Answer the following in about 50 words each: 10 x 3 = 30 marks
9. Ex Post Facto research.
Ex Post Facto research is a non-experimental design where researchers investigate cause-and-effect relationships by analyzing existing conditions or data without manipulating variables. It studies the effects of events or characteristics after they have occurred. For instance, examining the impact of early childhood education on adult academic success involves comparing groups with different educational experiences. While useful for exploring causality in real-world settings, it lacks the control of experimental designs, making it harder to establish definitive cause-and-effect relationships. Researchers must carefully consider confounding variables and biases when interpreting findings.
10. Steps of evaluating data in qualitative research.
Evaluating qualitative data involves several steps to ensure depth and accuracy. First, researchers familiarize themselves with the data by reading transcripts or listening to recordings multiple times. Next, coding is applied to identify patterns or recurring themes. These themes are then analyzed to derive insights, followed by triangulation to validate findings using multiple data sources or methods. Finally, interpretation connects the results to the research question, offering a meaningful narrative or explanation. This iterative process ensures reliability, contextual understanding, and relevance of the findings.
Discourse analysis examines how language is used in texts, conversations, or social contexts to uncover meanings, ideologies, or power dynamics. It focuses on the structure, context, and implications of communication, providing insights into how language shapes perceptions and interactions. For example, analyzing political speeches can reveal underlying strategies and beliefs. Discourse analysis is widely applied in psychology, linguistics, and social sciences to understand cultural and societal influences on communication. By exploring both content and context, it contributes to the understanding of human behavior and social structures.
12. Strengths and weaknesses of Field experiment.
Field experiments are conducted in natural settings, offering high ecological validity and practical applicability. For example, studying employee productivity in a workplace provides real-world insights. The strengths of field experiments include their ability to generalize findings and capture authentic behaviors. However, they have weaknesses, such as reduced control over variables, making results vulnerable to confounding factors. Ethical challenges, such as obtaining consent in public spaces, and logistical issues also arise. Despite these limitations, field experiments are valuable for studying phenomena in real-world contexts.
13. Types of constructs.
Constructs are theoretical concepts used to describe phenomena in research. Latent constructs like intelligence or anxiety are not directly observable and require indirect measurement. Observable constructs, such as age or weight, are directly measurable. Complex constructs, like personality, consist of multiple interrelated dimensions. For instance, self-esteem is a complex construct often measured using scales combining behavioral, emotional, and cognitive elements. Constructs guide the operationalization and measurement of abstract ideas, enabling researchers to study complex human behaviors systematically.
14. Causal Comparative research design.
Causal comparative research design examines cause-and-effect relationships by comparing groups with pre-existing differences, such as comparing urban and rural students’ academic performance. Unlike experimental designs, it does not involve random assignment or manipulation of variables. This design is valuable for exploring phenomena where manipulation is impractical or unethical. However, its reliance on observational data makes it prone to confounding variables, limiting the ability to establish definitive causality. Rigorous control of extraneous factors can improve its reliability.
15. Objectivity Safeguards.
Safeguarding objectivity in research involves several strategies. Researchers use standardized methods and procedures to ensure consistency. Double-blind designs minimize bias by concealing group assignments from participants and experimenters. Inter-rater reliability is applied in qualitative studies to reduce subjective interpretation. Additionally, pre-registering hypotheses and analysis plans ensures transparency. Avoiding leading questions and maintaining a neutral stance further enhance objectivity, contributing to the credibility and reliability of research outcomes.
16. Research biases.
Research biases are systematic errors that affect the validity of findings. Selection bias occurs when groups differ significantly due to flawed sampling. Confirmation bias happens when researchers favor data supporting their hypotheses. Observer bias occurs when expectations influence data interpretation. To mitigate these biases, researchers use strategies such as randomization, blinding, and clearly defined protocols. Addressing biases is crucial for ensuring the integrity and reliability of research conclusions.
17. Memory effect.
Memory effect refers to the influence of previous exposure to a task or stimulus on subsequent performance. For instance, in cognitive experiments, participants may improve on a task simply because of familiarity from earlier trials, not due to experimental manipulation. This can compromise the validity of repeated measures designs. Strategies like counterbalancing, introducing distractor tasks, and extending intervals between trials can reduce memory effects and ensure unbiased results.
18. Content analysis
Content analysis is a systematic technique for analyzing textual, visual, or auditory data to identify patterns, themes, or meanings. Researchers code data into categories based on predefined criteria or emergent themes. For instance, analyzing news articles to assess media bias involves categorizing language use and sentiment. Content analysis can be quantitative, measuring frequencies, or qualitative, exploring deeper meanings. While it provides valuable insights, its reliability depends on clear coding schemes and inter-coder consistency.
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