adequately through objective, disembodied analysis. Authors critically examine how their theoretical assumptions, personal histories, and methodological decisions might have influenced findings and interpretations (positional reflexivity). In a related vein, naturalistic researchers do not believe that political neutrality is possible or helpful. Within some qualitative research traditions, researchers collaborate with participants to bring about community-based political and economic change (social justice).
Qualitative researchers reject determinism, the idea that human behaviors are lawful and can be predicted. Traditional scientists try to discover relationships among variables that remain consistent across individuals beyond the experimental setting. Naturalistic inquiry rests on the belief that studying humans requires different methods than those used to study the material world. Advocates emphasize that no shared, universal reality remains constant over time and across cultural groups. The phenomena of most interest to naturalistic researchers are socially constructed, constantly changing, and multiple. Naturalistic researchers hold that all human phenomena occur within particular contexts and cannot be interpreted or understood apart from these contexts.
History
The principles that guide naturalistic research methods were developed in biology, anthropology, and sociology. Biologist Charles Darwin developed the natural history method, which employs detailed observation of the natural world directed by specific research questions and theory building based on analysis of patterns in the data, followed by confirmation (testing) with additional observations in the field. Qualitative researchers use similar strategies, which transform experiential, qualitative information gathered in the field into data amenable to systematic investigation, analysis, and theory development.
Ancient adventurers, writers, and missionaries wrote the first naturalistic accounts, describing the exotic people they encountered on their travels. During the early decades of the 20th century, cultural anthropologists and sociologists pioneered the use of ethnographic research methods for the scientific study of social phenomena. Ethnography is both a naturalistic research methodology and a written report that describes field study findings. Although there are many different ethnographic genres, all of them employ direct observation of naturally occurring events in the field. Early in the 20th century, University of Chicago sociologists used ethnographic methods to study urban life, producing pioneering studies of immigrants, crime, work, youth, and group relations. Sociologist Herbert Blumer, drawing on George Herbert Mead, William I. Thomas, and John Dewey, developed a rationale for the naturalistic study of the social world. In the 1970s, social scientists articulated ideas and theoretical issues pertinent to naturalistic inquiry. Interest in qualitative research methods grew. In the mid-1980s, Yvonne Lincoln and Egon Guba published Naturalistic Inquiry, which provided a detailed critique of positivism and examined implications for social research. Highlighting the features that set qualitative research apart from other methods, these authors also translated key concepts across what they thought were profoundly different paradigms (disciplinary worldviews). In recent years, qualitative researchers considered the implications of critical, feminist, postmodern, and poststructural theories for their enterprise. The recognition or rediscovery that researchers create the phenomena they study and that language plays an important part in this process has inspired methodological innovations and lively discussions. The discourse on naturalistic inquiry remains complex and ever changing. New issues and controversies emerge every year, reflecting philosophical debates within and across many academic fields.
Methodological Disadvantages and Advantages
Disadvantages
Many areas are not suited to naturalistic investigation. Naturalistic research designs cannot uncover cause and effect relationships and they cannot help researchers evaluate the effectiveness of specific medical treatments, school curricula, or parenting styles. They do not allow researchers to measure particular attributes (motivation, reading ability, or test anxiety) or to predict the outcomes of interventions with any degree of precision. Qualitative research permits only claims about the specific case under study. Generalizations beyond the research site are not appropriate. Furthermore, naturalistic researchers cannot set up logical conditions whereby they can demonstrate their own assumptions to be false.
Naturalistic inquiry is time consuming and difficult. Qualitative methods might seem to be easier to use than traditional experimental and survey methods because they do not require mastery of technical statistical and analytical methods. However, naturalistic inquiry is one of the most challenging research approaches to learn and employ. Qualitative researchers tailor methods to suit each project, revising data-collection strategies as questions and research foci emerge. Naturalistic researchers must have a high tolerance for uncertainty and the ability to work independently for extended periods of time, and these researchers must also be able to think creatively under pressure.
