Research Term Test 2 Flashcards
1
Q
4 levels of data
A
- nominal
- ordinal
- interval
- ratio
2
Q
Nominal
A
- allows to distinguish differences between items qualitatively
- no quantitative ordering or value
assign responses to different categories
- sex
- marital status
- postal code
- university major
- student ID
3
Q
Ordinal
A
- categories have logical order
- starts at lowest and ends at highest
- unknown numerical distance between catergories
- Likert scales - allows for comparison in relative terms
- ex. better or worse, smaller or larger
- letter grade in class: F - A
- Degrees held: BSc, MSc, PhD
- perceived exertion
4
Q
Interval
A
- measurement are numerical values
- intervals of equal length represent equal differences in characteristics
- Zero, does not signify absence of characteristic (think temperature)
- Starting point arbitrary
- ex. behavioural questionnaires, IQ test
5
Q
Ratio
A
- allow for id of absolute differences
- absolute zero
- zero means absence of characteristic
- most measured data first in this category
- ex. BMI, weight, height, VO2, age, time to completion
6
Q
Graphs - Describing data
A
- simplest way for describing data
- self-contained bundle of info
- title indicating variables, clear id of categories and values, units of measurements indicated
7
Q
Pie charts
A
- distribution of cases in form of a circle
- relative size of slice is proportional to proportion of cases within catergory
- can be used for all levels of measurements
- emphasize the relative importance of particular category to the total
- difficult to interpret when there are too many categories (~~5 max)
8
Q
Bar graphs & histograms
A
Horizontal axis: absicca
- categories or values of the scale
- independent variable
Vertical axis: ordinate
- frequencies: raw count or percentage
- calculated data
- dependent vairable
Bar graphs
- used when data is discrete (nominal or ordinal)
- gaps between bars
Histogram:
- sed when data is continuous (internal or ratio)
- no gaps between bars
9
Q
4 aspects of distribution
A
- shape
- centre
- spread
- existence of outliers
10
Q
Shape of graphs
A
- normal distribution
- skewness describes if its shape is off centered to right or left
- positive skewed (long tail to the right)
- negative skewed (long tail to the left)
11
Q
Center of graphs
A
place where equal number of score are on each side
- seen as the average
12
Q
Spread of graphs
A
- how tighly clustered the measurements aroudn the central point
- wide spread = heterogenous scores = platykurtic
- narrow spread = homogenous scores = leptokurtic
13
Q
Outliers of graphs
A
- upper and lower limits
- also ones that are disconnected from rest of the group
14
Q
Descriptive statistics
A
- used to characterize a group based on data taken from the group
Includes measures of:
- central tendency: extent to which data clusters around a point
- variability: extent to which data are spread out
15
Q
Central tendencies
A
- mean: arithmetic average of groups of numbers, calculated as sum of all numbers divided by total numbers of values in set
- (limitation to mean): affected by presence of outliers, sum of values is pulled away from middle when data is skewed, can produce value that is higher or lower than expected
- Median: single data value that resides in the middle of the data distribution
- mode: most frequent score in a distribution (limitation: often does not represent actual middle of values)
16
Q
Measures of variability/dispersion
A
- range: difference between high and low score
- standard deviation: estimate of spread of scores away from the mean
- standard error of the mean: estimate of expected difference between sample mean and population mean
17
Q
Inferential statistics
A
relationship between variables
useful example:
- is there a relationship between a simple field test and a difficult lab test for a variable of interest?
