Parametric vs. Nonparametric

Question 1

Mann - Whitney U

Answer 1

Ordinal data, but not limited to Compares two independent groups (Medians) Parametric Counterpart = 2 (Independent) Samples t-test

Question 2

Wilcoxon signed rank

Answer 2

Ordinal data Compares 1 median to a specified value / Para counterpart = z-test, 1-sample t-test Compares 2 dependent (paired) medians/ Para counterpart = Paired samples t-test

Question 3

Kruskal-Wallis

Answer 3

Ordinal data Compares 3 or more medians, 1 variable Parametric Counterpart = 1-way ANOVA

Question 4

Friedman

Answer 4

Compares 3 or more medians, 2 variables Parametric Counterpart = 2-way ANOVA

Question 5

Chi-Square Test of Independence

Answer 5

Tests 2 categorical variables for independence (lack of association) No parametric counterpart

Question 6

Primary citation for non-parametric test info

Answer 6

Martella, Nelson, Marchand-Martella (1999)

Question 7

Three general reasons to use a non-parametric test

Answer 7

1. Area of study is better represented by the median 2. You have a very small sample size 3. You have ordinal data, ranked data, or outliers that you can’t remove. 4. Data is not normal

Question 8

Reasons to use a parametric test.

Answer 8

1. We have continuous, “normally” distributed data 2. Usually have more statistical power

Question 9

General rule of thumb for parametrics

Answer 9

It is recommend that parametric tests be used even in those cases in which data do not meet the assumptions of normal distribution or homogeneity of variance but are derived from interval or ratio scores. Martella, Nelson, Marchand-Martella (1999)

Question 10

Non-parametric vs. Parametric Table

Answer 10

Question 12

Ordinal data, but not limited to Compares two independent groups (Medians) Parametric Counterpart = 2 (Independent) Samples t-test

Answer 12

Mann - Whitney U

Question 13

Ordinal data Compares 1 median to a specified value / Para counterpart = z-test, 1-sample t-test Compares 2 dependent (paired) medians/ Para counterpart = Paired samples t-test

Answer 13

Wilcoxon signed rank

Question 14

Ordinal data Compares 3 or more medians, 1 variable Parametric Counterpart = 1-way ANOVA

Answer 14

Kruskal-Wallis

Question 15

Compares 3 or more medians, 2 variables Parametric Counterpart = 2-way ANOVA

Answer 15

Friedman

Question 16

Tests 2 categorical variables for independence (lack of association) No parametric counterpart

Answer 16

Chi-Square Test of Independence

Question 17

Martella, Nelson, Marchand-Martella (1999)

Answer 17

Primary citation for non-parametric test info

Question 18

1. Area of study is better represented by the median 2. You have a very small sample size 3. You have ordinal data, ranked data, or outliers that you can’t remove. 4. Data is not normal

Answer 18

Three general reasons to use a non-parametric test

Question 19

1. We have continuous, “normally” distributed data 2. Usually have more statistical power

Answer 19

Reasons to use a parametric test.

Question 20

It is recommend that parametric tests be used even in those cases in which data do not meet the assumptions of normal distribution or homogeneity of variance but are derived from interval or ratio scores. Martella, Nelson, Marchand-Martella (1999)

Answer 20

General rule of thumb for parametrics

Question 21

Answer 21

Non-parametric vs. Parametric Table

Question 22

Nominal

Answer 22

A Nominal Scale is a measurement scale in which numbers serve as “tags” or “labels” only, to identify or classify an object. A nominal scale measurement normally deals only with non-numeric (quantitative) variables or where numbers have no value.

Question 23

Ordinal

Answer 23

the level of measurement that reports the ranking and ordering of the data without actually establishing the degree of variation between them.

quantitative data which have naturally occurring orders and the difference between is unknown. It can be named, grouped and also ranked

EX: Likert scales

Question 24

Interval

Answer 24

numeric scales in which we know both the order and the exact differences between the values.

Without a true zero, it is impossible to compute ratios. With interval data, we can add and subtract, but cannot multiply or divide.

Celsius example

10 degrees plus 10 degress celsius equals 20 degrees C but that doesn’t mean 10 is twice as hot. 10C = 50 F, 20 C = 68F

Question 25

Ratio

Answer 25

the ultimate nirvana when it comes to data measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied.