Human Growth and Development

Data Collection Methods There are several different approaches which a researcher can take to collect relevant information about people.

Method

Limitations

Example

Observational: Observing participants in their natural environments with minimal interference.

Hard to establish cause-and-effect relationships. Limited generalizability to other populations. Time-consuming and expensive. Participant dropout (attrition) can affect results. Social desirability bias can lead to inaccurate responses.

Watching children interact on a playground to study social behaviors.

Case Studies: In-depth analysis of an individual or small group,

Studying a patient with a rare psychological disorder to understand their experiences and treatment outcomes. Distributing an online survey asking adults about their exercise habits and motivations. Conducting interviews with teachers to understand challenges in implementing new teaching methods.

focusing on life histories, attitudes, behaviors, etc.

Survey: Collecting self-reported data through questionnaires or interviews. Interviews: Structured or semi-structured conversations to gather detailed information.

Participants may be reluctant to discuss sensitive topics.

Reliability and Validity In order to assess the quality and accuracy of the data and findings in a research we rely on the concepts of reliability and validity. Reliability refers to the consistency of research results. A reliable tool produces the same results under similar conditions, such as when a test gives consistent outcomes when repeated. Validity refers to the accuracy of a measurement — whether it measures what it claims to. Types of validity include: ●​ Internal validity: How accurately the study measures the relationship between variables. ●​ External validity: The ability to generalize results to other settings or populations. ●​ Ecological validity: Extent to which the findings of a study can be applied to the real world

●​ Construct validity: Whether the test measures the intended concept. ●​ Criterion validity: How well one measure predicts another outcome.

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