Advantages
Once controversial, naturalistic research methods are now used in social psychology, developmental psychology, qualitative sociology, and anthropology. Researchers in professional schools (education, nursing, health sciences, law, social work, and counseling) and applied fields (regional planning, library science, program evaluation, information science, and sports administration) employ naturalistic strategies to investigate social phenomena. Naturalistic approaches are well suited to the study of groups about which little is known. They are also holistic and comprehensive. Qualitative researchers try to tell the whole story, in context. A well-written report has some of the same characteristics as a good novel, bringing the lives of participants to life. Naturalistic methods help researchers understand how people view the world, what they value, and how these values and cognitive schemas are reflected in practices and social structures. Through the study of groups unlike their own, researchers learn that many different ways are available to raise children, teach, heal, maintain social order, and initiate change. Readers learn about the extraordinary variety of designs for living and adaptive strategies humans have created, thus broadening awareness of possibilities beyond conventional ways of life. Thus, naturalistic inquiry can provide insights that deepen our understanding of the human experience and generate new theoretical insights. For researchers, the process of performing qualitative research extends and intensifies the senses and provides interesting and gratifying experiences as relationships are formed with the participants from whom and with whom one learns.
Further Readings
- Descriptive Statistics
- Distributions
- Graphical Displays of Data
- Hypothesis Testing
- p Value
- Alternative Hypotheses
- Beta
- Critical Value
- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
- Null Hypothesis
- One-Tailed Test
- Power
- Power Analysis
- Significance Level, Concept of
- Significance Level, Interpretation and Construction
- Significance, Statistical
- Two-Tailed Test
- Type I Error
- Type II Error
- Type III Error
- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Statistical Power Analysis for the Behavioral Sciences
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Q-Statistic
- R2
- Association, Measures of
- Coefficient of Concordance
- Coefficient of Variation
- Coefficients of Correlation, Alienation, and Determination
- Confidence Intervals
- Margin of Error
- Nonparametric Statistics
- Odds Ratio
- Parameters
- Parametric Statistics
- Partial Correlation
- Pearson Product-Moment Correlation Coefficient
- Polychoric Correlation Coefficient
- Randomization Tests
- Regression Coefficient
- Semipartial Correlation Coefficient
- Spearman Rank Order Correlation
- Standard Error of Estimate
- Standard Error of the Mean
- Student's t Test
- Unbiased Estimator
- Weights
- Item Response Theory
- Mathematical Concepts
- Measurement Concepts
- Organizations
- Publishing
- Qualitative Research
- Reliability of Scores
- Research Design Concepts
- Aptitude-Treatment Interaction
- Cause and Effect
- Concomitant Variable
- Confounding
- Control Group
- Interaction
- Internet-Based Research Method
- Intervention
- Matching
- Natural Experiments
- Network Analysis
- Placebo
- Replication
- Research
- Research Design Principles
- Treatment(s)
- Triangulation
- Unit of Analysis
- Yoked Control Procedure
- Research Designs
- A Priori Monte Carlo Simulation
- Action Research
- Adaptive Designs in Clinical Trials
- Applied Research
- Behavior Analysis Design
- Block Design
- Case-Only Design
- Causal-Comparative Design
- Cohort Design
- Completely Randomized Design
- Cross-Sectional Design
- Crossover Design
- Double-Blind Procedure
- Ex Post Facto Study
- Experimental Design
- Factorial Design
- Field Study
- Group-Sequential Designs in Clinical Trials
- Laboratory Experiments
- Latin Square Design
- Longitudinal Design
- Meta-Analysis
- Mixed Methods Design
- Mixed Model Design
- Monte Carlo Simulation
- Nested Factor Design
- Nonexperimental Design
- Observational Research
- Panel Design
- Partially Randomized Preference Trial Design
- Pilot Study
- Pragmatic Study
- Pre-Experimental Designs
- Pretest–Posttest Design
- Prospective Study
- Quantitative Research
- Quasi-Experimental Design
- Randomized Block Design
- Repeated Measures Design
- Response Surface Design
- Retrospective Study
- Sequential Design
- Single-Blind Study
- Single-Subject Design
- Split-Plot Factorial Design
- Thought Experiments
- Time Studies
- Time-Lag Study
- Time-Series Study
- Triple-Blind Study
- True Experimental Design
