QA

 Først indløbne spørgsmål  Derefter er ordet frit  Own laptops in the exam: Rules do not say anything about this, but specify printed aids and PC – so expect this is not permitted. Prepare for this.

 Rules for the use of PCs at exams:   Rules for DEDA exam specially:  library/Intranet/Uddannelse/Eksamen/PC- regler/Regler%20for%20brug%20af%20PC% 20ved%20eksamen%20i%20Experimental%2 0Design%20and%20Analysis.pdf library/Intranet/Uddannelse/Eksamen/PC- regler/Regler%20for%20brug%20af%20PC% 20ved%20eksamen%20i%20Experimental%2 0Design%20and%20Analysis.pdf

 Multiple choice sektionen:  Der kan være flere svarmuligheder til spørgsmålene.  Der kan være flere svarmuligheder på 0+ af spørgsmålene

 Q26 i eksamen: Gennemgang  26A) Work out the formula (algorithm) for the linear regression between the two variables.  Løsning: enten beregne manuelt eller sæt data ind i SPSS

Using SPSS to obtain scatterplot with regression line: Analyze > Regression > Curve Estimation... ”constant" is the intercept with y- axis, "b1" is the slope”

 Alle linear regressionformler har udseendet:  Y = a + bx --- dvs: (med tallene beregnet i SPSS)  Y = x  26B) Using your regression line formula, predict how many hours per week a person installing 50 games would spend playing them.  Løsning: Sæt tallene ind i formlen. Hvis vi bruger ovenstående:  Y = *50  Y = timer

 26C) Is the formula for the regression line the same if we put “number of games installed” on the Y-axis and “hours per week played” on the X-axis?  Nej – husk at regression af X on Y ikke er det samme som regression af Y on X – der vil være en lille smule forskel

 To predict Y from X requires a line that minimizes the deviations of the predicted Y's from actual Y's.  To predict X from Y requires a line that minimizes the deviations of the predicted X's from actual X's - a different task (although somewhat similar)!  Solution: To calculate regression of X on Y, swap the column labels (so that the "X" values are now the "Y" values, and vice versa); and re-do the calculations.  So X is now test results, Y is now stress score

 What is the difference between ordinal and interval data?? What kind of data are ratings?  Ordinal data fortæller os ikke noget om distancen imellem to målinger. Vi ved at ”1” er før ”2” men ikke hvor meget afstanden faktisk er.  I interval data er afstanden mellem målepunkterne konstant, men der er ikke et sandt nulpunkt  Ratings – ratings er vist bare et udtryk. Kig på hvilke karakteristika måleenheden der bliver brugt har. F.eks. ”ratings on a scale from 0-50” – jamen så er det intervaldata.

 Values are measureable  Measuring size of variables is important for comparing results between studies/projects  Different measures provide different quality of data:  Nominal (categorical) data  Ordinal data  Interval data  Ratio data Non-parametric Parametric

 Nominal data (categorical, frequency data)  When numbers are used as names  No relationship between the size of the number and what is being measured  Two things with same number are equivalent  Two things with different numbers are different

 E.g. Numbers on the shirts of soccer players  Nominal data are only used for frequencies  How many times ”3” occurs in a sample  How often player 3 scores compared to player 1

 Ordinal data  Provides information about the ordering of the data  Does not tell us about the relative differences between values

 For example: The order of people who complete a race – from the winner to the last to cross the finish line.  Typical scale for questionnaire data

 Interval data  When measurements are made on a scale with equal intervals between points on the scale, but the scale has no true zero point.

 Examples:  Celsius temperature scale: 100 is water's boiling point; 0 is an arbitrary zero-point (when water freezes), not a true absence of temperature.  Equal intervals represent equal amounts, but ratio statements are meaningless - e.g., 60 deg C is not twice as hot as 30 deg!

 Ratio data  When measurements are made on a scale with equal intervals between points on the scale, and the scale has a true zero point.  e.g. height, weight, time, distance.  Measurements of relevance include: Reaction times, numbers correct answered, error scores in usability tests.

