LEVELS & TYPES OF DATA

Question 1

Nominal data

Answer 1

The most basic level of data.
-Data is in separate categories.
-These categories can be allocated numbers, but numbers bare no meaning.
-Closed questions produce nominal data.
-Observations produce nominal data

Question 2

Strengths of Nominal Data

Answer 2

-easy to generate from closed qs
-large amounts of questions can be collected quickly, increasing reliability.

Question 3

Weaknesses of Nominal data

Answer 3

-Without linear scale, ps may be unable to express degrees of response.
-can only use the mode as a measure of spread.

Question 4

Ordinal data

Answer 4

Slightly more precise than nominal.
-Data which can be ranked in ‘order’.
-Needs to be an increase in value of points along you data (numbers of your data DO have a meaning)
-Size of each does not have to be equal
-Units of measurement are not of equal, definable size, cannot tell us gap between 1st and 2nd etc…
-If psychologists record behaviour for each ps individually, data will be ordinal or interval.
-Based on opinion, tend to be subjective rather than objective.

Question 5

Strengths of Ordinal data

Answer 5

-More info than nominal data.
-Indicates relative values on linear scale instead of just totals.

Question 6

Weaknesses of Ordinal data

Answer 6

-Gaps between values are not equal, a mean cannot be used to assess central tendency.

Question 7

Interval data

Answer 7

Most precise
-Has equal intervals, gives more than just order, also shows difference between 1st and 2nd etc…
-Equal intervals could be cm, kg or seconds etc…
-Measurement of time tells us which ps is quickest/slowest, but also by how much.
-If machine can be bought to measure DV such as a thermometer or clock, data is likely to be interval.

Question 8

Strengths of Interval data

Answer 8

-More info as points are directly comparable as are all of equal value.
-Scientific measures used to record distance between values
-Highly reliable

Question 9

Weaknesses of Interval data

Answer 9

-No absolute baseline if scientific methods are not used.
-Ps can demonstrate a variable that the scale does not measure.

Question 10

Ratio

Answer 10

Most precise (Can be classed as same as interval)
-One refinement of interval data is data which has a ‘true zero’ e.g Time.
e.g a temp of 0 degrees does not mean there is no heat (temp is interval data).
However 0 seconds does not indicate time, data with true zero such as time are known as ratio level data, generally treated as interval when it comes to statistical analysis.

Question 11

Data analysis: nominal data- levels of central tendency=

Answer 11

Mode, frequencies, percentages, pie chart, bar chart.

Question 12

Data analysis: Ordinal data- levels of central tendency=

Answer 12

Median, mode, range frequencies, percentages, bar chart, pie chart.

Question 13

Data analysis: Interval/ratio- level of central tendency=

Answer 13

Mean, median, mode
frequencies, percentages
range, variance, standard deviation
bar chart, pie chart, histogram.

Question 14

Primary data

Answer 14

Researcher collects data witnessing an event or carrying out experiment, questionnaire etc.
Can be quantitative or qualitative.
Collected first hand by researcher.
(experiments, observations, interviews, questionnaires)

Question 15

Strengths of Primary data

Answer 15

-gathered first hand following careful operationalisation
-more trustworthy therefore more valid
-high internal validity as collected for purpose of study

Question 16

Weaknesses of primary data

Answer 16

-expensive to obtain because researchers have to conduct whole study
-limited to the time, place and number of ps

Question 17

Secondary data

Answer 17

Collected second hand through analysis of existing data.
When we use statistics or refer to existing research to develop our own theories.

Question 18

Strengths of secondary data

Answer 18

-easy to find without having to conduct research themselves
-only way to obtain data in some cases such as things from the past
-cheaper, easier to gain

Question 19

Weaknesses of secondary data

Answer 19

-make historical comparisons difficult
-ethical issues change during time
-may not be representative of wider population

Question 20

Repeated measures design, data type & suitable tests:

Answer 20

Nominal: sign test
Ordinal: Wilcoxon
Interval: Related t test

Question 21

Matched pairs, data type & suitable tests:

Answer 21

Nominal: sign test
Ordinal: Wilcoxon
Interval: Related t test

Question 22

Independent measures, data type & suitable tests:

Answer 22

Nominal: Chi squared
Ordinal: Mann Whitney U
Interval: Independent t test

Question 23

Correlation, data type & suitable tests:

Answer 23

Nominal: Chi squared
Ordinal: Spearman’s Rho
Interval: Pearson moment

Question 24

Quantitative data

Answer 24

Expressed numerically

Question 25

Strengths of quantitative data

Answer 25

-easy for comparisons
-quantify behaviour
-easy to replicate
-standardised
-highly reliable
-objective, no interpretation (factual)

Question 26

Weaknesses of quantitative data

Answer 26

• lacks reasoning
  -low validity
  -may give false impressions
  -no context in numbers
  -limited usefulness

Question 27

Qualitative data

Answer 27

Descriptive data that gives insight into ps thoughts and beliefs.

Question 28

Strengths of qualitative data

Answer 28

-high in detail
-high validity
-easy to interpret
-can be useful

Question 29

Weaknesses of qualitative data

Answer 29

-hard to summarise
-hard to compare
-less reliable
-researcher bias when interpreting
-subjective (bias risk)