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Exercise Research > Week 3: Levels of measurement and Displaying data > Flashcards

Week 3: Levels of measurement and Displaying data Flashcards

1
Q

List the types of data (2 points)

A
  1. Qualitative
  2. Quantitive
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2
Q

Describe qualitative data (5 points)

A
  • Data representing information and concepts that are not represented by numbers
  • Quality information – in depth. Describes qualities or characteristics
  • Example:
    • Why do students prefer mixed gender rather than single gender classes?
    • What are the underlying reasons that contribute to choosing to go to classes etc.
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3
Q

Describe quantitive data (6 points)

A
  • Measures of values or counts and are expressed as numbers. Data that can be counted or measured in numerical values
  • Quantity information – lots of data to enable generalization
  • Example:
    • Distance jumped
    • number of participants in a sport
    • how many goals scored etc.
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4
Q

List the levels of measurement (4 points)

A
  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio
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5
Q

Describe Nominal Level (9 points)

A
  • Nominal = Naming or classifying (sometimes called categorical)
  • Simplest and least precise of the levels of measurement
  • Numbers often assigned. Examples:
    • Male = 1, Female = 2
    • Basketball players, Netball players
  • Report frequency something occurs or exists
  • NO other calculation can be made with nominal measures
  • Very useful for differentiating between objects or people
    • i.e. football teams; gender; sport played
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6
Q

Describe Ordinal Level (11 points)

A
  • More precise than the nominal level
    • Has the property of order – “Ranking”
    • Use the terms “more than” or “less than”
  • Numbers assigned represent relative amounts of the quality or attribute measured.
  • The order is important but the difference between is not known.
  • Ranking is an example of an ordinal measurement
  • No indication of how much difference
  • Cannot assume equal differences
  • E.g. points for team cross country running race
    • 1st, 2nd, 3rd …etc.
    • No indication of how far 2nd was off 1st etc.
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7
Q

Describe Interval Level (8 points)

A
  • More precise than nominal or ordinal scales
  • Equal differences in the measurements reflect equal differences in the amount of the variable being assessed
  • E.g. temperature:
    • 40ºC is 10ºC hotter than 30ºC
    • 30ºC is 10ºC hotter than 20ºC
  • Zero point is arbitrary
    • does not represent absence of the attribute
    • 0ºC does not represent absence of temperature
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8
Q

Describe Ratio Level (6 points)

A
  • The most precise & useful
  • Same characteristics of interval scale, plus, an absolute zero that reflects the absence of the attribute being measured
  • A negative score is not possible
  • Examples:
    • Exam scores
    • Repetitions of an exercise such as strength Strength testing
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9
Q

List the types of distribution tables (3 points)

A
  1. Rank order distribution
  2. Simple frequency
  3. Grounded frequency
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10
Q

Describe raw data (4 points)

A
  • the data originally generated that has not been processed or changed in any way
  • Any order
  • Lacks meaning- just a bunch of numbers
  • e.g. 13.5, 7.2, 13.2, 12.5, 19.1, 21.7, 29.0, 4.9, 33.6, 28.1, 20.6, 30.4, 25.9
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11
Q

Describe rank order distribution table (4 points)

A
  • Most common
  • Ranks highest to lowest
  • e.g. 33.6, 30.4, 29.0, 28.1, 25.9, 21.7, 20.6, 19.1, 13.5, 13.2, 12.5, 7.2, 4.9
  • Used when there is a small number data points (N)
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12
Q

Describe range (6 points)

A
  • The range (R) is the distance in value from the highest (H) to lowest score (L)
  • R = H – L
  • For example:
    • Highest score = 33.6
    • Lowest score = 4.9
    • R = 33.6 – 4.9 = 28.7
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13
Q

Describe simple frequency distribution (4 points)

A
  • Rather than having long, cumbersome list of numbers, helpful to form the numbers into a simple frequency distribution
  • First step = rank the scores
  • Then count the number of times (frequency = f ) a value occurs
  • Do this type of distribution if there are 20 different frequency values or less
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14
Q

Describe group frequency distribution (7 points)

A
  • Grouped frequency distribution is a listing of groups/values & the frequency of a value in a group *
  • Further compact the data and are particularly appropriate for large sets of data, meaning more than 20 different values
  • First step = decide how many groups should be formed and the size of the interval needed
    • Somewhere between 10 and 20 groups
    • Less than 10 groups - too many in each group or generalising
    • More than 20 groups - too few in each group
  • Use this for establishing interval size
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15
Q

Describe graphs (10 points)

A
  • All graphs should include:
    1. A title
    2. Label on the X - axis (horizontal)
    3. Label on the Y - axis (vertical)
  • General practice: variables on the X - axis and frequency on the Y - axis
  • Data can be displayed using a graph with common types being:
    • Histogram
    • Frequency polygon
    • Cumulative frequency graph
    • Bar graph
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16
Q