 Q20B – is it a correlational study or between-groups design?  Korrelation er en analysemetode. Mange typer eksperimentelle designs kan give ophav til en korrelationsanalyse.  Eksperimentelle design er mere grundlæggende.  I dette tilfælde er der tale om et between-groups (independent measures) eksperiment design – 3 grupper der måles hver for sig  Bemærk: Ikke nødvendigvis et ”true” eksperiment – står ikke noget i opgaven om random allocation af participants

 Q21B – skal vi regne SD ud i hånden?? Hvilken SD regner SPSS ud? Den for populationen eller den for samplet?  SD for sample: ; SD for population: – hvad siger SPSS?  Note: Excel bruger SD for population (N-1)

 Q21C – er det +/- 1 SD? (Vel ikke +/- en halv SD?)  +/- 1 SD – når man siger ”within x SD of the mean” betyder det + eller –  Fordi data er normalfordelte ville vi forvente at 68% af de 20 scores lå indenfor en SD i begge retninger

Relationship between the normal curve and the standard deviation: All normal curves share this property: the SD cuts off a constant proportion of the distribution of scores: mean Number of standard deviations either side of mean frequency 99.7%68%95%

 About 68% of scores will fall in the range of the mean plus and minus 1 SD;  95% in the range of the mean +/- 2 SD's;  99.7% in the range of the mean +/- 3 SD's.

 Q22C – skal 998 tillægges datasættet, nu hvor det ikke kan udelades?  Fejl i opgaven: 998 findes ikke i talrækken.  Ideen var at observere hvordan mean, median, mode og SD ændrer sig forskelligt når scores ændrer sig, dvs. hvorvidt mean, median eller mode er mest ”sårbare”.  Der vil ikke være fejl i eksamenssættet.

 Q25 – hvorfor er der 6 values fra hver by, når du skriver at der burde være noget andet?  &%¤&¤... Host host... Nja der burde så stå ”six” i opgaveteksten, ikke ”seven” – det er en fejl.  Hvis det her skulle opstå, så brug ALTID de rå data. Løs opgaven med de data I bliver givet.  Som sagt: Eksamenssættet er grundigt tjekket.

 Hvornår bruger vi z- scores?

Using z-scores, we can represent a given score in terms of how different it is from the mean of the group of scores. X i = 64 μ = 63 How to calculate z-score: Going beyond the data: Z-scores SD = 2 - SD from the mean

We can do the same thing to calculate the relationship of a sample mean to the population mean: (1) we obtain a particular sample mean; (2) we can represent this in terms of how different it is from the mean of its parent population. μ = 63 64

 We use z-scores whenever we want to evaluate how far from a sample mean a score is  E.g. to evaluate if a score is an outlier  Hvis f.eks. en score er -1 SD fra mean, så ved vi at den falder indenfor de 68% hyppigste scores (grundet normalfordelingen i dataene) – dvs. ikke statistisk signifikant ved p<0.05  Or, conversely, how far away from the population mean our sample mean is

 Hvordan finder man ud af om scores i et datasæt er normalfordelt?

 Parametric statistics work on the mean -> All data must be interval or ratio level data  Parametric tests also make assumptions about the variance between groups or conditions  So we must BOTH have parametric measure AND scores must adhere to the requirements of parametric statistics

 For independent-measures (between groups), we assume that variance in one condition is the same as the other: Homogeneity of variance  The spread of scores in each sample should be roughly similar  Tested using Levene´s test (we do this in SPSS – often i gives you Levene´s test when running e.g. T-test)  For repeated-measures (within subjects), we operate with the sphericity assumption,  Tested using Mauchly´s test  Basically the same thing: homogeneity of variance

SPSS output (independent measures t-test) t is calculated by dividing difference in means with standard error: 4.58/ Row 1 left show result of Levene´s test – tests the hypothesis that variance in the two samples is equal. If Levene´s test is significant at p<0.05 the assumption of homogenity of variance in the samples has been violated (this is annoying). If not, we assume equal variance (use row 1) Sig. is < than.05, so there is a significant difference between alcohol/no alcohol on performance

 We also assume our data come from a population with a normal distribution  We can test how much a distribution is similar to the normal distribution using the Kolmogorov-Smirnov test (the vodka test) and the Shapiro-Wilk tests  The tests compare the set of scores in the sample to a normally distributed set of scores with the same mean and standard deviation  If the test is non-significant (p>0.05) the distribution of the sample is NOT significantly different from a normal distribution (i.e. it is normal) [OPPOSITE OF LEVENE´S TEST!]  If p<0.05, the distribution of the sample is significantly different from normal (e.g. positively or negatively skewed).

 We can run Kolmogorov-Smirnov and Shapiro-Wilk tests in SPSS  The most important is the Kolmogorov-Smirnov Test (K-S- test)  SPSS produces an output that includes the test statistic itself (D), the degrees of freedom (df) (= the sample size) and the significance value of the test (sig.).  If the significance of the K-S-test is less than.05, the distribution deviates significantly from the normal

 Brush-up on standard deviation from the mean  SD from the mean is just how many SD´s your score/result deviates from the mean value of the sample/population