Research Methods

A psychologist predicts that participants who revise in silence will recall more words than participants who revise with music playing. Which type of hypothesis is this, and why?

• AA non-directional (two-tailed) hypothesis, because two conditions are compared
• BA null hypothesis, because it predicts no effect of music
• CA directional (one-tailed) hypothesis, because the direction of the difference is stated✓
• DA correlational hypothesis, because two variables are measured

Answer: The prediction states the direction of the expected difference (the silence group will recall MORE), which makes it directional (one-tailed). A non-directional hypothesis would simply predict a difference without specifying which group does better.

A researcher conducts an unrelated-design experiment comparing reaction times (measured in milliseconds) between a caffeine group and a placebo group. The data are normally distributed. Which inferential statistical test is most appropriate?

• AWilcoxon signed-ranks test
• BSpearman's rho
• CRelated t-test
• DUnrelated t-test✓

Answer: The design is unrelated (independent groups), the data are interval (milliseconds) and normally distributed (parametric), so the unrelated t-test is correct. The related t-test is for repeated measures, and Wilcoxon is the non-parametric equivalent for related ordinal data.

Which statistical test should be used for a test of difference using a repeated measures design with data at an ordinal level of measurement?

• AWilcoxon signed-ranks test✓
• BMann-Whitney U test
• CChi-squared test
• DSpearman's rho

Answer: Wilcoxon is used for tests of difference with related (repeated measures/matched pairs) designs at ordinal level. Mann-Whitney is the unrelated equivalent; chi-squared is for nominal data and association; Spearman's is for correlation.

A researcher tests for a difference in ordinal-level scores between two separate, independent groups of participants. Which inferential test is appropriate?

• AWilcoxon signed-ranks test
• BMann-Whitney U test✓
• CRelated t-test
• DSign test

Answer: Mann-Whitney is the test of difference for an unrelated (independent groups) design with ordinal data. Wilcoxon and the sign test are for related designs; the related t-test requires a related design and interval, normally distributed data.

A study records whether participants are 'left-handed' or 'right-handed' and whether they prefer 'tea' or 'coffee', then tests for an association between the two. What test and level of measurement apply?

• ASpearman's rho; ordinal data
• BUnrelated t-test; interval data
• CSign test; nominal data
• DChi-squared test; nominal data✓

Answer: Both variables are categories (nominal) and the study tests for an association between them with independent data, so chi-squared is correct. The sign test is for differences in a related design, not associations between two nominal variables.

In hypothesis testing, what does a significance level of p ≤ 0.05 represent?

• AThere is a 5% probability that the alternative hypothesis is true
• BThe researcher accepts up to a 5% probability that the result occurred due to chance if the null hypothesis is true✓
• CThe result is correct 95% of the time
• DThere is a 95% probability the null hypothesis is false

Answer: p ≤ 0.05 means the researcher accepts up to a 5% (1 in 20) probability that the observed results occurred by chance (sampling error) when the null hypothesis is actually true. It is not the probability that a hypothesis is true or false.

A researcher rejects the null hypothesis and accepts the alternative hypothesis, but in reality the null hypothesis was true. What type of error has been made?

• AA Type II error (a false negative)
• BA counterbalancing error
• CA Type I error (a false positive)✓
• DA sampling error

Answer: Rejecting a true null hypothesis (claiming an effect exists when it does not) is a Type I error, or false positive. A Type II error is the opposite: retaining a false null hypothesis (missing a real effect).

If a researcher uses a more lenient significance level, such as p ≤ 0.10 instead of p ≤ 0.05, what is the most likely consequence?

• AThe risk of a Type I error increases and the risk of a Type II error decreases✓
• BThe risk of a Type I error decreases and the risk of a Type II error increases
• CBoth Type I and Type II error risks decrease
• DNeither error risk is affected by the significance level

Answer: A more lenient level (e.g. 0.10) makes it easier to reject the null, increasing the chance of a false positive (Type I error) but reducing the chance of missing a genuine effect (Type II error). The two error types trade off against each other.

A researcher measures the relationship between hours of revision and exam marks for 20 students, with both variables recorded at the interval level but not normally distributed. Which test is most suitable?

• ASpearman's rho✓
• BPearson's r
• CWilcoxon signed-ranks test
• DRelated t-test

Answer: This is a test of correlation. Because the data are not normally distributed, the non-parametric Spearman's rho (which ranks the data) is appropriate rather than the parametric Pearson's r. Wilcoxon and the related t-test are tests of difference, not correlation.

An experimenter selects participants by listing everyone in the target population and choosing every 5th person on the list. What sampling method is this?

• AOpportunity sampling
• BStratified sampling
• CSystematic sampling✓
• DVolunteer sampling

Answer: Selecting every nth member from an ordered list is systematic sampling. Stratified sampling selects proportionally from subgroups; opportunity uses whoever is available; volunteer relies on self-selection through advertising.

Which of the following is the clearest example of the median being a more appropriate measure of central tendency than the mean?

• AThe data are normally distributed with no extreme values
• BThe data set contains an extreme outlier that would distort the mean✓
• CThe data are nominal categories with no order
• DThe researcher wants to report the most frequent value

Answer: The median is preferred over the mean when the data are skewed by an extreme outlier, because the median is not distorted by anomalous values. Nominal data require the mode, and the most frequent value is by definition the mode.

Why is a repeated measures design particularly vulnerable to order effects, and how are they typically controlled?

• AParticipants differ between conditions; controlled by random allocation
• BThe same participants take part in all conditions, so practice or fatigue can affect later conditions; controlled by counterbalancing✓
• CDemand characteristics are stronger; controlled by a single-blind procedure
• DSample sizes are smaller; controlled by using a matched pairs design

Answer: In repeated measures the same people do every condition, so performance can improve (practice) or worsen (fatigue/boredom) across conditions, an order effect. Counterbalancing (e.g. ABBA) varies the order so these effects are spread evenly across conditions.