Chapter 3

How We Study Families

Personal Inquiry vs Experiential Reality

  • Personal Inquiry

    • Related to your own personal experiences and seeking out new knowledge at the individual level

    • Examples discussed: taking a relationship quiz to see if this qualifies as research, observing how others parent their children, watching how family/friends/partners do things.

  • Experiential Reality

    • Your own experiences are important to you and those who care about you

    • Personal experiences are limited

    • Personal experience provides a way to “know” family, but it may limit studying/ thinking about family experiences.

    • Science provides norms for transcending the blinders of personal experience.

WHAT IS TRUE ABOUT YOUR FAMILY THAT YOU BELIEVE (ASSUME) IS TRUE ABOUT MOST FAMILIES

  • Acknowledges assumptions about families beyond one's personal experience

THE BLINDERS OF PERSONAL EXPERIENCE

  • Central aim of scientific investigation: find out what is actually going on, not what we assume is happening

  • Personal experiences create assumptions (e.g., assuming families may not be violent because your family wasn’t violent)

  • Science: a logical system that bases knowledge on systematic observation and empirical evidence – facts we can verify

SPECIFIC PROBLEMS WITH PERSONAL INQUIRY

  • Overgeneralization

  • Confirmation Bias

  • Agreement Reality (echo chamber)

SPECIFIC PROBLEMS WITH PERSONAL INQUIRY (DETAILED)

  • Sample Size: N of 1 does not imply the same experiences apply to others

  • Sampling bias: data collection methods bias findings and limit generalizability to the larger population

  • One person’s beliefs/behavior/psychology do not always reflect the larger group

  • Increasing sample size improves representativeness:

    • Asking 100 people is better, 500 is even better

  • Population: the target group for generalizations

  • With one person, the population cannot be defined

  • Data are collected from people within that population to make generalizations about the larger population

  • Examples of populations: college students, adults in the United States, survivors of child abuse

SCIENTIFIC INQUIRY: TRANSCENDING PERSONAL EXPERIENCE

  • The Blinders of Personal Experience must be overcome by scientific inquiry

  • Research seeks to make generalizations about GROUPS of people, not individuals within the group

  • For most people, most of the time

  • We cannot assume other people think or believe as we do

PRINCIPLES OF SCIENTIFIC INQUIRY

  • What is the rule that specifies how this group of individuals is named? (Illustrative names: Arthur, Alfred, Ann, )

  • Falsifiability – Are the following statements falsifiable?

    • All Swans are white.

    • There is an undetectable purple unicorn somewhere in the universe.

    • All couples who have frequent sex will have high relationship satisfaction.

    • Newton’s Law of Universal Gravitation.

  • Empirical Inquiry – Research focused on the collection and interpretation of information through systematic means that reduce bias and increase the generalizability of the knowledge learned

  • Systematic – a set of rules or procedures followed to gather data

  • Reduce Bias – Methods (e.g., randomization, control procedures, account for correlation, etc.) reduce the impact of biases on conclusions

  • Increase generalizability – Expand the scope of who the findings apply to, from individuals to groups, and define which groups are being generalized to

REDUCTIONISM VS. SYSTEMIC ANALYSIS

  • Reductionism: reducing something complex to its smallest measurable component

    • Example: The relationship between meditation and anxiety in an individual; reductionism can be useful

  • Systemic Analysis: analyzing a system with its parts together rather than in isolation

    • Example: Mental health disparities for queer youth in school; systemic analysis can be useful

PRINCIPLES OF SCIENTIFIC INQUIRY (CONT.)

  • Empirical Inquiry

  • Systematic methods

  • Reducing bias

  • Increasing generalizability

RESEARCH METHODS

  • We need to systematically examine various family processes

  • Personal observation isn’t systematic in nature

  • Systematic: being intentional and explicit about what you are measuring and how you’re measuring it

  • Research provides a framework to study individuals and families

  • Helps identify what works for MOST families MOST of the time

  • Multistage process: idea, theory, sampling, measures, and analysis

RESEARCH QUESTIONS

  • A good research question (aka research goals) is the cornerstone of a good study of family life.

  • A great methodology cannot compensate for a poor research question

  • Infinite possible questions, but not all are worth pursuing

  • Example considerations: Do families with twins spend more time eating together than families with triplets?

  • A GREAT study can involve: asking parents about eating, observing them eating together, varying meals, including infants to teens, and comparing groups.

RESEARCH QUESTIONS (CRITERIA)

  • Important: Must address an issue of great significance

  • Feasible: Must be doable given resource constraints

  • Meaningful: Must have important implications in theory, research, practice, or policy

VARIETY OF METHODS USED TO UNDERSTAND FAMILIES

  • Research goals/research questions drive the study

  • Example: Effects of COVID on college student GPA

  • Research Strategies / Research Methods: the overall plan to ask the question

  • Connecting research questions and research methods

  • A great methodology cannot compensate for a poor question

  • Research Tactics: Specific procedures of data collection or problem-solving (methods)

  • Statistical Analysis: test whether the research questions were answered

SAMPLING

  • To overcome the limitations of personal inquiry, recruit (sample) people to generalize findings

  • Intentionally collect data from individuals, couples, families, etc.

  • Data collection methods: surveys, focus groups, interviews

  • Each strategy has strengths and limitations

  • Aim for a sample that matches the population it represents

  • Possible populations: college students, adults in the United States, pregnant women, racial groups, etc.

