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Research Methods 1 > Descriptive Statistics- Graphs > Flashcards

Descriptive Statistics- Graphs Flashcards

1
Q

What do descriptive statistics present?

A

An overview of collected information (data)

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2
Q

What do descriptive statistics make coherent & easily digestible?

A

Large sets of information

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3
Q

What do descriptive statistics avoid?

A

Distorting the data

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4
Q

What do descriptive statistics suggest?

A

The appropriate inferential test to use.

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5
Q

How many parts are involved in the presentation of descriptive statistics?

A

3

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6
Q

What are the 3 parts involved in the presentation of descriptive statistics?

A

Graphical presentation (figures), numbers (often in table format), & verbal description (written in a report).

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7
Q

What do descriptive statistics provide?

A

An integrated, coherent & concise summary of data that can be related back to any question being asked.

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8
Q

What do we aim to do with collected data?

A

Get a visual summary of it (by transferring numbers into a graphical presentation).

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9
Q

How many levels of measurement are there?

A

4

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10
Q

What levels of measurement are there?

A

Nominal (measuring name-based data), ordinal (measuring order-based data), interval (measuring numerical data with no real zero point), & ratio (measuring numerical data with a true zero point) levels.

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11
Q

What are examples of nominal data?

A

Eye colours, car models, & favourite colours.

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12
Q

What are examples of ordinal data?

A

Rankings & rating scales

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13
Q

What are examples of interval data?

A

Times of the day & temperatures.

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14
Q

What are examples of ratio data?

A

Heights, weights, reaction times, & exam scores.

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15
Q

Which mathematical notation can be applied to nominal data?

A

”=” & “≠”

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16
Q

Which mathematical notation can be applied to ordinal data?

A

”=”, “≠”, “>”, & “<”

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17
Q

Which mathematical notation can be applied to interval data?

A

”=”, “≠”, “>”, “<”, “+”, & “-“

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18
Q

Which mathematical notation can be applied to ratio data?

A

”=”, “≠”, “>”, “<”, “+”, “-“, “x”, & “÷”

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19
Q

In which order does each level of measurement increase in complexity & informativity?

A

Nominal data, then ordinal data, then interval data, then ratio data.

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20
Q

What limits the choice of statistical test that can be applied to data?

A

The mathematical notation that can be applied to each data’s level of measurement.

21
Q

Which levels of measurement are discrete?

A

Nominal & ordinal levels.

22
Q

Which levels of measurement are continuous?

A

Interval & ratio levels.

23
Q

What is the aim of displaying the frequencies from categorical data?

A

To display it in the simplest way that conveys all the relevant bits of information.

24
Q

What do we present if we want to create a visual summary (or plot) of nominal data?

A

A bar chart of frequencies.

25
Q

When visually summarising nominal data, how do we indicate that we do not have a continuous scale on the x-axis?

A

By placing spaces in between each bar.

26
Q

If you show 10 people a face & ask them to judge the age of the face shown, how many responses will you get?

A

10 (N = 10)

27
Q

If you show 10 people a face & ask them to judge the age of the face shown, what responses might you get?

A

52, 49, 52, 61, 57, 57, 56, 54, 55, & 59

28
Q

What does N = 50 mean?

A

You have 50 scores.

29
Q

What must we do to make sense of continuous (interval & ratio) data?

A

Organise it

30
Q

Once our data has been divided into (meaningful) intervals, what do we graph to assess frequency distribution?

A

The number of items in each interval.

31
Q

How can we divide data into intervals?

A

By determining the smallest & largest score in our data set, & counting how many times these scores and every score in between are present.

32
Q

What does a frequency table look like?

A

It has the dependent variable (e.g. “Score”) and the independent variable (e.g. “Frequency”) in separate cells at the top of the table, then 2 rows of numbers underneath each of these headings.

33
Q

What does a frequency histogram look like?

A

It has the independent variable (e.g. “Age Scores”) on the x-axis, and the dependent variable (e.g. “Frequency”) on the y-axis, with bars representing each independent variable in the middle.

34
Q

How do you group data in order to create a frequency histogram?

A

By locating your minimum & maximum values (e.g. a minimum score of 57 & a maximum score of 96), & identifying how many intermediate values you have (e.g. 40).

35
Q

How do you create a frequency histogram?

A

By using a frequency table.

36
Q

What is an example of data that could be grouped & transformed into a frequency histogram?

A

The frequency of scores (in percentages) on a statistics test.

37
Q

If you have scattered & sparse data that have not been grouped, what will its frequency histogram look like?

A

It will have the independent variable (e.g. “Score) along the x-axis, and the dependent variable (e.g. “Frequency Count”) along the y-axis, with clusters of bars of varying lengths in the middle.

38
Q

What is an example of a range of scores & their frequencies that could be grouped together in a frequency histogram?

A

Scores of 57, 58, 59, & 60, with frequencies of 1, 2, 1, & 0.

39
Q

How would you represent scores of 61, 62, 63, and 64 with frequencies of 1, 1, 0, & 0, in a frequency table for use in a frequency histogram?

A

Under the heading “Scores”, you would put “61-64”, and under the heading “Frequency”, you would put “2”.

40
Q

What does a histogram of grouped data look like?

A

It has the independent variable (e.g. “Score”) on the x-axis, & the dependent variable (e.g. “Frequency Count”) on the y-axis, with several bars all joined together in the centre.

41
Q

What is the advantage of presenting grouped data in a histogram?

A

It means that the data has not been changed, but that the shape of the histogram is clear.

42
Q

What tells you the number of times an individual score appears (along the y-axis) in a histogram?

A

The height of each bar

43
Q

What tells you what each score is (along the x-axis) in a histogram?

A

The position of each bar.

44
Q

What are we interested in when creating a histogram?

A

The range of our values.

45
Q

What do the tails of a histogram look like?

A

They are short assorted purple bars, either side of a bump in the middle.

46
Q

What does the centre of a histogram look like?

A

An array of thin bars of different heights.

47
Q

What are some possible differences that could be present between 2 histograms?

A

Their shapes (they could have 1 or 2 centres), their location on the graphs, & the colour of their bars.

48
Q

What does a histogram with a negative skew look like?

A

It has 1 centre (towards the right) & 1 tail (towards the left), each located in the middle of the graph.

49
Q

What does a histogram with a positive skew look like?

A

It has 1 centre (towards the left) & 1 tail (towards the right), each located in the middle of the graph.

Research Methods 1 (16 decks)

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