Discover published sets by community

The environment can affect concentration and comfort; examples include noise level, lighting, and temperature. A controlled environment is necessary for accurate assessments.

Reliability refers to the consistency of a measure; examples include test-retest reliability and internal consistency. Unreliable instruments yield inconsistent results.

Validity is about how well an instrument measures what it's supposed to measure; examples are content validity and construct validity. Invalid measures can't be interpreted meaningfully.

The number of participants can impact the statistical power and generalizability; examples include too few participants affecting reliability or too many causing practical difficulties. Optimal sample size is calculated based on desired power and effect size.

Individual differences such as age, gender, and cultural background can influence measurement; examples include educational tests that may favor certain cultural knowledge. Striving for a representative sample can minimize biases.

Participants may answer in a socially desirable way or be influenced by the way questions are asked; examples are acquiescence bias or the wording effect. Careful questionnaire design and anonymity can reduce this.

Expectations of the researcher can influence the measurement; examples include confirmation bias and observer-expectancy effect. Double-blind studies and objective measures can reduce this.

The way a test is administered can influence results; examples are computer-based versus paper-based tests and the presence of an administrator. Standardized procedures are important.

A participant's alertness and cognitive function can vary throughout the day; examples include morning vs. evening testing, where performance might differ. Timing should be consistent across participants or controlled statistically.

Types of scales (nominal, ordinal, interval, ratio) influence the type of analysis; examples include using parametric tests on interval/ratio data and nonparametric tests on nominal/ordinal data. Choice of scale impacts the informativeness of the data.

The sequence in which measures are presented can affect outcomes; examples include practice effects and fatigue. Counterbalancing or randomization can help manage order effects.

Unclear instructions can lead to confusion and inconsistent results; examples are ambiguous wording or overly complex explanations. Providing clear, simple instructions is essential.

Participants' expectations can influence their behavior; examples are placebo effects and demand characteristics. Masking the study's purpose and using control groups can minimize expectancy effects.

Factors like fatigue, hunger, or stress can affect performance; examples include taking a test while sleep-deprived or after eating. Ensuring participants are in a similar state or using within-subject designs can control for these variables.