Chapter 1 - Basic Concepts Flashcards
What are the main characteristics of nominal variables?
Nominal variables represent unordered categories with no natural ordering (e.g., blue, orange).
How do ordinal variables differ from nominal and quantitative variables?
Ordinal variables have a natural order (e.g., first, second, third) but lack equal intervals or a unit of measurement.
What are the advantages and limitations of ordinal variables in research?
Advantages: Reflect natural order.
Limitations: Lack equal intervals, making some statistical analyses invalid.
Why are interval variables important for statistical analyses?
They have equal intervals, enabling meaningful arithmetic operations and statistical tests.
Give an example of a measurement on a ratio scale and explain why it meets its criteria.
Example: Weight in kilograms. It has a natural order, equal intervals, units of measurement, and an absolute zero point.
What is a psychological construct?
A psychological construct is a hypothetical attribute (e.g., intelligence, happiness) that explains behavior but cannot be directly measured.
How is an operational definition used to measure a psychological construct like intelligence?
By defining it through measurable criteria, such as test scores assessing verbal reasoning and spatial ability.
What are the challenges of indirectly measuring psychological constructs?
They may lack precision, involve cultural biases, and depend on the validity of the operational definitions.
Why are scores like GPA sometimes criticized when used as quantitative measures?
GPA assumes equal intervals between grades, which may not reflect actual performance differences.
Explain the link between operational measures and measurement scales (nominal, ordinal, interval, ratio).
Operational measures determine how abstract constructs are quantified, fitting them into one of these scales based on properties like order and intervals.
How do you define the reliability of a measurement tool?
Reliability refers to the consistency of a tool, producing similar results under similar conditions.
Why can the validity of a tool vary based on cultural or population context?
The relevance and interpretation of the tool’s items may differ across cultures or populations.
How do reliability and validity influence the quality of research results?
Reliable tools provide consistent data, while valid tools ensure the data measures what it intends to.
Why is it difficult to ensure both reliability and validity in psychological tests?
Ensuring consistency while capturing the true essence of an abstract construct is complex.
Give an example where a measurement tool is reliable but not valid.
A scale consistently measures weight incorrectly by 2 kg (reliable but not valid).
What is the difference between descriptive and inferential statistics?
Descriptive statistics summarize data (e.g., averages), while inferential statistics draw conclusions about populations from samples.
What does the term “population” mean in statistics, and how does it differ from a sample?
A population includes all individuals of interest, while a sample is a subset used to make inferences about the population.
What is a parameter in statistics? Provide an example.
A parameter is a numerical characteristic of a population, such as the average height of all Canadians.
What is a sample statistic, and why is it used to estimate a parameter?
A sample statistic, like a sample mean, is a calculated value from the sample used to infer the population parameter.
Explain how descriptive statistics can be used to interpret a psychological test.
Descriptive statistics, like mean scores, provide insights into the average performance of test-takers.
What is simple random sampling, and why is it important in research?
A method where every individual in a population has an equal chance of selection, ensuring representativeness.
What types of bias can arise in sampling?
Sampling bias (e.g., over-representing certain groups) and convenience sampling bias (e.g., using easily available participants).
How can sampling bias affect the external validity of a study?
It limits the generalizability of findings to the broader population.
How do convenience samples differ from random samples?
Convenience samples rely on easily accessible participants, while random samples are unbiased and representative.
Why is sampling error inevitable, even in a random sample?
Sampling error arises from natural variation and differences between a sample and the population.
What is measurement error, and why is it inevitable in research?
The variability in repeated measurements of the same entity due to imperfect tools or methods.
How does the concept of measurement error influence statistical interpretations?
It highlights the need to treat all measurements as estimates with inherent uncertainty.
Why does sample size influence sampling error?
Larger samples reduce variability and provide more accurate population estimates.
Explain how a confidence interval relates to measurement uncertainty.
It defines a range within which the true population parameter is likely to fall, accounting for uncertainty.
How does sampling error differ from sampling bias?
Sampling error is random and unavoidable; sampling bias is systematic and avoidable.
What is a significance test, and how is it used to compare two populations?
A test determines whether differences between groups are statistically meaningful rather than due to chance.
How do significance tests differ from estimation procedures?
Significance tests focus on whether a difference exists, while estimation quantifies the magnitude of the difference.
Why do some researchers criticize the overuse of significance tests?
They can lead to overemphasis on arbitrary p-value thresholds and ignore practical significance.
Why is estimation considered a more general and interpretative approach than significance testing?
Estimation provides confidence intervals and focuses on the size of effects rather than binary decisions.
