Exam Revision Flashcards

Question

What is measurement error?

Answer 1

The discrepancy between the actual value we’re trying to measure, and the number we use to represent that value.

Answer 2

values have to have the same meaning over time and across situations

Answer 3

instrument measures what it set out to measure refers to the accuracy of a measure (whether the results really do represent what they are supposed to measure

Answer 4

ability of the measure to produce the same results under the same conditions

Answer 5

measure to produce consistent results when the same entities are tested at two different points in time

Answer 6

* Systematic variation * Unsystematic variation * Rnadomisation

Answer 7

Differences in performance created by a specific experimental manipulation. This is what we want

Answer 8

Differences in performance created by unknown factors . **Age***, **Gender***,** IQ***, **Time of day***, Measurement error etc. **These differences can be controleld of course (e.g., inclusion/exclusion of pps setting age range of 18-25)**

Answer 9

effects of unsystematic variation does not remove unsystematic variation

Answer 10

* The hypothesised cause * A predictor variable * A manipulated variable (in experiments)

Answer 11

* The proposed effect , change in DV * An outcome variable * Measured not manipulated (in experiments)

Answer 12

* Null hypothesis * Alternative hypothesis

Answer 13

that there is no effect of the predictor variable on the outcome variable

Answer 14

is that this is an effect of the predictor variable on the outcome variable

Answer 15

the probability of the null hypothesis being true (referred as p-value) by computing a statistic and how likely it is that the statistic has that value by chance alone Referred as p-value

Answer 16

null hypothesis

Answer 17

an alternate hypothesis

Answer 18

The alternative hypothesis is that this is an effect of the group on the outcome variable

Answer 19

The alternative hypothesis is that this the mean of the outcome variable for group 1 is larger than the mean of group 2

Answer 20

There would be far **greater** engagment in stats lecture if they were held at 4 PM and not 9AM

Answer 21

two ends of the tail of normal distirbution

Answer 22

1. A significant result means the effect is important 2. A non-significant result means the null hypothesis is true 3. A significant result means the null hypothesis is false (just give probability that data occured given null hypothesis, doesn't say huge evidence that null hypothesis is categorically false)

Answer 23

researchers degrees of freedom cchange after results are in and some analysis has been done

Answer 24

selective reporting of significant results

Answer 25

Hypothesising After the Results are Known

Answer 26

combination

Answer 27

1. Effect Sizes 2. Meta-analysis 3. Bayesian Estimation 4. Registration 5. Sense

Answer 28

* There a quite a few measures of effect size * Get used to using them and understanding how studies can be compared on the basis of effect size * A brief example: Cohen’s d

Answer 29

Effect size is a quantitative measure of the magnitude of the experimental effect. The larger the effect size the stronger the relationship between two variables.

Answer 30

Meta-analysis is a study design used to systematically assess previous research studies to derive conclusions about that body of research

Answer 31

* Bringing together multiple studies to get a more realistic idea of the effect * Can assess effect siz that are averaged across studies

Answer 32

investigating publication bias and other bias in meta-analysis values studies by their sample size and observe bias

Answer 33

probabilities of the data given the hypothesis and null hypothesis

Answer 34

conventional NHST analysis (and effect sizes)

Answer 35

* Telling people what you are doing before you do it * Tell people how you intend to analyze the data * Largely limits researcher degrees of freedom (HARKING p-hacking) * A peer reviewed registered study can be published whatever the outcome * The scientific record is therefore less biased to positive findings

Answer 36

* Knowing what you have done in the context of NHST * Knowing misconceptions of NHST * Understanding the outcomes * Adopting measures to reduce researcher degrees of freedom (like preregistration etc..)

Answer 37

at interval level

Answer 38

equal differences in the property being measured.

Answer 39

continuous variables can be measured in discrete terms; we measure age we rarely use nanoseconds but use years (or possibly years and months). In doing so we turn a continuous variable into a discrete one treat discrete variables as if they were continuous, e.g., the number of boyfriends/girlfriends that you have had is a discrete variable. However, you might read a magazine that says ‘the average number of boyfriends that women in their 20s have has increased from 4.6 to 8.9’

Answer 40

instrument is measuring what it claims to measure (does your lecturers’ helpfulness rating scale actually measure lecturers’ helpfulness?).

Answer 41

unsystematic variation and systematic variation

Answer 42

between-group design,

Answer 43

differences between the characteristics of the people allocated to each of the groups is likely to create considerable random variation both within each condition and between them

Answer 44

, repeated-measures designs have more power to d

Answer 45

independent and repeated design measure

Answer 46

* Practice effects * Boredom effects

Answer 47

Participants may perform differently in the second condition because of familiarity with the experimental situation and/or the measures being used.

Answer 48

: Participants may perform differently in the second condition because they are tired or bored from having completed the first condition.

