Research Methods: Intro & Descriptive Research

BMI trends in the United States (1990–2005)

  • Timeframe and changes
    • Fifteen-year period beginning in 19901990 and ending in 02/200502/2005.
    • In the slide notes, a new category for obesity was added in 19971997.
    • Additional category changes in 02/200302/2003 and 02/200502/2005.
    • Overall observation: obesity rates rise across the country, with some areas increasing faster than others, and the pattern spreading from the East to the West.
  • Observed patterns and questions
    • Pattern: BMI/obesity rates appear to go up across the country.
    • Variation by region: rates increase more quickly in some areas; early growth often observed in the East before spreading West.
    • Possible factor suggestions raised: food culture and dietary patterns.
  • Regional observations and hypotheses
    • Higher obesity rates reported in the South; hypothesized link to food culture (e.g., fried foods).
    • Curious about outliers: Colorado and Vermont show notably lower rates; questions about what explains these discrepancies.
    • Possible contributing factors proposed:
    • Healthy food availability and access
      • Existence of fewer food deserts in some regions
      • Density of fast-food restaurants versus availability of farmers' markets
    • Poverty levels and economic factors
  • Key questions to explore (from descriptive observations)
    • Are differences driven by local food environments (availability, affordability, variety)?
    • Do fast-food density and farmers market availability correlate with regional BMI differences?
    • Is poverty more prevalent in regions with faster-rising obesity rates?
  • Important methodological note about descriptive research (why collection and interpretation matter)
    • Descriptive observations can reveal patterns and generate new questions, but they cannot determine causation or explain why behaviors occur.
    • Purpose of descriptive research: detailed data collection across multiple sources to understand a complex issue/behavior, generate hypotheses, and yield insights not obtainable via other methods.
  • What a descriptive study collects and aims to achieve
    • Data collection methods include interviews, observations, tests, and archival records.
    • Goal: compile qualitative and quantitative data to understand complex phenomena and to generate hypotheses.
    • Example mentioned: a case study concept (e.g., an individual aged 114114 years) used to illustrate gathering comprehensive data to explore what contributes to extreme longevity.

Data collection methods in descriptive research: overview

  • Primary data collection methods discussed
    • Observation
    • Surveys
    • Tests
    • Archival records
  • Focus on observation and surveys in detail (as two core approaches)
  • Core issues to consider across data collection
    • Validity (external vs internal)
    • Ethics (awareness of being observed, presence effects)
    • Inter-rater reliability (consistency in interpreting observed behaviors)
    • Practicality and depth of data (time, cost, depth vs breadth)

Observation as a data collection method

  • Definition
    • Systematically observing and recording someone’s behavior.
  • Example to illustrate use
    • Studying how parents and adolescents discuss sensitive topics (e.g., curfew): what their conversations look like, how contentious they are, how they solve problems, body language, and whether insults occur.
    • Variables of interest: whether specific behaviors or stances are present or absent.
  • Key design considerations
    • Environment: naturalistic (e.g., living room) vs. lab (controlled environment)
    • Validity concerns
    • External validity: how well do the findings generalize to real-world settings?
    • Internal validity: how well does the design control for confounding factors?
    • Ethics
    • Whether participants know they are being observed (known observation) vs. hidden observation
    • Potential for the observer’s presence to alter behavior (reactivity)
    • Control vs realism trade-off
    • Controlled environments allow manipulation of factors (temperature, number of people, weather) but may reduce real-world representation.
    • Interpreting behavior and reliability
    • Inter-rater reliability: do multiple observers agree on what constitutes a given behavior (e.g., aggressive posture)?
  • Broad applications and examples
    • Observing children’s social interactions on a playground to understand group formation and conflict resolution.
    • Studying decision-making processes within cults via participant observation (researcher joins the group to observe dynamics from inside).
    • Controlled observation in a mock shopping environment to examine how environmental factors (e.g., higher temperature) influence decision-making.
  • General takeaway
    • Observation provides rich, detailed data but requires careful consideration of validity, ethics, and reliability to ensure findings are meaningful and not biased by the observer or setting.

Surveys as a data collection method

  • Advantages of surveys
    • Quick data collection: e.g., surveying many participants in a short time.
    • Cost-effective: relatively inexpensive compared to some observational or longitudinal approaches.
    • Ability to capture attitudes, beliefs, and self-reported behaviors (e.g., attitudes toward gun control, time spent on activities, or media consumption).
  • Example use cases
    • Asking people how many times they go out to get boba in an average week.
    • Asking about time spent watching violent vs. pro-social television programs.
  • Common problems and design challenges
    • Clarity of questions: need precise definitions to avoid ambiguous responses (e.g., the Zuckerman’s Sensation Seeking Scale item about "wild uninhibited parties").
    • Forced-choice limitations: many surveys offer only two or a few options, which may not capture nuance (context-dependent answers).
    • Definitions and time frames matter: e.g.,
    • "Have you taken any drugs in the past week?" depends on what is meant by drugs (illicit drugs vs. any medication like Advil).
    • Time frame interpretation: does "past month" mean the last 30 days, or the calendar month?
    • Memory recall: respondents may not accurately remember past behaviors (e.g., what was eaten yesterday or last week).
    • Response options and interpretation
    • People may interpret response categories differently (e.g., what counts as "four or more" cigarettes may vary by person).
    • Double-barreled questions
    • Example: "Do you think the new stop sign and stoplight will reduce traffic accidents?" — answers may vary if the respondent agrees only with one part.
    • Complexity and nuance
    • Real-world behavior often does not fit neatly into predefined categories; context can influence responses.
  • A specific example highlighted
    • Zuckerman’s Sensation Seeking Scale as a question set used to illustrate potential survey problems (ambiguity, forced choices, interpretation issues).
  • Practical guidance for researchers
    • When reading about descriptive data, consider creating a pros/cons chart for each method (case study, observation, surveys) to weigh strengths and limitations.
    • The textbook promises deeper exploration of these methods to help fill out such a chart.

Connections, implications, and takeaways

  • Foundational research principles connected to the content
    • Descriptive vs inferential aims: descriptive methods reveal patterns; they do not establish causation.
    • Validity and reliability challenges across methods: internal validity (control of confounds) and external validity (generalizability); inter-rater reliability ensures consistent interpretation of observed behaviors.
    • Ethics in research: awareness of observation, consent, and potential behavioral changes due to being observed.
  • Real-world relevance
    • Understanding obesity patterns can inform public health policy, resource allocation, and interventions targeting food environments (availability, affordability, and accessibility).
    • The discussion of observation and surveys highlights how researchers collect evidence to form hypotheses about why patterns exist, and where to intervene.
  • Practical implications for exams or future work
    • Be able to identify what descriptive observations can and cannot tell you.
    • Be able to list pros and cons of case studies, observational methods, and surveys.
    • Recognize key terms: external validity, internal validity, inter-rater reliability, known vs hidden observation, double-barreled questions, forced-choice questions, memory recall issues.