Describe the different types of graphs (10 points)

A
  • Histogram is the most common type of graph
    • Based on group frequency distribution data
  • Frequency Polygon isa line graph of class frequency plotted against class midpoint
    • Depicts the same data as a histogram
  • Bar Graph compares data across different categories or groups
    • Each bar is separate
    • Cannot draw a frequency polygon from this
  • Important note: A bar graph is used to compare discrete or categorical variables in a graphical format whereas a histogram depicts the frequency distribution of variables in a dataset
  • Cumulative Frequency Graph is a line graph of ordered scores (X – axis) plotted against the number of subjects who scored at or below a given score on the Y – axis.
    • As the name suggests the score accumulates with each frequency result until you reach your total N
17
Q

Describe a normal curve (4 points)

A
  • A normal curve represents a normal distribution of scores that you would expect to see for a specific population E.g.. exam results within a unit
  • Symmetrical, bilateral, bell shaped curve that theoretically occurs when a large number of scores are normally distributed
  • i.e. most scores in the middle and less towards each end
  • Sometimes skewed
18
Q

Describe mean, median and mode (2 points)

A
  • Numerical values that describe the middle or central characteristics of a set of scores
  • If measures of central tendency are known, any given score can be compared to the middle scores
19
Q

Describe mode (5 points)

A
  • Simplest measure of central tendency
  • More useful for larger sets of data than smaller ones
  • It is that score that most often occurs within a group of scores
  • If there is a tie = multimodal
  • If a normal distribution, the mode will be near the middle of the curve and be fairly representative of the middle scores
20
Q

Describes the advantages of mode (4 points)

A
  • Can be used with nominal, ordinal, interval or ratio data
  • Easy to identify
  • No calculations are necessary
  • Quick estimate of the center of the group that is fairly accurate when the distribution is normal
21
Q

Describes the disadvantages of mode (3 points)

A
  • May need to do a simple frequency distribution if lot of data
  • It is unstable compared to other measures
  • It disregards extreme scores
22
Q

Describe median (4 points)

A
  • It is based on the number of scores & their rank order
  • Need to do a rank-order distribution
  • If the number of scores in the group is odd, the median is the middle score
  • If the number of scores is even, median falls between the middle two scores
23
Q

Describes the advantages of median (3 points)

A
  • Can be used with ordinal, interval or ratio data
  • Disregards extreme scores
  • More appropriate when small number of scores in data set or when distribution is skewed with extreme scores
24
Q

Describes the disadvantages of median (2 points)

A
  • Lack of effect by the extreme scores can sometimes be a disadvantage
  • The median is an ordinal level of measurement and does not consider the size of the scores
25
Q

Describe mean (4 points)

A
  • The numerical average of a group of numbers is the mean
    • Sum all scores, then divide by the number scores
  • Not always a whole number – it is continuous rather than discrete
  • Assumes that the level of measurement is either interval or ratio
26
Q

Describes the advantages of mean (3 points)

A
  • Most commonly used measure of central tendency
  • Considers both the number of scores and their size (most sensitive measure)
  • Provides a basis for many additional calculations that yield even more information
27
Q

Describes the disadvantages of mean (2 points)

A
  • Very sensitive to extreme scores
  • Assumes a level of measurement that is either interval or ratio
28
Q

List the measures of variability (3 points)

A
  1. Range
  2. Variance
  3. Standard deviation
29
Q

Describe range (6 points)

A
  • A quick description – spread of scores in a distribution
    • Rough estimate
  • Easily calculated – difference between the highest & lowest score
  • Determined by only 2 scores
    • Not a sensitive indicator
  • Because it represents only extreme scores and provides no distribution information it is the least useful
30
Q

Describe variance (9 points)

A
  • Describes the spread of all scores in relation to each scores distance from the mean
  • Calculating variance:
    1. examine the spread of scores by determining the distance of each score from the mean, called the deviation from the mean
    2. Square (^2) each deviation and add each deviation into a sum
    3. Variance (s^2) = = Σ of squared deviations, divided by N
  • Variance is expressed as squared units
    • deviations squared to eliminate negative values
    • However this inflates scores and
    • does not give a precise indication of variability
31
Q

Describe standard deviation (8 points)

A
  • The problem of squared units that occurs in the variance can easily be rectified by taking the square root (√‾) of the variance. This is called the standard deviation
  • Most common statistical measure of variability
  • Applicable to interval and ratio level data
  • Most accurate & mathematically correct
  • Most statistical applications are based on the mean and standard deviation of the set of data
  • Results are often reported as the MEAN ± SD. For example 6 ± 2
  • A relatively small SD indicates the group has little variability
  • A relatively large SD indicates the group has large variability

Exercise Research (11 decks)

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