1
Q
Levels of measurement
A
- Nominal
- Ordinal
- Interval
- Ratio
2
Q
Nominal measurement
A
lowest in the hierarchy of measurements
- labelling or categorizing, classifying variables or events into categories (similar characteristics)
- no qualitative meaning
- true quantitative measurement
- no ranking occurs
3
Q
Ordinal measurement
A
- ranks events or objects on some attribute
- assigning numbers to each category
- examples: shortest to tallest, ADL’s 1= completely dependent 2= needs another persons assistance 3= needs mechanical assistance 4= completely independent
- cannot be used for mathematical operations
4
Q
Interval measurement
A
- involves ranking events or variables on a scale in which the intervals between the numbers are equal, the 0 value is arbitrarily set and does not have an absolute value.
- some addition and subtraction can occur
- can calculate the mean and standard deviation
- used with IQ testing
5
Q
Ratio Measurement
A
- highest form of measurement
- equal intervals between numbers
- true 0 is on the scale, meaning there is a total absence of property at 0
- variables here are considered continuous
6
Q
Classification of stats
A
- descriptive
- inferential
7
Q
Descriptive stats
A
- describe and synthesize data
- includes:
- frequency distribution
- measures of central tendency
- measures of variability
8
Q
Frequency distributions
A
- systematic listing of all the values of a variable from the lowest to the highest with the number of times each value is observed
- can be displayed in a frequency polygon
9
Q
Measures of central tendency
A
- distribution has an average, or one number that represents the distribution of values
- this includes:
- mean
* median - mode
- mean
10
Q
Mean
A
- sum of a set scores divided by the number of scores
- Example: 10 students scores are 55, 41, 46, 56, 45, 46, 58, 41, 50, 35. The sum of the scores is 473 and divided by 10 = 47.3
11
Q
Median
A
- middle scores
- Examples: 1, 3, 5, 7 median would be between 3 and 5 there for it would be 4
- Examples: 2,4,6,8,10, median would
12
Q
Mode
A
-the score that occurs the most frequently
13
Q
Measures of variability
A
- are used to describe the dispersion or the spread of data.
- included:
- Range
- Percentile
- Standard deviation
14
Q
Range
A
- the difference between the highest and lowest scores in the set.
15
Q
Percentile
A
- assigns the score to specific place within the distribution .
- Example: 50th percentile means that there are 50% of cases are higher than you.
- 98th percentile means that 2% of cases are higher than you
16
Q
Standard deviation
A
- most commonly used measure of variability
- is the average amount that each individual scare varies from the mean to the set of scores.
- the larger the standard deviation, the more variable the set of scores is.
17
Q
Bivariate statistics
A
- allow a researcher to consider two variables together and describe the relationship between the variables
- used for correlational studies
18
Q
Correlations
A
-tells the researcher to what extent the studies are related. Measured with a correlation coefficient
19
Q
Correlation coefficient
A
- which is an index that describes the relationship between 2 variables
- the possible values range from -1.00 through .00 to +1.00.
- (- or +) indicate the positive or negative relationship between variables
20
Q
Positive Correlation
A
-indicates that a high scores on one variable are paired with high scores on the other. and vise versa
21
Q
Negative correlation
A
- indicates that low scores on one variable are paired with high scores on the other variable and vice versa
- (inverse)
22
Q
Zero correlation
A
-when 2 variables are totally unrelated
23
Q
Describing risk
A
- the purpose is to be able to identify the risk before and after the exposure to an intervention
- Includes:
- Absolute Risk (AR)
- Absolute risk reduction (ARR)
- Odds ratio (OR)
- number needed to treat (NNT)
24
Q
Absolute risk (AR)
A
- simply the proportion of people who experienced an undesirable outcome in each group
25
Absolute risk reduction
-a comparison of the 2 risks is calculated by subtracting the AR for the exposed group from the AR of the unexposed group
26
Odds Ratio
-is the proportion of people with the adverse outcome relative to those without it
27
Number needed to be treated
-estimates how many people need to receive the intervention to prevent one undesirable outcome. Calculated by dividing 1 by ARR
28
Inferential Statistics
-based on the law of probability and are used to draw conclusions about the population on the bases of data obtained from the sample
-Purposes:
- to estimate the probability that the
sample accurately reflects the
population
- to test hypotheses about the population
-should be used when sample is randomly selected and the measurement scale is at the interval or ratio level
29
Sampling distributions
-selection of sample units by random selection
- are the most effective means of securing representative samples
-based on the assumption of random sampling from populations
-
30
Sampling errors
- refers to the variation in the statistical values that different samples of the population may present.
- the error will affect the statistical probability that the sample will accurately reflect the population
31
Parameter estimation
- estimating a population parameter
| - such as a mean, a proportion or a difference in the mean of 2 groups
32
Confidence interval
-sample mean that establishes a range of values for the population value and the probabiliy that the population value falls within that range
33
Hypothesis testing
- commonly used for inferential statistics
- it enables the researchers to predict the outcome of their studies
- involves the null and research hypothesis
34
Null hypothesis
- states that there is no relationship between the independent and dependent variables
* example- there is no relationship between the independent and dependent variables
35
Research hypothesis
- aka scientific hypothesis
| - is the prediction that the researcher makes about what will happening in the study
36
Type 1 error
- occurs when the researcher states that a relationship exists when there is none
- rejecting a null when you should accept
37
Type 2 error
- when the researcher states that a relationship does not exist when it does
- accepting a null when you should reject it
38
Level of significance
- usually expressed in terms of levels (p< 0.05) rather than an actual probability
- common levels are 0.01 and 0.05
39
Tests of statistical significance
-either done with parameter testing or nonparametric testing
40
Parameter
-refers to the occurrence of a variable in the total population
41
Statistic
- refers to the occurance in a smaller sample
42
Parametric testing
- use the sample stat to estimate the population parameter
-flexible and powerful, allow researcher to study effects on the other variable and there interaction
-Have 3 characteristics
1.focus on population parameters
2. require measurements at least on an interval scale
3.they involve other assumption, such as the
assumption that the variables are normally
distributed in the population
43
Nonparametric testing
- require fewer assumptions than the parametric method, because they are not based on population parameters and involve less restrictive asumptions about the shape of the distribution
- data must be measured on a nominal or ordinal scale
- most useful when data cannot be interpreted as interval level
44
Bivariant statistical testing
- used to analyze the relationship between the 2 variables
- Includes:
1. t-tests
2. analysis of variance
3. chi-squared tests
4. product moment correlation
45
Multivariate statistical analysis
- deals with 3 or more variables simultaneously
