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Design & Analysis of Research in HPERD

by: Brooklyn Haag

Design & Analysis of Research in HPERD PEP 455

Marketplace > University of Idaho > Physical Education > PEP 455 > Design Analysis of Research in HPERD
Brooklyn Haag
GPA 3.77

Douglas Dickin

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Douglas Dickin
Class Notes
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This 5 page Class Notes was uploaded by Brooklyn Haag on Friday October 23, 2015. The Class Notes belongs to PEP 455 at University of Idaho taught by Douglas Dickin in Fall. Since its upload, it has received 26 views. For similar materials see /class/227790/pep-455-university-of-idaho in Physical Education at University of Idaho.

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Date Created: 10/23/15
Chapter Outline Why we need statistics r computers in statistical analysis escription and inference are not statistical techniques Ways to select a sample Justifying post hoc explanations Difficulty ofrandom sampling and assignment how goo oes it ave to be Measures of central tendency and variability Basic Concepts of statistical techniques Why We Need Statistics Statistics is an ohjecti e way of interpreting a collection of ohserv ions Types of statistics 7 Descriptive tecnniuues e Cenelatienal tecnniuues 7 Differences amurlg gruups Example of Correlation What duesthis gure tell usv ls the cunelallun pusmve urneganve What duesthattell us then Huw strung is the currelalmr How Com puters Are Used in Statistics Frequently used in of ces labs and homes for statistical analysis Hardware and software Types of software for statistics 7 Statistical Analysis system SAS 7 Statistical Package furthe Secial Sciences SPSS e Excel777 Ways to Select a Sample Random sampling tables of random num e s Strati ed random sampling ma 39c sampling Random assignment Justifying post hoc explanations How good does the sample have to be Good enough for our purposes Measures of Central Tendency and Variability Central tendency scores 7 Wh t a rs an avera ge Mean typicallythuughtulasthe average Median M de rn du tnese measurestell us abuutthe data Range etseeres Vananee Standavddeviatiun Calculating Central Tendency curring number an unel Me n Line numbers up in order and take middle number Mode Most frequently 0c 7 Same data vnu have mure tn 7 lfan Even numberetake mean urtne Wu m the middle Mean Takes a sum of all scores and divides value by the number of scores Calculating Variability Range Simply the minimum and the ma um scores Variance 7 measure at drspersrun ur scurEs based an the squared deviatmn ur eaen seure rrum the me e What Standard Deviation is the square root of the variance Why do we want to know Central Tendency and Variability The measure of central tendency give us 7 One number sent the data 7 Pruvides insight rntu What the data luuk like 7 errtatrun 7 Extreme seures can skevvthe data The measure ofVariability e akes rntu aeeuunttne spread at seures 7 Farms uurunderstandrng urtne Nurmal Curve Freausncv m a scare Normal Curve mam culmtnllnmnlumhnd Example from Book for Mean and SD BlandN nunum hi is d 539quot amp of calculating mean 3 g and SD n s 25 x a i Z 2 Typically not the 2 j E easiest way to h 4 calculate a a u u nNows xeul wAnLW m Another Formula for Variancequot x Data point n 1 of points z Sum of n To get standard deviation tak e roo Descriptive statistics U ed to organize simplify and summarize the collected data Data typically consist of a set of scor calle is 39 fro a I u Ion These scores result m the measurements taken The or al measurements or values in a distributlon are called raw sco e Types of Scores Continuous e a contlnuous progresslon rromtne smallest amount to tne largest amount g easurement pusslble alung tne cunllnuum e g nelgnt welgnt temperature strengtn Discret 7 measurement ano classlncatlon are posslole only ln Whole unlts e g SlZE uHamlly number utscnuuls ln cuunlly e chnotomous e 2 categoryyanaole yesno allyeoeao Scales of Measurement Nominal Ordinal Interval Ratio Nominal e Merely classlnes ohlects ln accoroance yntn slmllantles ano olrrerences yntn respectto some rarcny or scores 7 Exam les culur Elf half tender respunse tn ayesnu questlurl snue preference Ordinal 7 Type oroata tnatls cnaractenzeo oytne aolllty to rank oroer on tn o s or n unoerlylno contlnuum e No common 7 Examples class ranks place crnnlsn ln a race unlt of measurement Interval Data having knuvvn and equal distance between seere units but having an arbitrary zeru pennt 7 Example Temperature en FahrenheitCelsius Scale Persenanty and intelligence Scares Ratio 7 Pussesses same prupemes uf interval data but dues have a true zeru pennt 7 Examples helghturwelght lstance measurement Categories of Statistical Tests Parametric e Nurmal distributmn 7 Equal vananees 7 independent ubservatmns Nonparametric distribution free e Distributlun is net nurmal Normal curve helps determine test to use Normal Curve Fvenuannym mm A n u 3 Same an vinath ul new e s ewness 5 in r Kunusls Skewness Kurtosis A A E C Statistics What statistical techniques tell us Reliability significance of effect Strength of the relationship meaningfulness Types of statistical techniques Relationships among variability Differences among groups Cause and effect Correlation is no proof of causation


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