Social Psychology Research Methods: Measures Of Central Tendency, Measures Of Dispersion

Question 1

advantages and disadvantages of quantitative data

Answer 1

Advantages:
- easy to compare
- easy to understand
- can present it in different ways e.g. graphs, charts
- easy analysis
- easy to draw conclusions
- uses controlled variables making it easier to repeat

Disadvantages:
- produces unrealistic information which only focus’ on small fragments of behaviour
- cannot be converted into qualitative data
- gives a very superficial views which may lack validity

Question 2

advantages and disadvantages of qualitative data

Answer 2

Advantages:
- more detailed allowing for more understanding
- can be converted into quantitative data
- enables the researcher to delve into the reasons behind their initial findings
- produce more ecologically valid as it is collected in a more natural circumstance

Disadvantages:
- not easy to analyse and compare
- less scientific than quantitative data
- hard to replicate due to the lack of control in methods, lacks reliability
- difficult to draw comparisons

Question 3

Quantitative: levels of measurement
Nominal (categories)

Answer 3

Only be put into categories, e.g. what’s your favourite colour? Or grouping people into “fast”, “average” and “slow” runners in a race.

example:
- hair colour
- gender
- favourite football team

Question 4

Quantitative: levels of measurement
Ordinal (order)

Answer 4

Can only be put in order, the difference between each is not necessarily the same, e.g., positioning runners in a race, (1st, 2nd, etc). Data from ranked scale is considered ordinal.

example:
- GSCE grade
- shoe size

Question 5

Quantitative: levels of measurement
Interval/ratio

Answer 5

Data measured using units of equal intervals (cm, time, weight, temperature), e.g. Timing each runner in a race, so they have a finishing time. More precise than the other levels of measurement.

example:
- age
- height

Question 6

quantitative: measures of central tendency
Mean (advantages, disadvantages)

Answer 6

Average score, showing the middle of the data range when all data points are taken into account. Adding all the scores then dividing by total number of scores

Advantages:
- is the most powerful measure of central tendency as it takes all scores into account

Disadvantages:
- can be distorted by extreme scores so unrepresentative of the data set as a whole. Also assumes interval level data

Question 7

quantitative: measures of central tendency
Median (advantages, disadvantages)

Answer 7

The middle value in a set of scores, place all the scores in rank order and find the middle number. If the two middle scores and divide by 2

Advantages:
- unaffected by extreme scores so more appropriate measure when there are extreme scores. More representative of a small set of values than the mode

Disadvantages:
- only takes into account middle values, so lacks sensitivity as it ignores most values in a data set. Unreliable with small sets of data

Question 8

quantitative: measures of central tendency
Mode (advantages, disadvantages)

Answer 8

Most frequently occuring value in a set of data. The mode is easy to spot, though in some sets of scores there are more than one more occurring score. Bi-modal for 2, multi-modal for more than 2

Question 9

quantitative: measures of dispersion
Range

Answer 9

The difference between the highest and lowest scores in a given data set. Biggest-smallest. If a data set has extreme scores, it is often better to calculate the inter-quartile range. This involves cutting out the top and bottom 25% of the data, and calculating the range of the remaining scores.

Advantages:
- convenient way to express how spread out the data is
- easy to calculate

Disadvantages:
- provides no information about the spread of values within a range
- not representative

Question 10

what is standard deviation? (advantages, disadvantages)

Answer 10

• Report to 2 decimal places unless instructed otherwise, show every step of working
• Standard deviation:
  A more useful way of looking at the spread of scores within a data set. Deviation refers to the distance of each value from the mean. Each score in a data set would have a deviation value, so to get a single value that represents all deviation scores the SD needs to be calculated. For example, if the mean rating for obedience given by male participants was 7, and one male in the group was 9, the deviation score would be +2. If a different male in the group rating himself as 5 on an obedience scale, the deviation would be -2.

Advantages:
- precise way to measure the dispersion of the extract values being taken into account

Disadvantages:
- cannot be immediately sensed from the data, as opposed to the range
- more difficult to calculate