# HLT 4307

 The flashcards below were created by user kppatel702 on FreezingBlue Flashcards. Measurement: The act of collection of information on which a decision is made Evaluation The use of measurement in making decisions Law Concise statement of fact that has been proven time and time again, generally accepted as true and universal Theory an explanation of a set of related observations that is based upon proof that has been verified Hypothesis attempt to explain some basic observations before precise data has been rigorously collected and analyzed Quantitative deals with numberscan be measured ex. length, speed Qualitative descriptionscan be observed ex. yellow, soft Statistics a collection of methods for planning experiments, obtaining data, then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data Population complete collection of all elements to be studied (scores, people) Census the collection of data from members of a population Sample a sub collection of elements drawn from a population Statistic a numerical measurement describing some characteristic of a sample Parameter a numerical measurement describing some characteristic of a population Descriptive Statistic summarize or describe characteristics of a known set of data Inferential Statistics use sample data to make inferences (or conclusions and predictions) about a sample correlation or experimental designs Important characteristics of data 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 Measure of Central Tendency value at the center or middle of a data set median - use when there are extreme valuesmode - when data is categoricalmean - every other time Variability how different scores are from the mean (spread, dispersion) rangestandard deviationvariance Range Max - Minused to get a general estimate of different scores are from either other Exclusive range Highest score - lowest Inclusive range Highest - lowest + 1 Standard deviation 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 Variance the same thing as standard deviation except squared Descriptive X is Yhow things aremost common type of studyobserve and measure specific characteristics without attempting to modify the subjects that are being studied Correlational x is related to yhow things are in relation to other thingsused most commonly in health science studiesobservations not manipulated but related to each other Experimental x causes y how things are and how they got that wayhard to do well; apply treatments and observe effectsused sometimes in evaluation but usually to explain descriptive evaluations Methods of sampling Random - equal chance of being selected Systematic - every nth element in a population (ex. every third person) Convenience - data easy to getStratified - but into subgroups then choose randomly from the groupCluster - divide population into clusters then choose random clusters and use all the population within the cluster Experimental Designs 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 Confounding 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 placeboMatching - participants with similar characteristicsRandomized Controlled Trial - randomly assign to each experimental group Frequency Distribution lists data values (individually or groups of intervals)interval is called class or bin; helpful for large data sets Skewness Distribution extends to one side more then the other Skewed to the left (negatively)Skewed to the right (positively) Histogram a type of graph that portrays the nature of a data distributionNormal distribution has a bell shape Kurtosis Has to do with how flat or peaked a distribution appears Platykurtic - more flatLeptokurtic - more peaked Charts Column - to compare, bars horizontalBar - same except verticalLine - to show trendPie - to show proportions Correlation relationship between two variablescan be generated for predicting the value of one variable given the value of the other variablegood for data that comes in pairs Experimental research aims to find casual mechanisms and determine predictabilityalways at least one independent variable and one dependent variablerelationships can be bivariate or multivariate Correlation vs. Experimental Correlation:investigates linear relationship between two variablescontinuous variablesdata can be graphically presentedneither is truly the ind. or dep. variablecalled a bivariate relationshipno causation Correlation coefficient (r) a numerical measure of the strength of the relationship between two variables representing quantitative datar is in between -1 and 1value of r does not change even if units changemeasures strength of a linear relationship only Homoscedasticity (homogeneity or variance) variance or errors are randomly and evenly distributedvariance or errors on one variable are not correlated with variance or errors on another variable Requirements for r Sample of pair x,y is a random sample of independent quantitative dataapproximate straight line patternoutliers need to be removed if their known to be errors Common errors involving correlation Causation: wrong to conclude that correlation implies casualtyAverages: 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 Measurement consists of rules for assigning numbers to (objects) in such a way as to represent quantities of attributes most measurement is indirect Variables of interest what do you want to know and how an you know itempirical or operational definitions: what can be measured that bests reflects what we want to measuere Classical test theory: O = T + E Observed score: actual score n a testTrue score: theoretical reflection of the actual amount of a trait or characteristic an individual possessesError score: part of the score that is random True Score 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 Errors Error - did not intend to measure that messed up the scoreSystematic error - repeatedly occurs and affects scores predictablyNon systematic error - unpredictable and varies Levels of Measurement Nominal - characteristic, names, least precise measure, mutually exclusive (cant be both) Ordinal - order, rankingInterval - where a test or assessment tool is based on something we can talk about how much higher performance is compared to a lower oneRatio - characterized by the presence of an absolute zero; absence of any of the trait that is being measured Reliability the degree to which scores are: free from errors of measurement; consistent, or stable across a variety of conditions types of reliability: Test retest reliabilityInterrater reliabilityInternal consistency reliabilityParallel forms reliability Test-retest 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 stabilityaffected by change, carry over effects Interrater Reliability measure that tells you how much two raters agree on their judgments of some outcomecorrelation of scores measured by two different observers or raters number of agreements/ number of possible agreements Internal consistency reliability 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 formsa function of the relationship between items on a scale and number of items Parallel forms of reliability 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 Authorkppatel702 ID36809 Card SetHLT 4307 Descriptiontest 1 info Updated2010-09-23T17:38:32Z Show Answers