Lesson 3.2.1

3.2.1 Correlational research and Spearman's rho Quiz: Pearson Edexcel Psychology, Unit 3

20 questions

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Lesson 3.2.1, Correlational research and Spearman's rho: 20 multiple choice questions for the Pearson Edexcel Psychology (9PS0), Unit 3: Biological psychology, written with Revision Ninja.

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The 20 questions

  1. In a correlational study, what are the two variables called?

    • Co-variables, which are measured for each participant to see whether they change together, without manipulation by the researcher
    • Hypothesis and null hypothesis, which are stated before the data are collected and tested with a statistical test
    • Independent and dependent variables, which are manipulated and measured respectively by the researcher in the design
    • Experimental and control conditions, which are created by random allocation of participants to each group in the study

  2. What does a positive correlation between two co-variables mean?

    • The two variables are unrelated, so changes in one variable have no consistent relationship with changes in the other at all
    • As the value of one variable increases, the value of the other variable tends to decrease in a consistent way across the sample
    • One variable causes the other to change, so the researcher can conclude a cause and effect relationship from the data alone
    • As the value of one variable increases, the value of the other variable tends to increase in a consistent way across the sample

  3. What does a correlation coefficient of about -0.9 indicate?

    • A perfect positive relationship, in which every participant has the same value on both variables measured in the study
    • A weak positive relationship, in which the two variables change together only occasionally across the sample of participants
    • No relationship, because a correlation coefficient must be close to zero when the data are collected from a large sample
    • A strong negative relationship, in which one variable tends to decrease as the other increases across the sample of participants

  4. Which of the following is the main reason correlational research cannot establish causation?

    • Correlational studies always produce a coefficient of zero, which means that no relationship can ever be identified in the data
    • Correlational studies do not manipulate a variable, so a third variable or the reverse direction may explain the relationship
    • Correlational studies always use too few participants, so their results are never large enough to show any real effects
    • Correlational studies can only be carried out with animals, so they cannot be used to draw conclusions about human behaviour

  5. A scatter diagram shows points rising from bottom left to top right. What type of correlation does this suggest?

    • A negative correlation, because the points show one variable falling as the other variable increases across the sample
    • A perfect correlation of exactly -1, because all points must lie on a single straight line for any correlation to exist
    • A positive correlation, because the points show both variables increasing together across the sample of participants
    • A zero correlation, because a scatter diagram with a rising pattern always shows that no relationship exists at all

  6. What is the range of possible values for Spearman's rho?

    • From -1 to +1, with values closer to either extreme indicating a stronger relationship and values near 0 a weak one
    • From 1 to 5, matching the five-point scale on which both variables are measured in the typical correlational study
    • From 0 to 100, with values closer to 100 indicating a stronger relationship between the two variables in the sample
    • From -10 to +10, with the sign showing the direction and the size showing the number of pairs in the sample

  7. Spearman's rho is used rather than Pearson's correlation when the data are:

    • ordinal data or data that are not normally distributed, where the values can be ranked but the intervals are not equal
    • experimental data with a control group, where each condition contains participants allocated at random from the same pool
    • nominal data, which are categories with no order, such as the type of car a participant owns or the colour of their hair
    • interval data that are normally distributed, with equal intervals and no outliers and a linear relationship between variables

  8. A researcher collects data for 10 pairs of ranks and finds the sum of squared rank differences is 24. What is Spearman's rho, to two decimal places?

    • 0.85, calculated from 1 minus (6 times 24) divided by (10 times 99), which gives 1 minus 0.15
    • 0.15, calculated as 6 times 24 divided by 10 times 99, with no subtraction from the value of one
    • -0.85, calculated as the negative of the sum of rank differences divided by the number of pairs in the sample
    • 0.24, calculated directly as the sum of squared rank differences divided by the number of pairs in the sample

  9. A researcher finds Spearman's rho = 0.70 with n = 10. The critical value for a two-tailed test at the 0.05 level is 0.648. What is the correct conclusion?

    • The result is not significant, because 0.70 is smaller than the critical value for two-tailed tests at the 0.05 level
    • The result cannot be interpreted, because Spearman's rho can only be used with samples of 20 participants or more
    • The result is significant at the 0.05 level, because the observed value exceeds the critical value for n = 10
    • The result is significant only at the 0.01 level, since any value above 0.5 is always significant at the 0.05 level

  10. Which pair of hypotheses is correct for a correlational study that predicts a relationship between two co-variables?

    • The null hypothesis is the one that the researcher hopes to confirm, and it always predicts a strong negative correlation
    • The alternative hypothesis states there is a relationship between the co-variables, and the null states there is no relationship
    • Both hypotheses predict the same relationship, so the statistical test is used only to measure the strength of that single prediction
    • The alternative hypothesis states there is no relationship, and the null hypothesis predicts a clear positive relationship

  11. A correlation of r = 0.60 is found between hours of sleep and mood score. Which statement is the most accurate interpretation?

