Descriptive statistics (MOCT/MOS) Flashcards

1
Q

Define measures of central tendency.

A

A method to find out the average score from a data set using (mode, median, mean)

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2
Q

Define mean

A

Average value in a data set

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3
Q

How do you calculate mean?

A

Add scores up
Divide by the number of ppts

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4
Q

Mean- Strengths

A

most robust as it uses all the scores to provide an average

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5
Q

Mean Weakness

A

Can be skewed by exteme low/high scores (anomalies)
Shouldn’t be used when there are extreme scores

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6
Q

Why is the mean a useful calculation?

A

Data summary, comparisons, predictive value

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7
Q

Define mode

A

Most frequent value or category in a data set

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8
Q

Mode- Strengths

A

Not skewed by anomalies
Useful to show the most popular value

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9
Q

Mode- weakness

A

Less robust- Only uses the most frequent score- ignoring all other data
Can’t be used if theres more than 1 mode

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10
Q

why is the mode a useful calculation?

A

used for categorical data. Where data is divided into categories

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11
Q

How to work out mode?

A

Put scores in rank order
Identify the most frequent value

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12
Q

Why shouldn’t the mode be used when there is more than 1 mode?

A

There are more than 1 categories that occur within the same highest frequency. So the mode doesn’t provide a single unique value to describe the mode

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13
Q

Define median

A

The middle value of a data set. when arranged in acceding/descending order

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14
Q

Median- strengths

A

Not skewed by extreme high or low values (anomalies)

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15
Q

Median- weakness

A

less robust. Only uses middle scores ignoring data that is v high/low

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16
Q

Why is the median a useful calculation?

A

useful for data where the order matters, like when you’re ranking things

more reliable when you don’t have many numbers to work with.

17
Q

How to work out median?

A

Put scores in rank order and identify middle value (divide by 2 if there are 2 medians)

18
Q

Define measures of spread

A

Describes how spread out/varied the values in a data set are through calculations of range, and standard deviation.

(How much the individual values in a data set differ/vary from each other)

19
Q

Define range

A

Refers to the difference between the higher and lowest values in a data set.

20
Q

How is the range calculated?

A
  1. Finding the largest and smallest value in a data set.
  2. Subtracting the smallest from the largest
  3. Add 1
21
Q

Why is there 1 added to the range?

A

To account for the fact that the scores are often rounded up and down in research. And make sure both highest/lowest values are included in the range.

22
Q

Range- strengths

A

Quick, and easy to calculate

23
Q

Range weakness

A

Less robust as it only uses highest and lowest scores. (Which could be skewed by anomalies) that aren’t typical of the majority of ppts.

24
Q

Standard deviation definition

A

Shows the amount of variation/dispersion of individual data points from the mean of a dataset.

25
Groups with scores that are more spread out….
Have larger standard deviations
26
Groups with closely clustered scores have….
Smaller deviations
27
What does it mean if the standard deviation of 2 groups are similar?
Not much variation/ They have similar variations around the mean
28
Standard deviation- strength
More robust- uses all scores to show how far a group of scores vary from the mean Not influenced by extreme scores at either end of the data set
29
Standard deviation- weakness
Time consuming to calculate
30
When are bar charts used?
Used for categorical, ordinal data, and frequency distributions
31
Describe how to plot the dv and iv on bar charts.
Levels of the iv= X axis The dv=Y axis
32
When are histograms used?
To show the pattern of a whole data set and distribution of numbers/scores (how numbers are spread out eg height/age)
33
Describe how to plot scores on a histogram
The dv: is plotted on the x axis (categorises) The frequency of each score: is plotted on the y axis
34
Scattergram definition
The results of a correlational study are displayed on a scatter gram.
35
Describe how to draw a scatter gram
1. Plot points 2. Draw a line 3. Line of best fit
36
How does a strong correlation look like on a Scattergram?
Data points are close to the line
37
How does a weak correlation look like on a Scattergram?
Data points are more spread out