- Wennberg Design
- Within-Subjects Design
- Zelen's Randomized Consent Design
- Research Ethics
- Research Process
- Clinical Significance
- Clinical Trial
- Cross-Validation
- Data Cleaning
- Delphi Technique
- Evidence-Based Decision Making
- Exploratory Data Analysis
- Follow-Up
- Inference: Deductive and Inductive
- Last Observation Carried Forward
- Planning Research
- Primary Data Source
- Protocol
- Q Methodology
- Research Hypothesis
- Research Question
- Scientific Method
- Secondary Data Source
- Standardization
- Statistical Control
- Type III Error
- Wave
- Research Validity Issues
- Bias
- Critical Thinking
- Ecological Validity
- Experimenter Expectancy Effect
- External Validity
- File Drawer Problem
- Hawthorne Effect
- Heisenberg Effect
- Internal Validity
- John Henry Effect
- Mortality
- Multiple Treatment Interference
- Multivalued Treatment Effects
- Nonclassical Experimenter Effects
- Order Effects
- Placebo Effect
- Pretest Sensitization
- Random Assignment
- Reactive Arrangements
- Regression to the Mean
- Selection
- Sequence Effects
- Threats to Validity
- Validity of Research Conclusions
- Volunteer Bias
- White Noise
- Sampling
- Cluster Sampling
- Convenience Sampling
- Demographics
- Error
- Exclusion Criteria
- Experience Sampling Method
- Nonprobability Sampling
- Population
- Probability Sampling
- Proportional Sampling
- Quota Sampling
- Random Sampling
- Random Selection
- Sample
- Sample Size
- Sample Size Planning
- Sampling
- Sampling and Retention of Underrepresented Groups
- Sampling Error
- Stratified Sampling
- Systematic Sampling
- Scaling
- Software Applications
- Statistical Assumptions
- Statistical Concepts
- Autocorrelation
- Biased Estimator
- Cohen's Kappa
- Collinearity
- Correlation
- Criterion Problem
- Critical Difference
- Data Mining
- Data Snooping
- Degrees of Freedom
- Directional Hypothesis
- Disturbance Terms
- Error Rates
- Expected Value
- Fixed-Effects Model
- Inclusion Criteria
- Influence Statistics
- Influential Data Points
- Intraclass Correlation
- Latent Variable
- Likelihood Ratio Statistic
- Loglinear Models
- Main Effects
- Markov Chains
- Method Variance
- Mixed- and Random-Effects Models
- Models
- Multilevel Modeling
- Odds
- Omega Squared
- Orthogonal Comparisons
- Outlier
- Overfitting
- Pooled Variance
- Precision
- Quality Effects Model
- Random-Effects Models
- Regression Artifacts
- Regression Discontinuity
- Residuals
- Restriction of Range
- Robust
- Root Mean Square Error
- Rosenthal Effect
- Serial Correlation
- Shrinkage
- Simple Main Effects
- Simpson's Paradox
- Sums of Squares
- Statistical Procedures
- Accuracy in Parameter Estimation
- Analysis of Covariance (ANCOVA)
- Analysis of Variance (ANOVA)
- Barycentric Discriminant Analysis
- Bivariate Regression
- Bonferroni Procedure
- Bootstrapping
- Canonical Correlation Analysis
- Categorical Data Analysis
- Confirmatory Factor Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
- Discriminant Analysis
- Dummy Coding
- Effect Coding
- Estimation
- Exploratory Factor Analysis
- Greenhouse–Geisser Correction
- Hierarchical Linear Modeling
- Holm's Sequential Bonferroni Procedure
- Jackknife
- Latent Growth Modeling
- Least Squares, Methods of
- Logistic Regression
- Mean Comparisons
- Missing Data, Imputation of
- Multiple Regression
- Multivariate Analysis of Variance (MANOVA)
- Pairwise Comparisons
- Path Analysis
- Post Hoc Analysis
- Post Hoc Comparisons
- Principal Components Analysis
- Propensity Score Analysis
- Sequential Analysis
- Stepwise Regression
- Structural Equation Modeling
- Survival Analysis
- Trend Analysis
- Yates's Correction
- Statistical Tests
- F Test
- t Test, Independent Samples
- t Test, One Sample
- t Test, Paired Samples
- z Test
- Bartlett's Test
- Behrens–Fisher t′ Statistic
- Chi-Square Test
- Duncan's Multiple Range Test
- Dunnett's Test
- Fisher's Least Significant Difference Test
- Friedman Test
- Honestly Significant Difference (HSD) Test
- Kolmogorov-Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar's Test
- Multiple Comparison Tests
- Newman–Keuls Test and Tukey Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey's Honestly Significant Difference (HSD)
- Welch's t Test
- Wilcoxon Rank Sum Test
- Theories, Laws, and Principles
- Bayes's Theorem
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Falsifiability
- Game Theory
- Gauss–Markov Theorem
- Generalizability Theory
- Grounded Theory
- Item Response Theory
- Occam's Razor
- Paradigm
- Positivism
- Probability, Laws of
- Theory
- Theory of Attitude Measurement
- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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