    • There is a moderately strong positive association between sleep and mood, but it does not show that sleep causes mood
    • Sleep causes mood, so people who sleep more will always have a better mood than people who sleep less in every case
    • There is no relationship, because a value of 0.60 is equal to the value expected from random chance in any sample
    • There is a perfect relationship, because any value above 0.50 shows that all participants have identical scores on both variables

  12. Why does a correlational study need a critical value to decide whether a rho is significant?

    • Because the critical value shows the probability that a rho could have occurred by chance at the sampling stage
    • Because the critical value converts rho into a percentage, so that it can be compared directly with a control group
    • Because a critical value gives the size of the sample that is needed to run a correlation in the first place
    • Because the critical value tells us the exact cause of the correlation, which the coefficient alone cannot reveal

  13. A scatter diagram shows no clear pattern and the points are randomly spread. What value of Spearman's rho would best match this pattern?

    • A value close to -1, because randomly spread points show that one variable always falls as the other rises in the sample
    • A value close to +1, because randomly spread points still show that both variables change together in a positive direction
    • A value close to 0, because randomly spread points show little or no consistent relationship between the two variables
    • A value of exactly 100, because any scatter diagram with a visible pattern of points must show a perfect relationship

  14. Which is a limitation of using correlations in psychology?

    • Correlations always show that one variable causes the other, so they can be used to test causal hypotheses directly
    • Correlations require the researcher to randomly allocate participants to conditions, which is often impossible to do ethically
    • Correlations cannot identify relationships between variables, so they are never used in any applied psychology research
    • Correlations can be affected by third variables that were not measured, which means an apparent link may be misleading

  15. Which of these is an example of a suitable co-variable pair for a correlational study linked to aggression?

    • Testosterone level and number of aggressive acts, both measured in each participant in the same study session
    • Length of a participant's hair and the day of the week on which they were tested by the researcher in the lab
    • Type of drug administered and the group to which each participant was randomly allocated by the researcher
    • Colour of the room and the order in which the questions were asked, which are both controlled by the experimenter

  16. A study correlates age with attitudes to drug use across 40 participants. What is the best reason to use Spearman's rho rather than Pearson's if attitudes are measured on an ordinal scale?

    • Because Spearman's rho does not need any data at all, and only uses the number of participants to find the relationship
    • Because Spearman's rho is designed to use ranks, so it fits ordinal attitude data that cannot be treated as equal intervals
    • Because Spearman's rho produces a larger coefficient than Pearson's for all data sets, which makes results easier to report
    • Because Spearman's rho requires the data to be fully normally distributed on an interval scale before it can be calculated

  17. A researcher reports a significant positive rho between hours of study and exam score. Which further step is most important before claiming that study improves exam performance?

    • Repeating the calculation with a different coefficient, such as a negative one, to check that the direction is reversed
    • Ignoring the significance result, because a significant correlation can never provide any evidence for a relationship
    • Considering third variables, such as prior ability or motivation, that might explain both study time and exam score
    • Converting the rank scores to percentages, since percentages are the only valid form of data to use in correlational work

  18. Which set of values is most consistent with a rho calculated from ranks of 10 pairs, where the sum of squared rank differences is 0?

    • rho = -1, because identical ranks on both variables show a perfect negative relationship between the variables measured
    • rho = +1, because identical ranks on both variables indicate a perfect positive relationship between the variables measured
    • rho = 0, because no differences in rank mean that the two variables have no relationship at all in the sample
    • rho = 0.5, because a sum of zero indicates that half the pairs are positive and half are negative in direction

  19. A researcher finds a rho of -0.40 for n = 20 in a two-tailed test. The critical value at the 0.05 level is 0.450. Which conclusion is correct?

    • The result is significant at the 0.01 level, because any rho near 0.5 is always significant at the most demanding level
    • The result is significant, because the absolute value of -0.40 is close to the critical value and the sign is negative
    • The result is not significant, because the absolute value 0.40 is smaller than the critical value of 0.450 at this level
    • The result is significant only if the sign is positive, since negative values of rho are never accepted as significant

  20. Which statement about scatter diagrams is most accurate?

    • They show the means of two experimental conditions, with each point representing the average score across all participants
    • They show the probability of a Type I error, with each point representing the risk of a false positive finding in the study
    • They show the strength and direction of the relationship between two co-variables, with each point representing one participant
    • They show the number of participants in each condition, with each point representing one independent variable manipulated

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