mod 3,4,5 Flashcards
exam (41 cards)
What is the purpose of evaluating measures and hypotheses in scientific research?
Makes sure that the tools for data collection and hypotheses are robust, accurate, and aligned with research objectives.
Evaluating measures and hypotheses is critical for maintaining the integrity of research findings.
What are the key aspects of ensuring accuracy of data in research?
- Valid and reliable data representation
- Consistency in data collection
- Generalizability of findings
- Avoiding bias
- Enhancing credibility of research
These aspects help in establishing the trustworthiness of research outcomes.
Define reliability in the context of measurement instruments.
A measure gives the same results under similar conditions. Reliability ensures consistent data and fewer errors.
What is test-retest reliability?
Measures stability over time by testing the same group with the same instrument on two occasions.
An example is a personality test given twice within a month.
What does internal consistency assess?
Whether the items in a test measure the same underlying construct.
Cronbach’s alpha is a common statistic used for this purpose.
What does validity refer to in measurement instruments?
Whether a measurement instrument measures what it claims to measure.
Validity is essential for ensuring meaningful interpretations of data.
What is content validity?
How well a test includes all parts of what it is measuring.
For example, a mathematics test should include problems from all relevant topics.
Describe the difference between Type 1 and Type 2 errors in hypothesis testing.
- Type 1 Error: Seeing an effect that isn’t real (false alarm).
- Example: A psychologist concludes that a new therapy works when it actually doesn’t.
- Type 2 Error: Missing a real effect (missed detection).
- Example: A psychologist dismisses a therapy as ineffective when it actually helps patients.
These errors can significantly impact research conclusions.
What is effect size?
Shows how strong a difference or relationship is, no matter the statistical test result.
Common measures include Cohen’s d, Pearson’s r, and Eta-squared.
Define statistical power.
The chance of correctly finding a real effect. Higher power means the study is more likely to detect true results.
What is probability sampling?
A way to select people so everyone has a real chance of being chosen.
This method reduces bias and is more scientific.
List types of probability sampling.
- Simple Random Sampling
- Systematic Sampling
- Stratified Sampling
- Cluster Sampling
- Multistage Sampling
Each type has its own methodology and application.
What is the major difference between probability and non-probability sampling?
- Probability Sampling: Known probability of selection, less bias
- Non-Probability Sampling: Not all individuals have equal chance, more bias
This distinction affects the generalizability of research findings.
What are objectives in research?
Specific goals a study aims to achieve.
Objectives provide direction and focus for the research.
What is a hypothesis?
A testable statement predicting the relationship between variables.
Hypotheses establish a basis for testing assumptions.
Define inductive reasoning.
Generalizing from specific observations.
For example, concluding that most students prefer online learning based on surveys.
What is the definition of a population in research?
The entire group under study.
An example is all college students in India.
What does descriptive statistics do?
Summarizes and organizes data to describe its main features.
Techniques include measures of central tendency and measures of dispersion.
What is the purpose of inferential statistics?
Uses sample data to make predictions or generalizations about a larger population.
Techniques include hypothesis testing and regression analysis.
What are independent variables (IV)?
The variable that is manipulated or categorized to observe its effect.
For example, sleep duration in a study on memory performance.
Define confounding variables.
Unknown variables that can affect the dependent variable, leading to misleading results.
In a study on exercise and weight loss, diet could be a confounding variable.
What is the dependent variable (DV)?
The variable that changes based on the independent variable.
Example: In the sleep study, memory performance is measured to see if sleep duration affects it.
What are control variables?
Variables that are kept constant to avoid influencing the dependent variable.
Example: Keeping the room temperature the same while studying sleep effects.
Define confounding variables.
Uncontrolled variables that can affect the dependent variable, leading to misleading results.
Example: In a study on exercise and weight loss, diet can be a confounding variable.
