L3 - Developmental Research Methods

→ Causality and developmental research (problem in psychology)

• interested in cause and affect, want to make these causal claims (which most times we cant do)

  • There may be more causes than just the one we are interested in

  • we dont know the relationships between the potential causal factors

  • We usually find links that are positively correlated to make causal claims

What is a causal inference?

• ways in which we draw causal connections between 2 variables

  • Important to control correlated variables

• typically the goal of an Experiment

Making causal inferences

• Example of genetics affecting abilities

We want to be able to understand the causal connections between early experiences and later behavioural functioning

• But CONFOUNDING variables (confounders) limit the extent to which we can truly assess cause and effect

• Errors in making causal inferences

  • Selection bias - limitation in selection in London

  • Simultaneity bias - time period

Factors to consider in causal inferences

• Randomization

  • Randomly sample to make causal inferences to avoid “convenient samples”

• internal vs. external validity - IQ example

  • Does it measure what it claims to measure?

• Causal inferences and growth

  • How can we account for growth and change in making causal inferences?

  • Problems of validity are problematic for growth and change perspectives.

  • We want to know what is changing and what is generating that change - we want to make a causal connection.

Contexts of Developmental Research

- What are methods of collecting data? 

• Descriptive Research

  • Research based on recording things that are observed

  • Often not causal (cant control something in the environment)

  • Not trying to manipulate anything or assess anything

    → E.g. Self report measures - Interviews and questionnaires, Teacher reports

    • Positives: lots of data, really fast

    • Negatives: Bias (how questions are formulated, can affect the answer)

Other types of descriptive research: → Observational Measures

• Naturalistic Observations (Just observing people un an uncontrolled setting)

  • Positives: Good external validity

  • Negatives: Control, limited range of behaviours

• Structured Observations (Scripted set of things happening, scenes that people come into)

  • Positives: control

  • Negatives: less descriptive, less external validity

Case studies

• Detailed descriptive study of a single individual

  • Positives: Allows study for unusual situations, raises questions for further study

  • Negatives: Lack of generalizability

    • Feral children example

• Non-descriptive approaches

  • Experimenter exerts some level of control to the study

  • Tries to measure different factors in different dimensions

  → Quantitative

  • Using numerical information

  • Countable

  • Measureable

  • “How many, “how much”, How often”

  • Often used in nomothetic approaches

    → Qualitative

  • Descriptive to some degree

  • Interpretations

  • Descriptive vs relational

  • Why, How, What happened

  • Often used in ideographic approaches

Correlational Research Approaches

• Examines the relationship between two or more variables

  • Positives: Quantify relationships and make predictions

  • Negatives: no causality

Experimental Research

• Manipulation of IV to examine effect on DV with random assignment

  • Positives: Causality

  • Negatives: not always possible to control all confounding variables; generalization becomes a problem with too much control

• Quasi-experiments

  • Comparison of groups that differ on a characteristic of interest

    • e.g. performance of 2 classes that are enrolled in the same courses

    • Schools tested using generalized testing

  • no random assignment

    • Positives: Allows for comparison of variables that cannot be controlled

    • Negatives: No causality

Developmental research methods (3 major types)

1. → Longitudinal designs

• Subjects studied at different times to see of anything changed

  • E.g.

    1. Studied at 2 years old

    2. Studied at 4 years old

    3. studies at 6 years old

    4. studied at 8 years old

2. → Cross sectional design

• All assessments done in one moment in time with all age groups to see if they differ on a particular dimension

  • E.g.

    • Subject A - 2 years old

    • Subject B - 4 years old

    • Subject C - 6 years old

    • Subject D - 8 years old

3. → Longitudinal-Sequential Design (aka Cross-sequential design)

   • Combines cross-sectional and longitudinal research

   • Children of different age groups are followed longitudinally

Hodges et al. (2021)