Answer 49

counterbalancing the order in which a person participates in a condition

Answer 50

we randomly determine whether a participant completes condition 1 before condition 2, or condition 2 before condition 1

Answer 51

A normal distribution

Answer 52

bell curve

Answer 53

This means that the distribution curve can be divided in the middle to produce two equal halves

Answer 54

1. Mean (central tendency) 2. Standard deviation (dispersion)

Answer 55

standard deviation

Answer 56

e.g., If we move 1σ to the right then it contains 34.1% of the valeues

Answer 57

normally distributed

Answer 58

scores divided by the number of scores

Answer 59

central tendency for roughly symmetric distributions

Answer 60

it can be greatly influenced by scores in tail e.g., extreme values

Answer 61

1. Median 2. Mode

Answer 62

the middle score when scores are ordered. the middle of a distribution: half the scores are above the median and half are below the median.

Answer 63

* extreme scores or skewed distribution * can be used with ordinal, interval and ratio data.

Answer 64

frequently occurring score in a distribution, a score that actually occurred

Answer 65

with nominal data

Answer 66

sample fluctuations and is therefore not recommended to be used as the only measure of central tendency

Answer 67

symmetric distribtions

Answer 68

mean is greater than the median, which is greater than the mode

Answer 69

usually the mode is greater than the median, which is greater than the mean

Answer 70

bulge or bend in greek

Answer 71

the tendency for the values of a random variable to cluster round its mean, mode, or median.

Answer 72

prefix meaning thin

Answer 73

a prefix meaning flat or wide (think Plateau)

Answer 74

Kolmogorov-Smirnov test Shapiro-Wilks test

Answer 75

sample size

Answer 76

significant normally disttibuted

Answer 77

lack of power in the test to detect a significant effect

Answer 78

determining whether data is normally distributed or not

Answer 79

parametric tests

Answer 80

each score occurs

Answer 81

1. Lack of symmetry (called skew) 2. Pointyness (called kurotsis)

Answer 82

tails of the distribution are as they should be

Answer 83

A Allow inferences of cause

Answer 84

A (18-26) = -8/4 = -2

Answer 85

A = 80% of 40 is 32 (0.80 * 40)

Answer 86

A = independence is an assumption of parametric and not dependence

Answer 87

A - correct B. Incorrect as value of skewnessis –0.079, which suggests that the dataare only very slightly negatively skewedbecause the value is close to zero C. Incorrect as value of kurtosis is0.098, which is fairly close to zero,suggesting that kurtosis was not aproblem for these data D. Incorrect as value of skewnessfor the number of hours spent practisingis –0.322, suggesting that the data areonly slightly negatively skewed

Answer 88

data is skewed

Answer 89

negatively skewed

Answer 90

positively skewed

Answer 91

how much our data lies around the ends/tails of our histogram which helps us to identify when outliers may be present in the data.

Answer 92

pointy or leptokurtic

Answer 93

be more sloped or platykurtic

Answer 94

peak of a frequency-distribution curve

Answer 95

normal distribution

Answer 96

all good! = normal distribution

Answer 97

platykurtic

Answer 98

leptokurtic

Answer 99

Good because both the skewness is between -1 and 1 and kurtosis values are between -2 and 2.

Answer 100

Bad because although the skewness is between 1 and -1, we have a problem with kurtosis with a value of 2.68 which is larger than 2 and -2

Answer 101

third variable = confounding variable e.g, we find that drownings and ice cream sales are correlated, we conclude that ice cream sales cause drowning. Are we correct? Maybe due to the weather

Answer 102

influencing your results e.g., ice cream and drowning session

Answer 103

Use of RCTs. Randomized Controlled Trials allow to even out the confounding variables between the groups

Answer 104

we need to actively manipulate the variable we are interested in, and control against a group (condition) where this variable was not manipulated.

Answer 105

causality between two variables cannot be assumed because there may be other measured or unmeasured variables affecting the results”

Answer 106

linearity or less commonly additivity

Answer 107

effect of many predictors

Answer 108

There is a a linear effect when the data increases at a steady rate like the graph on the left. Your cost increases steadily as the number of chocolate bars increases. The graph on the right shows a non-linear effect when there is not this steady increase rather there is a sharp change in your data. So you might feel ok if you eat a few chocolate bars but after that the risk of you having a stomach ache increases quite rapidly the more chocolates you eat. This effect is super important to check or your statistical analysis will be wrong even if your other assumptions are correct because a lot of statistical tests are based on linear models.

Answer 109

measurement error and NOT variance

Answer 110

recording instrument failure to human error

Answer 111

1. Systematic 2. Random

Answer 112

: predictable, typically constant or proportional to the true value and always affect the results of an experiment in a predictable direction

Answer 113

for example if I know I am 5ft2 and when I go to get measured I’m told I’m 6ft this is a systematic error and pretty identifiable - these usually happen when there is a problem with your experiment

Answer 114

measurable values being inconsistent when repeated measures of a constant attribute or quantity are taken.

