HLT 4307

The act of collection of information on which a decision is made

The use of measurement in making decisions

Concise statement of fact that has been proven time and time again, generally accepted as true and universal

an explanation of a set of related observations that is based upon proof that has been verified

attempt to explain some basic observations before precise data has been rigorously collected and analyzed

• deals with numbers
• can be measured

ex. length, speed

• descriptions
• can be observed

ex. yellow, soft

a collection of methods for planning experiments, obtaining data, then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data

complete collection of all elements to be studied (scores, people)

the collection of data from members of a population

a sub collection of elements drawn from a population

a numerical measurement describing some characteristic of a sample

a numerical measurement describing some characteristic of a population

summarize or describe characteristics of a known set of data

use sample data to make inferences (or conclusions and predictions) about a sample

correlation or experimental designs

Center: value that shows the middle of data set is

Variation: a measure of the amount that values vary among themselves

Distribution: nature or shape of distribution of data (bell shaped, uniform, or skewed)

Outliers: sample values that are far from the majority of other values

Time: changing characteristics of the data over time

value at the center or middle of a data set

• median - use when there are extreme values
• mode - when data is categorical
• mean - every other time

how different scores are from the mean (spread, dispersion)

• range
• standard deviation
• variance

• Max - Min
• used to get a general estimate of different scores are from either other

Highest score - lowest

Highest - lowest + 1

measure of variation of values about the mean

s can increase dramatically with inclusion of outliers

units are the same as data

larger sd, greater the variance

the same thing as standard deviation except squared

• X is Y
• how things are
• most common type of study
• observe and measure specific characteristics without attempting to modify the subjects that are being studied

• x is related to y
• how things are in relation to other things
• used most commonly in health science studies
• observations not manipulated but related to each other

x causes y

• how things are and how they got that way
• hard to do well; apply treatments and observe effects
• used sometimes in evaluation but usually to explain descriptive evaluations

• Random - equal chance of being selected
• Systematic - every nth element in a population (ex. every third person)
• Convenience - data easy to get
• Stratified - but into subgroups then choose randomly from the group
• Cluster - divide population into clusters then choose random clusters and use all the population within the cluster

Cross sectional - all data observed, measured and collected at ONE point in time

Retrospective - data collected from the past

Prospective - data collected in the future from groups (cohorts) sharing common factors

Occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors

• Plan an experiment to avoid confounding
• Can avoid it by:
• Binding - participants dont know whether they are receiving treatment or placebo
• Matching - participants with similar characteristics
• Randomized Controlled Trial - randomly assign to each experimental group

• lists data values (individually or groups of intervals)
• interval is called class or bin; helpful for large data sets

Distribution extends to one side more then the other

• Skewed to the left (negatively)
• Skewed to the right (positively)

• a type of graph that portrays the nature of a data distribution
• Normal distribution has a bell shape

Has to do with how flat or peaked a distribution appears

• Platykurtic - more flat
• Leptokurtic - more peaked

• Column - to compare, bars horizontal
• Bar - same except vertical
• Line - to show trend
• Pie - to show proportions

• relationship between two variables
• can be generated for predicting the value of one variable given the value of the other variable
• good for data that comes in pairs

• aims to find casual mechanisms and determine predictability
• always at least one independent variable and one dependent variable
• relationships can be bivariate or multivariate

• Correlation:
• investigates linear relationship between two variables
• continuous variables
• data can be graphically presented
• neither is truly the ind. or dep. variable
• called a bivariate relationship
• no causation

• a numerical measure of the strength of the relationship between two variables representing quantitative data
• r is in between -1 and 1
• value of r does not change even if units change
• measures strength of a linear relationship only

• variance or errors are randomly and evenly distributed
• variance or errors on one variable are not correlated with variance or errors on another variable

• Sample of pair x,y is a random sample of independent quantitative data
• approximate straight line pattern
• outliers need to be removed if their known to be errors

• Causation: wrong to conclude that correlation implies casualty
• Averages: averages suppress individual variation and may inflate the correlation coefficient
• Linearity: there may be some relationship between x and y even when there is no linear correlation

consists of rules for assigning numbers to (objects) in such a way as to represent quantities of attributes

most measurement is indirect

• what do you want to know and how an you know it
• empirical or operational definitions: what can be measured that bests reflects what we want to measuere

• Observed score: actual score n a test
• True score: theoretical reflection of the actual amount of a trait or characteristic an individual possesses
• Error score: part of the score that is random

• The actual amount of the attribute you want to measure (ex. true dietary intake)
• Assumption: the construct is real and exists much like blood level or atomic weight if only we could measure it accurately

• Error - did not intend to measure that messed up the score
• Systematic error - repeatedly occurs and affects scores predictably
• Non systematic error - unpredictable and varies

• Nominal - characteristic, names, least precise measure, mutually exclusive (cant be both)
• Ordinal - order, ranking
• Interval - where a test or assessment tool is based on something we can talk about how much higher performance is compared to a lower one
• Ratio - characterized by the presence of an absolute zero; absence of any of the trait that is being measured

the degree to which scores are: free from errors of measurement; consistent, or stable across a variety of conditions

• types of reliability:
• Test retest reliability
• Interrater reliability
• Internal consistency reliability
• Parallel forms reliability

used when you want to examine whether a test is reliable over time (do it again in time by the same people) then find the correlation efficient when comparing scores aka correlation on a test given at two diff times

ex. same test is taken in july and january by the same people

• longer times require greater stability
• affected by change, carry over effects

• measure that tells you how much two raters agree on their judgments of some outcome
• correlation of scores measured by two different observers or raters

number of agreements/ number of possible agreements

used when you want to know whether the items are consistent with one another in that they represent one dimension, construct, or area of interest

• ex. different test forms
• a function of the relationship between items on a scale and number of items

• when you wan to examine the equivalence or similarity between two different forms of the same test (correlation of scores between two different versions of the test)
• ex. studying two different things same method

then find correlation coefficient