Data Analysis (Week 16)

Question 1

Descriptive Statistics

Answer 1

• Describe data
• Indicate trends
• Show how the IV affects the DV
• Doesn’t tell you which hypothesis to accept
  **Two types of descriptive statistics: **
  1. Measures of central tendency (averages)
  2. Measures of spread (range, standard deviation)

Question 2

Measures of central tendency

Answer 2

A mathematical way to find out the typical or average score from a data set, using the mode, median or mean.

Question 3

Types of data

Answer 3

• Nominal data
• Ordinal data
• Interval data

Question 4

Types of data

Nominal Data

Answer 4

• Data that is discrete
• Fits into named categories eg. tally chart
• Always use the mode when finding the average of this type of data.

Question 5

Ordinal data

Answer 5

• Data that can be put into rank order
• Is subjective
• Intervals between the score may not be equal
• Usually from rating scales
• Always use the median when finding the average of this type of data.
  eg. in a school survey ppts are asked to rate how hard they thought they worked, from 1 to 10.

Question 6

Interval data

Answer 6

• Data can be put into rank order
• Is objective
• Intervals between scores are equal
• Usually an objective measure like, time, height, weight.
• Always use the mean when finding the average of this type of data.
  eg. How many mins each student spends on revision.

Question 7

The Mode

Answer 7

• Most frequent score in a data set
• Most suited to discrete data that is organised into categories (nominal data)
• If 2 or more values are equally common there will be 2 or more modes.

Question 8

Evaluating the Mode

Answer 8

**Strengths: **
* Is not skewed by anomalies
* Useful to show the most popular value.
**Weaknesses: **
* Less sensitive as it only uses the most frequent score - ignoring all other data
* Should not be used if there is more than one mode.

Question 9

The median

Answer 9

• Only used with numerical data on a linear scale
• Cannot be used with discrete data
• Useful in Pysch for subjective linear data on rating scales (ordinal data)
• To find the median all the scores in the data set are put into a list from smallest to largest.
• The middle number is the median
• If there is an even number of scores, the two middle values are added together and divided by 2 to find the median.

Question 10

Evaluating the median

Answer 10

Strengths:
* Not skewed by anomalies
Weaknesses:
* Less sensitive than the mean, as it only uses the middle scores - ignoring data that is very low or high.

Question 11

The Mean

Answer 11

• Usually called the ‘average’
• Can only be used with numerical data from linear scales.
• Uses Data which is objective
• Uses data that has equal intervals between the scores eg. weight, height, time (known as interval data)
• Mean is worked out by adding up all the scores in the data set and dividing by the total number of scores.
• It’s the most informative measure of central tendency because it takes every score into account.

Question 12

Evaluating the mean

Answer 12

Strengths:
* More sensitive as it uses the scores to provide an average.
Weaknesses:
* Can be skewed by anomalies - shouldn’t be used when there are extreme scores.

Question 13

Measures of Spread

Answer 13

They show how widespread the scores are across the samle. How varied the ppts were.
Includes;
* The range
* Standard deviation

Question 14

The Range

Answer 14

• Simplest measure of spread
• Find the largest and smallest value in the data set.
• Subtract smallest value from largest
• Add 1
• A Higher range shows more variation between ppts

Question 15

Evaluating the range

Answer 15

Strengths:
* Quick and easy to calculate
Weaknesses:
* Less sensitive as it only uses the highest and lowest
* Can be skewed by extreme values and it may look like there is lots of variation but there may not be.

Question 16

The Standard deviation

Answer 16

• Standard deviation considers the difference between each data point and the mean.
• Tells us the spread of the group
• Scores that are more spread out have larger standard deviations
• Closely clustered scores have smaller standard deviations
• When standard deviations of 2 groups are similar, it means they have a similar variation around the mean.

Question 17

Evaluating standard deviation

Answer 17

Strengths:
* More sensitive than the range, as it uses all the scores to show how far a group of ppts scores vary from the mean.
* not influenced by extreme score at either end of the data set
Weaknesses:
* Time consuming to calculate

Question 18

Bar charts

Answer 18

• Used for data in discrete categories and total or average scores.
• Gaps between each bar because columns aren’t related in any linear way.
• IV go along X-axis
• DV go on Y-axis

Question 19

Histograms

Answer 19

• Show the pattern in a whole data set, when this is continuous data
• Can illustrate the distribution of a set of scores
• Dv plotted on the x-axis in groups
• frequency of each score on the y-axis
• Bars have no gaps
• So if no scores in a category a gap must be left empty

Question 20

Scatter graphs

Answer 20

• Results from a correlational study are displayed on a scatter graph.
• Dots/crosses represent individual’s scores.
• Sometimes a line of best fit is drawn - it should come as close to as many points as possible.
• Strong correlation=data points lie close to line.
• Weak correlation=data points are more spread out.