Answer 115

for example my height is 5ft2 when I measure it in the morning but its 5ft when I measure myself in the evening. This is because my measurements were taken at different times so there would be some variability – for those of you who believe you shrink throughout the day.

Answer 116

Average squared deviation of each number from its mean.

Answer 117

things being measured and of the measurement process

Answer 118

states that the sampling distribution of the mean approaches a normal distribution, as the sample size increases. This fact holds especially true for sample sizes over 30. Therefore, as a sample size increases, the sample mean and standard deviation will be closer in value to the population mean μ and standard deviation σ .

Answer 119

Frequency of scores Look at distribution of data, skewness, kurotsis

Answer 120

To identify outliers Shows median rather than mean (good for non-normally distributed data)

Answer 121

simply bar charts with lines instead of bars

Answer 122

display means (and standard errors)

Answer 123

a relationship between two variables, e.g. correlation or regression Only use regression lines for regressions!

Answer 124

Particular kind of scatterplot that can be used instead of the 3-D scatterplot clearer to read

Answer 125

Males M = 27.29 Females SD = 12.20

Answer 126

3.26 & 3.42

Answer 127

with parametric or non-parametric tests

Answer 128

bell shape

Answer 129

the mean μ

Answer 130

the mean (μ) and the standard deviation (σ).

Answer 131

normally distributed

Answer 132

μ = 0 is peak of distribution Block areas under the curve and gives us insight to way data is distributed and certain scores occuring if they belong to normally distribution e.g., 34.1% of values lie one SD below mean

Answer 133

SD above or below the mean of a particular score is

Answer 134

Take a value of participant (e.g., 56 years old) and take away mean of distribution (e.g., mean age of class is 23) divided by SD (class like 2)

Answer 135

a z score of 2 means the original score was 2 standard deviations above the mean

Answer 136

pecentiles

Answer 137

First calculating z score: graph shows that most people scored below 90. Since 90 is 2 standard deviations above the mean z = (90 - 80)/5 = 2 Z score to pecentile can be looked at table that z score of 2 is equivalent to the 97.7th percentle: The proportion of people scoring below 90 is thus .977 and proportion of people scoring above 90 is 2.3% (1-0.977)

Answer 138

an unbiased estimate of the population mean.

Answer 139

Via computing standard error of mean - smaller SEM the better

Answer 140

representative the mean was of the observed data.

Answer 141

most data points were close to the mean

Answer 142

widely spread from the mean.

Answer 143

computed by dividing the standard deviation of the sample by the the square root of the number in the sample

Answer 144

standard error of the mean more confident we can be that the sample mean is representative of the population.

Answer 145

as samples get large (usually defined as greater than 30), the sampling distribution has a normal distribution with a mean equal to the population mean, SD = SEM

Answer 146

SEM (standard error of the mean)

Answer 147

calculate boundaries and range of values within which we believe the true value of the population mean value will fall. Such boundaries are called confidence intervals.

Answer 148

these intervals (created by samples) will contain the population mean

Answer 149

95 of these samples, the confidence intervals we constructed would contain the true value of the mean in the population.

Answer 150

* Dots show the means for each sample * Lines sticking out representing Ci for the sample means * If there was a vertical line down it represents population mean * If confidence intervals don't overlap then it shows significant difference between the sample means

Answer 151

0 (it does not contain it) or 1 (it does contain it). You have no way of knowing which it is.

Answer 152

would be –1.96 and +1.96 -

Answer 153

-1.96 and 1.96

Answer 154

very different from the true mean, indicating that it is a bad representation of the population

Answer 155

smaller - make sense as more we measure more certain sample mean close to population mean

Answer 156

LB = Mean - (1.96 * SEM) UB = Mean + (1.96 * SEM)

Answer 157

*** M - 530 * N = 10 * SEM = 100/ square root of 10 = 31.62** * Value of z for 95% CI is number of SD one must go from mean (in both directions) to contain 0.95 of the scores * Value of 1.96 was found in z-table * Since each tail is to contain 0.025 of the scores, you find the values of z for which is 1-0.025 = 0.975 of the socres below * 95% of z scores lie between -1.96 and +1.96 *** Lower limit = 530 - (1.96) (31.62) = 468.02 * Upper limit = 530 + (1.96)(31.62) = 591.98**

Answer 158

signal/noise

Answer 159

probability of obtaining a certain value or p value.

Answer 160

systematic variation against unsystematic

Answer 161

the null hypothesis is rejected; When the null hypothesis is rejected, the outcome is said to be “statistically significant”

Answer 162

null hypothesis is not rejected.

Answer 163

think the variance accounted for by the model is larger than the one unaccounted for by the model (i.e. there is a statistically significant effect but in reality there isn’t)

Answer 164

think there was too much variance unaccounted for by the model (i.e. there is no statistically significant effect but in reality there is)

Answer 165

fit of the model

Answer 166

population, when in fact there isn’t.

