Kin 306 - 10/17 Test

Front 1

There are more criterion for ____ and _____ people than adults because those two groups are more at risk.

Back 1

children and elderly

Front 2

Measurement –

Back 2

Collecting information on which decision will be based

Front 3

Evaluation –

Back 3

Using measurement to make decisions

Front 4

2 Types of standards –

Back 4

Norm-referenced standards and Criterion-referenced standard

Front 5

Norm-referenced standards -

Back 5

Obtained by testing many people in a group and determining what their values are (the norms). Know the group the norm refers to.

Front 6

Criterion-referenced standards –

Back 6

predetermined established level to be compared against.

Front 7

(Criterion-referenced) You can either:

Back 7

1-Meet or exceed the criterion (sometimes called “passing”) or 2-Fall below the criterion (sometimes called “failing”) Not everybody agrees with the decision.

Front 8

Frequency distribution -

Back 8

what is the frequency that a score (or range of scores) is observed

Front 9

Statistics

Back 9

Mathematical methods to analyze test scores. Allow us to describe groups of data and the relationship between them

Front 10

Histogram:

Back 10

A number of small ranges (bins) of observations on the x-axis (abscissa). The number of observations (frequency) on the y-axis

Front 11

Standard shape is a

Back 11

bell

Front 12

“Positive skews”

Back 12

refers to a long tail on the positive side.

Front 13

“Camel hump/bimodal”

Back 13

refers to two peaks that data is clustered around.

Front 14

“Mesokurtic”

Back 14

means a standard bell width.

Front 15

“Leptokurtik”

Back 15

means skinny bell curve.

Front 16

“Platykurtic”

Back 16

means flat, fat shape (plat, fat, flat)

Front 17

Mode –

Back 17

Represents the common numbers

Front 18

Median –

Back 18

represents the middle score. Or “50th” percentile. Good for dealing with skewed data or with extreme outliers

Front 19

Mean -

Back 19

Average. “x bar”

Front 20

Range –

Back 20

difference between the highest score and the lowest score

Front 21

~68.3% of all values fall within

Back 21

+/- 1 standard deviations from the mean

Front 22

~95.4% of all values will fall within

Back 22

+/-2 standard deviations from the mean

Front 23

~99.7% of all values fall within

Back 23

+/-3 standard deviations from the mean

Front 24

s=

Back 24

√ ∑(X-Xbar)2 / (n-1)

Front 25

__ axis is the independent variable while the __ axis represents is the dependent variable (what we’re often interested in)

Back 25

X...Y

Front 26

Correlation Techniques –

Back 26

a mathematical measure of the degree of relationship between two measures (x & y).

Front 27

r -

Back 27

Pearson product correlation
A line is fit through the data which sits in a position that minimizes the total deviation, in the x & y direction, from the line to all the data points = line of best fit, = regression line. Range is from +1 to -1. Value indicates direction of relationship, not whether or not is a good relationship. Higher r values allow for prediction of one score. If r=0 there is no relationship

Front 28

s=

Back 28

√ ∑(X-Xbar)2 / (n-1)

Front 29

Positive Relationship –

Back 29

A person with a high measure on one variable tends to have a high measure on the other variabe

Front 30

__ axis is the independent variable while the __ axis represents is the dependent variable (what we’re often interested in)

Back 30

X...Y

Front 31

Correlation Techniques –

Back 31

a mathematical measure of the degree of relationship between two measures (x & y).

Front 32

r -

Back 32

Pearson product correlation
A line is fit through the data which sits in a position that minimizes the total deviation, in the x & y direction, from the line to all the data points = line of best fit, = regression line. Range is from +1 to -1. Value indicates direction of relationship, not whether or not is a good relationship. Higher r values allow for prediction of one score. If r=0 there is no relationship

Front 33

s=

Back 33

√ ∑(X-Xbar)2 / (n-1)

Front 34

Positive Relationship –

Back 34

A person with a high measure on one variable tends to have a high measure on the other variable

Front 35

__ axis is the independent variable while the __ axis represents is the dependent variable (what we’re often interested in)

Back 35

X...Y

Front 36

Correlation Techniques –

Back 36

a mathematical measure of the degree of relationship between two measures (x & y).

Front 37

r -

Back 37

Pearson product correlation
A line is fit through the data which sits in a position that minimizes the total deviation, in the x & y direction, from the line to all the data points = line of best fit, = regression line. Range is from +1 to -1. Value indicates direction of relationship, not whether or not is a good relationship. Higher r values allow for prediction of one score. If r=0 there is no relationship

Front 38

Positive Relationship –

Back 38

A person with a high measure on one variable tends to have a high measure on the other variable

Front 39

Negative Relationship –

Back 39

A person with a high measure on one variable tends to have a low measure on the other variable

Front 40

No relationships -

Back 40

a person with a high measure on one variable may have any (i.e. high or low) measure on the other variable

Front 41

Correlation Technique –

Back 41

mathematical measure of the degree of relationship between two measures (x & y)

Front 42

However, remember correlation does not imply _______

Back 42

causation

Front 43

Interpretation of strength of r:
± 0.80 - 1.00
± 0.60 - 0.79
± 0.40 - 0.59
± 0.20 - 0.39
± 0.00 - 0.19

Back 43

- Very Strong relationship
- Strong relationship
- Moderate relationship
- Weak relationship
- No relationship

Front 44

How do you decided if a test is a good one? 3 key elements:

Back 44

1. Reliability
2. Objectivity
3. Validity

Front 45

1. Reliability

Back 45

- Consistent measures (NOT found in the skinfold test). Goal is <5% error

Front 46

2. Objectivity

Back 46

- Removes subjectivity
- Two different individuals should be able to arrive at the same score/number
- Clearly defined scoring system.
- Ways to improve objectivity: remove subjectivity through automation or creating a set of criteria

Front 47

3. Validity

Back 47

...does the test measure what it’s supposed to measure?
- Logical validity - subjective (expert) decision that the test measures what it claims.
–You have a valid method to measure something.
- Concurrent validity: see if the measures made by a new test correlates well with the gold standards.
- Bioelectrical Impedance Analysis had a strong correlation, but was overestimating.

Front 48

Factors affecting the reliability of a test:

Back 48

- A test should discriminate throughout the total range of ability: No perfect scores and no zeroes. (Example: When doing a chest press test, make sure that everyone can lift and empty bar and that you have more weight then needed)
- Test only a single attribute (a pull-up is influenced by arm muscles, grip strength, body weight, etc)
- Ensure the test is appropriate for the population being tested. (A balance to test older people would result in a perfect score for a college students)
- Enjoyablitiy – people wont do it well (or at all) if they don’t enjoy it!

Front 49

3 Parts of Test Administration

Back 49

1) Practice and warm up must be consistent
2) Directions to give the subject:
3) Scoring

Front 50

1) Practice and warm up must be consistent

Back 50

-Ensure that all participants have the same amount of practice and the same warm up (beware of the training effect,
- Not just the subject, the proctor needs to practice the test before measuring someone.

Front 51

2) Directions to give the subject:

Back 51

- Exact directions
- Write out instructions and read them to the subject
- Do they get hints to improve performance?
- Don’t give them the instructions to read, they may think they don’t need to instructions and so won’t read them. Potentially, they might also misinterpret it.

Front 52

3) Scoring

Back 52

- Decide what is a successful completion.
- How is an incorrect performance handled?
- Score sheet: focus in on necessary data

Front 53

Before starting data collection on real subject, practice on a “_____” to work out all of the details and the “kinks”

Back 53

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