SAMPLING (DETAILED)

  • Sample: the people actually involved in the research; a small percentage of the population

  • If sample characteristics don’t match the target population, generalizability suffers.

  • Recruitment can introduce bias.

  • Examples of sampling differences: high school teenagers vs newly engaged couples vs recently divorced

ILLUSTRATIVE CONCEPTS: POPULATION VS. SAMPLE

  • ENTIRE POPULATION OF COUPLES (illustration) vs

  • SAMPLE OF COUPLES

  • Unmeasured Population

  • These figures illustrate how a sample is drawn from a larger population and the potential bias if the sample isn’t representative

OVERGENERALIZATION

  • Caution against assuming findings from a small sample apply to all groups

ICE CREAM AND FAMILY VIOLENCE (ILLUSTRATIVE CORRELATION)

  • Odd research finding: ice cream consumption is positively correlated with violence

  • Does ice cream cause violence? No

  • Common third variable: weather (hot temperatures) affects both:

    • When it’s hot, people eat more ice cream

    • When it’s hot, people are more irritable and impulsive

CORRELATIONS VS CAUSATION

  • Correlation: tendency for two variables to co-occur

  • Positive correlation: both tend to increase together

  • Negative correlation: as one increases, the other tends to decrease

  • Range: correlations can range from −1extto1-1 ext{ to } 1

  • Causation: one variable causes a change in another; e.g., taking medication to reduce depressive symptoms (example explanation)

CAUSAL MODELS

  • Simple causal model: Childhood Abuse → Depression

  • Mediated causal model: Childhood Abuse → Insecure Attachment → Depression

    • Interpretation: Adults abused in childhood report higher depression in adulthood; or childhood abuse causes depression by creating insecure attachment, which then leads to higher depression

CONSTRUCTS AND VARIABLES

  • Construct: idea or conceptualization (e.g., relationship quality)

  • Variable: measurable representation of a construct (e.g., "How strong is your relationship with your partner?")

  • Many constructs are multidimensional and require multiple questions

  • Example: PTSD includes multiple components (flashbacks, mood alterations, maladaptive cognitions, etc.)

  • Guided by a theoretical framework — different theories will frame constructs differently

MARITAL/RELATIONSHIP QUALITY

  • Open-ended exercise: What does marital/partner quality mean to you?

  • Discussion in groups (4-6) about what constitutes quality, what it is and isn’t, and how you would know

  • Activity emphasizes that quality is a socially constructed and subjective construct requiring measurement.

CLASSIC RESEARCH DESIGN — EXPERIMENTAL RESEARCH

  • Randomization into groups that receive a certain condition

  • Manipulation of an independent variable while holding all other variables constant

  • Examining the influence on a dependent variable

  • Questions for Family Science: practical problems and applicability of true experimental design

GATHERING DATA

  • Broad category covering data collection processes (not elaborated in detail in these slides)

NATURAL (QUASI) EXPERIMENTS

  • Using real-world sorting or natural manipulation of variables to approximate randomization and control

  • Examples: COVID-19 studies; introverts vs extroverts; couples with high vs low relationship quality; college students pre-during-post pandemic conditions

QUANTITATIVE VS. QUALITATIVE METHODS

  • Quantitative

    • Surveys

    • Observation and coding

    • Secondary data analysis

    • Biological data analysis (growing in use)

    • Big Data/metadata analysis

  • Qualitative

    • Interviews

    • Observation and case studies

    • Content analysis (identifying common themes in qualitative data)

  • Mixed Methods: a combination of both approaches

PARTICIPANT OBSERVATION AND FAMILY COMMUNICATION

  • Lab-based: inviting parent-child and child into the laboratory to code a discussion on a topic

  • Data types: qualitative (transcripts) and quantitative (coding systems)

  • Field work: observing families in their homes (e.g., family dinners)

  • Most data are personal notes

IN-DEPTH INTERVIEWS AND FAMILY COMMUNICATION

  • Individual interviews: recruit people from families and ask about their experience of family communication

  • Interview formats:

    • Structured (diagnostic) or unstructured (asks about processes but allows additional information)

  • Group interviews: multiple people interviewed simultaneously

  • Family interviews: interview multiple family members or entire families

  • Multiple family interviews were conducted simultaneously

NOTE: Some numerical references from the figures and examples include the following data points:

  • Figure 1 (Johnson et al.): Relationship satisfaction and sexual frequency profiles for n = 2,101 couples

  • Subgroups: Highly Satisfied and Frequent Sex Profile (n = 1,815; 86.38%), Dissatisfied and Infrequent Sex Profile (n = 76; 3.60%), Satisfied Male Partner/ Highly Dissatisfied Female Partner and Moderate Sex Profile (n = 84; 4.01%), Satisfied Male Partner/Dissatisfied Female Partner and Moderate Sex Profile (n = 126; 6.01%)

  • Sexual frequency scale: 1 = not in the past 3 months, 2 = once per month or less, 3 = 2-3 times per month, 4 = once per week, 5 = 2-3 times per week, 6 = more than 3 times per week, 7 = daily

  • Relationship satisfaction scale: 0 = very dissatisfied to 10 = very satisfied

  • Population terms and sampling language emphasize representativeness and generalizability.