Answer 167

a-level of usually 0.05

Answer 168

population when, in reality, there is.

Answer 169

β-level (often 0.2)

Answer 170

the size of the an effect

Answer 171

Standardized = comparable across studies Not (as) reliant on the sample size Allows people to objectively evaluate the size of observed effect.

Answer 172

the effect explains 1% of the total variance.

Answer 173

the effect accounts for 9% of the total variance.

Answer 174

effect accounts for 25% of the variance

Answer 175

effect should be placed within the research context.

Answer 176

.8, or an 80% chance of detecting an effect if one genuinely exists.

Answer 177

it may be because we do not have enough statistical power

Answer 178

correctly rejecting a false H0 OR the ability of the test to find an effect assuming there is one in the population,

Answer 179

1 - β OR probability of making Type II error

Answer 180

your sample sizee

Answer 181

1. Probability of a type 1 error or a-level [level at which we decide effect is sig - p-value) --> bigger [more lenient] alpha then more power) 2. True alternate hypothesis H1 [effect size] (degree of overlap, less means more power) - if you find large effect in lit then better chance of detecting something 3. The sampel size [N]) --> bigger the sample, less the noise and more power 4. The particular tests to be employed - parametric tests greater power to detect sig effect since more sensitive

Answer 182

Sample size calculation at a desired level of power (usually power set to 0.8 in formula)

Answer 183

* Calculate power of test * Calculate sample size necessary to detect an decent effect size and achieve a certain level of power based on past research

Answer 184

Type 1 error p = alpha Type II error p = beta Accepting null hypothesis which is correct - p = 1- alpha Accepting alternate hypo which is correct - p = 1 - beta

Answer 185

bigger difference means higher power and and correctly reject the null hypothesis than distributions that overlap more

Answer 186

This means that the overlap in distributions is smaller and the power is therefore greater, but this time because of a smaller standard error of our estimate of the means.

Answer 187

us how. We usually set the power to 0.8.

Answer 188

A measure of variability: The number of standard deviations from the population mean or a particular data point is Z-scores are a standardised measure, hence they ignore measurement units

Answer 189

Z-scores allow researchers to calculate the probability of a score occurring within a standard normal distribution Enables us to compare two scores that are from different samples (which may have different means and standard deviations)

Answer 190

Let’s say Trish takes a test and scores 25 and the mean is 20 You may calculate the z-score to be 1.25 you would use a z-score table to see what percentile they would be in (marked in red) so to read the table you would go down to the value 1.2 and you would go across to 0.05 which totals to 1.25 and you can see about 89.4% of other students performed worse.

Answer 191

We would use our table and look down the column to a z-score of 1 and across to the 0.00 column (in purple) and we can see 84.1% of students performed worse than Josh so Trish performed better than Josh.

Answer 192

68% of scores are within 1 SD of the mean, 95% are within 2 SDs and 99.7% are within 3 SDs.

Answer 193

: by taking into account the variability and size of our sample we can estimate how far away from the real population mean our mean is!

Answer 194

the 95% confidence interval range

Answer 195

high statistical power

Answer 196

low statistical power

Answer 197

missing a real effect – Type II error)

Answer 198

FALSE (or TRUE).

Answer 199

us finding an effect when the null hypothesis (H0) is true.

Answer 200

H0 is true

Answer 201

larger than the one we have found if there were no effect in the population (e.g. the null hypothesis were true)

Answer 202

p = .049, p = .050 are essentially the same thing- the former is ‘statistically significant’. Importance is dependent upon the experimental design/aims: e.g., A statistically significant weight increase of 0.1Kg between two adults experimental groups may be less important than the same increase between two groups of babies.

Answer 203

A as one-tailed is directional and two tailed is non-direcitonal

Answer 204

A as If we’d collected 100 samples, calculated the mean and then calculated a confidence interval for that mean, then for 95 of these samples the confidence intervals we constructed would contain the true value of the mean in the population

Answer 205

A and just because test statistic is sig does not mean its important effect

Answer 206

A as If we use the conventional criterion then the probability of this error is .05 (or 5%) when there is no effect in the population

Answer 207

A The standard error (which is the standard deviation of the distribution of sample means), defined as σ_Χ ̅ =σ/√N, decreases as the sample size (N) increases and vice versa

Answer 208

A The null hypothesis is the opposite of the alternative hypothesis and so usually states that an effect is absent

Answer 209

A A Type II error would occur when we obtain a small test statistic (perhaps because there is a lot of natural variation between our samples)

Answer 210

A - To make sure our estimates of the parameters that define our model and significance tests are accurate we have to assume homoscedasticity (also known as homogeneity of variance)

Answer 211

is the error

Answer 212

e.g., outcome 1 is equal to model plus error 1 and outcome 2 is equal to model plus error 2 and so on...

