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 and a 95% probability that the researcher should reject it outright
• BThe researcher accepts up to a 5% probability that the result occurred due to chance if the null hypothesis is true✓
• CThe result the researcher obtained will turn out to be correct on 95 occasions out of every 100 that the study is repeated
• DThere is a 95% probability that the null hypothesis is false, so it can be rejected with complete confidence by the researcher

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, and so free of odd or extreme values
• BThe data set contains an extreme outlier that would distort the mean✓
• CThe data are nominal categories with no inherent order at all (e.g. eye colour)
• DThe researcher wants to report the most frequently occurring value in the set

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 from one another between the two conditions, so individual differences (participant variables) can build up; controlled by random allocation to groups
• BThe same participants take part in all conditions, so practice or fatigue can affect later conditions; controlled by counterbalancing✓
• CDemand characteristics cause the order effects, because participants work out the aim and deliberately alter their answers; controlled by a single-blind procedure
• DSample sizes are always smaller in this design; controlled by switching to matched pairs or independent groups

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.

Which of the following best describes the difference between internal validity and external validity?

• AInternal validity concerns whether the results generalise to other settings and populations; external validity concerns whether the IV, rather than a confounding variable, caused the change in the DV
• BBoth terms refer to the consistency of a measurement when it is repeated over time (reliability)
• CInternal validity refers to ecological validity; external validity refers to temporal validity
• DInternal validity concerns whether the IV (not a confounding variable) caused the change in the DV; external validity concerns whether results generalise beyond the study✓

Answer: Internal validity asks whether the change in the DV was genuinely caused by the IV rather than by confounding variables. External validity (which includes ecological and temporal validity) concerns whether findings generalise to other settings, people and times.

According to the BPS Code of Ethics, which procedure most directly addresses the ethical issue of deception in a study where the true aim could not be revealed beforehand?

• AObtaining fully informed consent from each participant before the study begins, including the true aim
• BMaintaining the confidentiality and anonymity of all participant data
• CDebriefing participants fully after the study and offering the right to withdraw their data✓
• DConducting a cost-benefit analysis through an ethics committee (a research panel)

Answer: When deception is unavoidable, a full debrief afterwards is the main way to deal with it: participants are told the true aim and given the right to withdraw their data. Informed consent cannot fully address deception because the aim was hidden at the outset.

What is an independent variable?

• AThe variable which the researcher measures as the study outcome
• BA variable which varies systematically alongside the independent one
• CThe variable the researcher manipulates to see its effect✓
• DA variable held constant throughout the investigation

Answer: The IV is manipulated and the DV measured. A variable that changes systematically alongside the IV is a confounding variable, and one that simply adds noise is extraneous.

What is the difference between a Type I and a Type II error?

• AType I rejects a true null, Type II retains a false null✓
• BType I retains a false null, Type II rejects a true null
• CType I occurs in one-tailed tests, Type II in two-tailed tests
• DType I applies to parametric tests, Type II to non-parametric

Answer: A Type I error is a false positive and becomes more likely with a lenient significance level such as 0.10. A Type II error is a false negative and becomes more likely with a stringent level such as 0.01.

What are demand characteristics?

• ACues leading researchers to record their results in a biased way
• BCues leading participants to guess the aim and change behaviour✓
• CFeatures of the sample which make it unrepresentative of the population
• DRequirements a study must meet to be ethically approved

Answer: Participants may show the please-U or screw-U effect once they think they know the aim. Single-blind procedures and deception reduce it, at the cost of raising ethical concerns.

Why is a matched pairs design used?

• AIt controls participant variables without order effects✓
• BIt controls order effects but not participant variables
• CIt requires the smallest number of participants possible
• DIt removes the need for random allocation altogether

Answer: Participants are paired on relevant characteristics and split across conditions, so it combines a strength of repeated measures with one of independent groups. The cost is time-consuming matching and a larger sample.

What is counterbalancing used for?

• AControlling participant variables in an independent design
• BEnsuring the sample represents the target population
• CEnsuring the researcher remains blind to the condition
• DControlling order effects in a repeated measures design✓

Answer: Half the participants do condition A first and half do B first, so practice and fatigue effects are spread evenly rather than favouring one condition. It does not remove the effects, only balances them.

What does a correlation coefficient of −0.85 indicate?

• AA strong negative relationship between the two variables✓
• BA weak negative relationship between the two variables
• CA strong positive relationship once the sign is ignored
• DNo meaningful relationship between the two variables

Answer: Magnitude gives strength and sign gives direction, so −0.85 is strong and inverse. Correlation never establishes causation, and a curvilinear relationship can produce a coefficient near zero despite a clear pattern.

What is the purpose of a pilot study?

• ATo gather the main findings by using a much smaller sample size
• BTo replicate an earlier study and thereby confirm its conclusions
• CTo obtain ethical approval from the review committee
• DTo test procedures and identify problems before the main study✓

Answer: A small-scale trial run reveals ambiguous instructions, poorly calibrated materials or unworkable timings while they are still cheap to fix. It is about refining method, not collecting data.

When is a sign test used?

• AOrdinal data, independent groups, testing for a difference
• BInterval data, repeated measures, testing for correlation
• CNominal data, independent groups, testing for association
• DNominal data, repeated measures, testing for a difference✓

Answer: The three questions are always level of measurement, design, and difference or association. Chi-square handles nominal data with independent groups and tests association, which is the last option.

What does it mean for a result to be significant at p ≤ 0.05?

• AThere is a 95% probability that the hypothesis is definitely true
• BThe result will replicate in 95% of all future investigations
• CThere is a 5% or lower probability the result is due to chance✓
• DThe effect found is large enough to matter in practice

Answer: Significance is about probability, not proof, and says nothing about effect size or practical importance. A very large sample can make a trivial difference statistically significant.