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# KIN250-Exam1Review.pdf KIN 250

MSU

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This 5 page Study Guide was uploaded by Brittany Ballog on Sunday September 27, 2015. The Study Guide belongs to KIN 250 at Michigan State University taught by Larissa True in Summer 2015. Since its upload, it has received 21 views. For similar materials see Measurments in Kinesiology in Kinesiology at Michigan State University.

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Date Created: 09/27/15

Exam 1 Review 0 Chapter 1 0 Test Measurement Evaluation 0 Norm reference test person s score is compared to scores of people who have already taken the same test I Example SAT GRE O Criterion reference test standard of performance is set that all or most students are expected to meet usually pass or fail I Example medical exam physical 0 Chapter 2 0 Four Learning Domains I Cognitive human intellectual development acquiring and using knowledge I Affective social and emotional skills sportsmanship I Motor human movement patterns I Physical all types of physicalbodily change affects wellbeing and functional health 0 Conducting a Needs assessment first thing you do when building a program I Needs assessment pretest meets or exceeds initial standard upgrade standards provide learning experiences repeat needs assessment posttest I Needs assessment pretest falls below initial standards provide learning experiences repeat needs assessment posttest O Phases of Program Development understand it s a continuous process 1 Est a program philosophy Developing program goals Planning program activities Delivering the program Evaluating and improving the program connects to 2 3 4 9593 0 Chapter 3 0 Independent variable cause an effect on the dependent variable variable that is manipulated I Example treatment group exposure to different condition 0 Dependent variable characteristic that is expected to outcome variable I Change as a result of an experimental treatment or I Vary across population as a result of exposure to a certain condition 0 Levels of measurement I Nominal simplest and least precise cant imply anything about magnitude of difference between the categories qualitative 0 Examples gender colors and MampMs country of residence I Ordinal can arrange in an order and rank but cant find numerical differences 0 Examples social class upper middle lower class rank freshman sophomore junior senior I Interval allows us to rank them and compare the sizes of difference between them no true zero point 0 Examples temp time of day on a 12hour clock I Ratio zero means something none of the quantity is present 0 Examples number of children income GPA years of work experience Skewness negative shifts to the right positive shifts to the left Mean add all sums then divide by n I most common and trustworthy not good for skewed data Median middle score if odd number middle value if even number halfway between the two middle values so add divide by 2 I independent of distribution shape especially useful with skewed data Mode most frequent value I Unimodal bimodal multimodal I calling for attention to values which cluster good for rough estimates Variance and standard deviation are both measures of dispersion or how spread out the data is around the mean Variance measure of how spread out the values are around the mean I Sum of squarestotal N I Once you do xmean then square that number then add all those up then divide by N to get variance Standard deviation need to calculate variance first before calculating SD positive square root of the variance I If the variance is 4 then sqrt 4 2 which is the SD I Xmean needs to equal 0 I Xmeanquot2 all numbers need to be positive I small SD observations are clustered tightly around a central value I large SD observations are scattered widely from the mean Normal Curve I Symmetrical distribution of scores with an equal number of scores above and below the midpoint of the abscissa I Mean median mode are all at the same point of the abscissa Correlation refers to the relationship between two or more variables I Strength weak or strong I Direction positive or negative I Shape linear or curvilinear Correlation direction I No correlation random or circular assortment of dots I Positive high values of one variable associated with high values of the other O O O O I Negative high values of one variable associated with low value of the other Correlation strength I R from l0 to 10 I RA2 shared variance can be viewed as a percentage Regression statistics that predict the value of one variables from the value of a second variable uses 1 variable to predict another I Strength of prediction shared variance equation to describe the relationship line of best fit I Purposes to model predict control I The degree to which X independent variable predicts dependent variable strength I The degree to which Y dependent variable can be explained by the model Rquot2 I Y BX A line of best fit line of regression 0 A y int b slope x given value Ttests and ANOVAs tell us if two things are different Null hypothesis no difference between the two variables Alternative hypothesis usually what you re trying to prove saying there is a significant difference between the two groups Type 1 error detecting a statistical difference between the two scores when a difference actually exists I When you reject a true null hypothesis Type 11 error when you do not reject a false null hypothesis Ttest value that represents the probability that the difference in the two sets of scores is real vs coincidental Pvalues I Level of significance number of tails if independent or dependent I P lt 05 reject null hypothesis only 5 chance the difference is a coincidence the difference is real I P gt 05 the difference is not statistically significant fail to reject the null hypothesis Independent samples ttest unpaired two means come from two independent samples independent from one another two different groups I Example women and men Dependent samples ttest paired two means come from the same sample one sample is measured on two different occasions same group I Example before treatment and after treatment ANOVA tests whether the mean of the dependent variable differs by the categorical variables use if more than 2 groups Oneway ANOVA equivalent to independent samples design for more than 3 populations groups 0 Chapter 4 O Validity degree of truthfulness of a test score 0000 Content validity simplest evidence of variability I Uses professional judgment and logic is used to determine that test items are representative of the content I Logic to determine if something is valid Criterionrelated validity concerned with individuals performance on two measures tapping the same construct I Example using a gold standard valid criterion Concurrent validity extend to which scores on a new measure are related to scores from a criterion measure administered at the same time I Example written first aid exam and handson measure Predictive validity uses scores from a new measure to predict performance on a criterion measure administered at a later time I Example predicting cardiovascular disease later in life using current risk factors like genetics smoking lack of exercise Reliability consistency repeatability precision I Two sets of scores must be obtained to determine the reliability of a test Testretest reliability correlation between two sets of data is conducted I R gt 70 indicates that the measure is reliable Splithalf oddeven reliability testing instrument is split in half or the oddeven items are separated to form two sets of scores from the test I Two sets of scores are correlated to estimate reliability Objectivity concerns the administration of tests results should be the same no matter who administers the test I Trying to take all personal bias out subjectivity I Highly objective tests height weight I Highly subjective judging a dive or form of a swing Can a test be reliable but not valid Yes Can a test be valid but not reliable No Valid and reliable hits in center of the target Test administration I Must plan ahead to increase the likelihood of smooth and efficient testing sessions I Proper planning also increases your chances of obtaining valid and reliable sources Securing material and preparing the test area I Equipment and supplies 0 Test directions should state what supplies are needed I Should be brought to testing location before the session I Testing area I Consider space requirements and privacy I Arrangement of testing area I Consider the sequence of test items to offset fatigue 0 Number of test stations equipment space supplies 0 Participants should understand the sequence 0 Knowledge of the test I Administer should know every detail of the test 0 Procedures number of trials measurement techniques 0 Scoring

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