Answer 213

scaling (multiplying by a constant) another variable

Answer 214

pearson correlation or regression

Answer 215

captures the effect of the predictor variables we have manipulated or measured

Answer 216

average amount that the data cary from the mean

Answer 217

squared (s squared)

Answer 218

xi minus average of all scores of pp which is squared and divided by total number of participants minus 1 done for each participant (sigma)

Answer 219

average of the squared difference the outcome values from the mean of all outcomes (explaining what the formula of variance does)

Answer 220

one variable covarys with another

Answer 221

when one variable deviates from its mean we would expect the other variable to deviate from its mean in a similar way. So, if one variable increases then the other, related variable, should also increase or even decrease at the same level.

Answer 222

square root variance

Answer 223

1. Calculate the error between the mean and each subject’s score for the first variable (x). 1. Calculate the error between the mean and their score for the second variable (y). 1. Multiply these error values. 1. Add these values and you get the product deviations. 1. The covariance is the average product deviations

Answer 224

The answer is positive: that tells us the x and y values tend to rise together.

Answer 225

X = the value of ‘x’ variable Y = the value of ‘y’ variable X(line) = mean of ‘x’ - e.g., green Y(line) = mean of ‘y’ - e.g., blue n = the number of items in the data set

Answer 226

the mean for one variable

Answer 227

as one variable deviates from the mean, the other variable deviates in the same direction.

Answer 228

a negative covariance indicates that as one variable deviates from the mean (e.g. increases), the other deviates from the mean in the opposite direction (e.g. decreases).

Answer 229

dependent upon the units /scales of measurement used So covariance is not a standardised measure e.g., if 2 variables measured in miles and covariance is 4.25 then if we convert data to kilometres then we have to calculate covariance again and see it increases to 11. Dependence of scale measurement is a problem as can not compare covariances in an objective way --> can not say whether covariance is large or small to another data unless both data sets measured in same units So we need to STANDARDISE it.

Answer 230

To overcome the problem of dependence on the measurement scale, we need to convert the covariance into a standard set of units

Answer 231

dividing by product of the standard deviations of both variables.

Answer 232

Same formula of covariance but multipled of SD of x and SD of y

Answer 233

standard deviation for the number of adverts watched (sx) was 1.67, SD of number of packets of crisps bought (sy) was 2.92. If we multiply these together we get 1.67 × 2.92 = 4.88. .Now, all we need to do is take the covariance, which we calculated a few pages ago as being 4.25, and divide by these multiplied standard deviations. This gives us r = 4.25/ 4.88 = .87.

Answer 234

correlational coefficient or Pearson's r

Answer 235

standardised

Answer 236

Describes a relationship between variables If one variable increases, what happens to the other variable?

Answer 237

product-moment correlation

Answer 238

Pearson's r correlation coefficient

Answer 239

-1 and +1 (direction of relationship)

Answer 240

be with each other and the mean

Answer 241

there is unexplained variance in the data and results in the data points being more spread out.

Answer 242

* example of high negative correlation. The data points are close together and are close to the mean. * On the other hand, the graph on the right shows a low positive correlation. The data points are more spread out and deviate more from the mean.

Answer 243

between one variable and another hence its use in calculating effect size

Answer 244

two variablesare perfectly positively correlated, so as one variable increases, the other increases by a proportionate amount.

Answer 245

a perfect negative relationship: if one variable increases, the other decreases by a proportionate amount.

Answer 246

small effect

Answer 247

medium effect

Answer 248

large effect

Answer 249

correlation coefficient is different from zero (i.e., different from 'no relationship')

Answer 250

relationship that we have observed is statistically meaningful.

Answer 251

1. Z scores 2. T-statistic

Answer 252

likely correlation in the population

Answer 253

decreases e.g 20 n p is not < 0.05 but at 200 pps it is p < 0.05

Answer 254

indicates no linear relationship at all so if one variable changes, the other stays the same.

Answer 255

causality e.g., although we conclude no of adverts increase nmber of toffees bought we can't say watching adverts caused us to buy toffees

Answer 256

* Third variable problem - causality between variables can not be assumed in any correlation * Direction of causality: Correlation coefficients give nothing about which variables causes other to change.

Answer 257

significant

Answer 258

covariance to a measure of variance

Answer 259

coefficient of determination

Answer 260

proportion of the variance for a dependent variable )outcome) that's explained by an independent variable . (predictor)

Answer 261

19.4% of variability in exam performance can be explained by exam anxiety the variance in y accounted for by x’,

Answer 262

Multiply 0.1 * 0.1 for example

Answer 263

the correlation but without an indication of its direction.

Answer 264

1. Bivarate correlations 2. Partial correlations 3. Semi-partial or part correlations

Answer 265

relation between 2 variables

Answer 266

looks at the relationship between two variables while ‘controlling’ the effect of one or more additional variables.

Answer 267

the effect of one or more variables on either X or Y

Answer 268

* A correlation calculates each data points distance from line (residuals) * This is the error relative to the model (unexplained variance) * A third variable might predict some of that variation in residuals

Answer 269

unfiltiered variation of the other

Answer 270

third variable constant (but we don't manipulate these)

Answer 271

For example, when studying the effect of a diet, the level of exercise might also influence weight loss We want to know the unique effect of diet, so we need to partial out the effect of exercise

Answer 272

Partial Correlation between IV1 and DV = D / D+C Unique variance accounted for by the predictor (IV1) in the DV, after accounting for variance shared with other variables.

Answer 273

Partial correlation: Purple / Red + Purple If we were doing just a partial correlation, we would see how much exam anxiety is influencing both exam performance and revision time.

Answer 274

The partial correlation that we calculated took account not only of the effect of revision on exam performance, but also of the effect of revision on anxiety. If we were to calculate the semi-partial correlation for the same data, then this would control for only the effect of revision on exam performance (the effect of revision on exam anxiety is ignored).

Answer 275

control variable—a variable whose influence is statistically removed or controlled for when examining the relationship between the two primary variables (IV and DV).

Answer 276

relative to the amount of variance in the outcome that is left to explain after the contribution of other predictors have been removed from both the predictor and outcome.

Answer 277

we could look at the relationship between bladder relaxation (did the person wet themselves or not?) and the number of large tarantulas crawling up your leg controlling for fear of spiders (the first variable is dichotomous, but the second variable and ‘controlled for’ variables are continuous).

Answer 278

* . First, notice that the partial correlation between exam performance and exam anxiety is −.247, which is considerably less than the correlation when the effect of revision time is not controlled for (r = −.441). * . Although this correlation is still statistically significant (its p-value is still below .05), the relationship is diminished. * value of R2 for the partial correlation is .06, which means that exam anxiety can now account for only 6% of the variance in exam performance. * When the effects of revision time were not controlled for, exam anxiety shared 19.4% of the variation in exam scores and so the inclusion of revision time has severely diminished the amount of variation in exam scores shared by anxiety. * As such, a truer measure of the role of exam anxiety has been obtained.

Answer 279

other variables are ruled out

Answer 280

effect that the third variable has on only one of the variables in the correlation

Answer 281

Partials out the effect of one or more variables on either X or Y. e.g. The amount revision explains exam performance after the contribution of anxiety has been removed from the one variable (usually the predictor- e.g. revision).

Answer 282

unique variation of one variable with the unfiltered variation of the other.

Answer 283

* Semi-Partial Correlation between IV1 and DV = D / D+C+F+G Unique variance accounted for by the predictor (IV1) in the DV, after accounting for variance shared with other variables.

Answer 284

* purple/red + purple + white+ orange * When we use semi-partial correlation to look at this relationship, we partial out the variance accounted for by exam anxiety (the orange bit) and look for the variance explained by revision time (the purple bit).

Answer 285

A partial correlation quantifies the relationship between two variables while accounting for the effects of a third variable on both variables in the original correlation. A semi-partial correlation quantifies the relationship between two variables while accounting for the effects of a third variable on only one of the variables in the original correlation.

Answer 286

of bivariate correlation coefficients.

Answer 287

* Spearman's roh * Kendall's tau test

Answer 288

ordinal scale (e.g., grades)

Answer 289

first ranking the data n(numbers converted into ranks), and then running Pearson’s r on the ranked data

Answer 290

data have violated parametric assumptions such as nonnormally distributed data

Answer 291

Spearman's rho

Answer 292

proportion of variance in the ranks that two variables share.

Answer 293

when you have a small data set with a large number of tied ranks. This means that if you rank all of the scores and many scores have the same rank, then Kendall’s tau should be used

Answer 294

For small datasets, many tied ranks Better estimate of correlation in population than Spearman’s ρ

Answer 295

proportion of variance shared by two variables (or the ranks of those two variables).

Answer 296

tau is not comparable to r and r s

Answer 297

Kendall’s statistic is actually a better estimate of the correlation in the population we can draw more accurate generalizations from Kendall’s statistic than from Spearman’s.

Answer 298

* What type of measurement = continous * How many predictor variables = one * What type of continous variable = continous * Meets assumption of parametric tests - No

Answer 299

Pearson's correlation coefficient r output box

Answer 300

one of the two variables is dichotomous (e.g., example of dichotomous variable is women being pregnant or not)

Answer 301

depends on whether the dichotomous variable is discrete or continuous

Answer 302

one variable is a discrete dichotomy (e.g. pregnancy),

Answer 303

one variable is a continuous dichotomy (e.g. passing or failing an exam). e.g. An example is passing or failing a statistics test: some people will only just fail while others will fail by a large margin; likewise some people will scrape a pass while others will clearly excel.

Answer 304

* Imagine interested in relationship between gender of a cat and how much time it spent away from home * Time spent away is measured in interval level --> mets assumptions of parametric data * Gender is discrete dichotomous variable coded with 0 for male and 1 for female

Answer 305

biseral correlation coefficient

Answer 306

biserial correlation bigger than point biserial

Answer 307

The researchers was interested in whether the amount someone gets paid and amount of holidays they take from work, whether these two variables would be related to their productivity at work - Pay: Annual salary - Holiday: Number of holiday days taken - Productivity: Productivity rating out of 10

Answer 308

medium effect size ±.1 = small effect ±.3 = medium effect ±.5 = large effect

Answer 309

o This indicates very little correlation between the 2 variables

Answer 310

the relationship between all possible combinations of your variables

Answer 311

- For Pay and Holiday, we can see the line is very flat and indicates the correlation between the two variables is quite low - - For pay and productivity, the line is steeper suggesting the correlation is fairly substantial between these 2 variables and same for holidays and pay and productivity and holidays here

Answer 312

* - The relationship between pay and holidays is very low correlation is -0.04 * - Between pay and productivity, there is a medium size correlation of r = 0.313 * Between holidays and productivity there is medium going on large effect size of 0.435 * Relationship between pay and productivity and also holidays and productivity is sig but correlation with pay and holidays was not sig

Answer 313

A student was interested in the relationship between the time spent preparing an essay, the interestingness of the essay topic and the essay mark received. He got 45 of his friends and asked them to rate, using a scale from 1 to 7, how interesting they thought the essay topic was (1 - I'll kill myself of boredom, 4 - it's not too bad!, 7 - it's the most interesting thing in the world!) (interesting). He then timed how long they spent writing the essay (hours), and got their percentage score on the essay (essay).

Answer 314

* Interval scale: difference between 10 degrees C and 20 degrees is same as 80 F and 90 F, 0 degrees does not mean absence of temp * Ratio: Height as 0 cm means no weight and weight, time

Answer 315

one IV and one DV

Answer 316

D. There was a significant positive correlation between interestingness of topic and the amount of time spent writing, with a large effect size. There was a non-significant positive correlation between time spent writing an essay and essay mark There was a non-significant positive correlation between interestingness of topic and essay mark

Answer 317

in between small and medium effect

Answer 318

your own research area

Answer 319

one variable is dichotomous, but there is an underlying continuum (e.g. pass/fail on an exam)

Answer 320

When one variable is dichotomous, and it is a true dichotomy (e.g. pregnancy)

Answer 321

* example of a true dichotomous relationship. * We can compare the differences in height between males and females. * Use dichotomous predictor of gender

Answer 322

* Continous * Two or more predictors that are continous * Multiple regression * Meets assumptions of parametric tests

Answer 323

every extra predictor you include, you have to add a coefficient; so, each predictor variable has its own coefficient, and the outcome variable is predicted from a combination of all the variables multiplied by their respective coefficients plus a residual term

Answer 324

* Y is the outcome variable, * b1 is the coefficient of the first predictor (X1), * b2 is the coefficient of the second predictor (X2), * bn is the coefficient of the nth predictor (Xn), * εi is the difference between the predicted and the observed value of Y for the ith participant.

Answer 325

we seek to find the linear combination of predictors that correlate maximally with the outcome variable.

Answer 326

an outcome variable from one or more predictor variables

Answer 327

for more than 2 predictor (X) variables

Answer 328

bad model can't uniquely explain the outcome

Answer 329

one model explains significantly more variance than the other

Answer 330

past work and the experimenter decides in which order to enter the predictors into the model

Answer 331

known predictors (from other research) should be entered into the model first in order of their importance in predicting the outcome. After known predictors have been entered, the experimenter can add any new predictors into the model. New predictors can be entered either all in one go, in a stepwise manner, or hierarchically (such that the new predictor suspected to be the most important is entered first).

Answer 332

The first model allows all the shared variance between Ad budget and Album sales to be accounted for. The second model then only has the option to explain more variance by the unique contribution from the added predictor Plays on the radio.

Answer 333

method in which all predictors are forced into the model simultaneously.

Answer 334

good theoretical reasons for including the chosen predictors,

Answer 335

makes no decision about the order in which variables are entered.

Answer 336

this method is the only appropriate method for theory testing because stepwise techniques are influenced by random variation in the data and so rarely give replicable results if the model is retested.

Answer 337

This option is for obtaining collinearity statistics such as the VIF, tolerance, Checking assumption of no multicolinearity

Answer 338

simple regression requires only one predictor.

Answer 339

e.g., two predictors are perfectly correlated , have a correlation coefficient of 1

Answer 340

to obtain unique estimates of the regression coefficients because there are an infinite number of combinations of coefficients that would work equally well.

Answer 341

real-life data

Answer 342

interchangable

Answer 343

* Untrustory bs * Limit size of R * Importance of predictors

Answer 344

a correlation matrix of all of the predictor variables and see if any correlate very highly (by very highly I mean correlations of above .80 or .90)

Answer 345

predictor has a strong linear relationship with the other predictor(s).

Answer 346

look at your variables to see if you need to include all variables whether all need to go in model if high correlation between 2 predictors (measuring same thing) then decide whether its important to include both vars or take one out and simplify regression model

Answer 347

reciporal (1/VIF) = inverse of VIF

Answer 348

ZRESID on Y and ZPRED on X Plot of residuals against predicted to asses homoscedasticity

Answer 349

(the standardized predicted values of the dependent variable based on the model). These values are standardized forms of the values predicted by the model.

Answer 350

(the standardized residuals, or errors). These values are the standardized differences between the observed data and the values that the model predicts).

Answer 351

* basics means and also a table of correlations between variables. * This is a first opportunity to determine whether there is high correlation between predictors, otherwise known as multi-collinearity

Answer 352

variance in terms of R squared, and more importantly how R squared changes between models and whether those changes are significant.

Answer 353

measure of how much of the variability in the outcome is accounted for by the predictors

Answer 354

fit in the general population

Answer 355

assumption of independent errors is tenable (value less than 1 or greater than 3 raise alarm bells) value closer to 2 the better = assumption met

Answer 356

F-tests for each model

Answer 357

significantly beter at predicting the outcome than using the mean as a 'best guess'

Answer 358

improvement in prediction that results from fitting the model, relative to the inaccuracy that still exists in the model

Answer 359

improvement in prediction resulting from fitting a regression line to the data rather than using the mean as an estimate of the outcome

Answer 360

total difference between the model and the observed data

Answer 361

number of predictors (e.g., 1 for first model, 3 for second)

Answer 362

Number of observations (N) minus number of coefficients in regression model (e.g., M1 has 2 coefficents - one for predictor and one for constant, M2 has 4 - one for each 3 predictor and one for constant)

Answer 363

calculated for each term (SSM, SSR) by dividing the SS by the df. T

Answer 364

F-ratio is calculated by dividing the average improvement in prediction by the model (MSM) by the average difference between the model and the observed data (MSR)

Answer 365

greater than 1 and SPSS calculates exact prob (p-value) of obtaining value of F by change

Answer 366

there is a positive relationship between the predictor and the outcome,

Answer 367

represents a negative relationship between predictor and outcome variable?

Answer 368

Indicating positive relationships so as advertising budget increases, record sales increases (outcome) plays on ratio increase as do record sales attractiveness of band increases record sales

Answer 369

predictor affects the outcome if the effects of all other predictors are held constant:

Answer 370

(b = 0.085): This value indicates that as advertising budget (x) increases by one unit, record sales (outcome, y) increase by 0.085 units. This interpretation is true only if the effects of attractiveness of the band and airplay are held constant.

Answer 371

not dependent on the units of measurements of variables

Answer 372

the number of standard deviations that the outcome will change as a result of one standard deviation change in the predictor.

Answer 373

a better insight into the ‘importance’ of a predictor in the mode

Answer 374

both variables have a comparable degree of importance in the model

Answer 375

true (pop) value of b

Answer 376

value of b in this sample is close to the true value of b in the populatio

Answer 377

in some samples the predictor has a negative relationship to the outcome whereas in others it has a positive relationship

Answer 378

two best predictors (advertising and airplay) have very tight confidence intervals indicating that the estimates for the current model are likely to be representative of the true population values interval for attractiveness is wider (but still does not cross zero) indicating that the parameter for this variable is less representative, but nevertheless significant.

Answer 379

Pearson's correlation coefficients

Answer 380

represent the relationships between each predictor and the outcome variable, controlling for the effects of the other two predictors.

Answer 381

represent the relationship between each predictor and the outcome, controlling for the effect that the other two variables have on the outcome. representing the unique relationship each predictor has with otucome

Answer 382

unique variance in outcome (ignore all other predictors) explained by predictor divided by variance in outcome not explained by all other predictors A/A+E

Answer 383

unique variance in outcome explained by predictor divided by total variance in outcome A/A+B+C+E

Answer 384

may be biased

Answer 385

serious problem.

Answer 386

a potential problem

Answer 387

For our current model the VIF values are all well below 10 and the tolerance statistics all well above 0.2; therefore, we can safely conclude that there is no collinearity within our data.

Answer 388

summary of residuals statistics to be examined of extreme cases To see whether individual scores (cases) influence the modelling of data too much

Answer 389

less than -2 or greater than 2 (We expect about 5% of our cases to do tha and 95% to have standardised residuals within about +/- 2.)

Answer 390

10 cases (5% of 200)

Answer 391

* 99% of cases should lie within ±2.5 so expect 1% of cases lie outside limits * From cases listed, clear two cases (1%) lie outside of limits (case, 164 [investigate further has residual 3] and 179) - 1% which isconform to accurate model

Answer 392

broken the assumptions of the regression

Answer 393

investigate and potentially remove them because they are ‘outliers’

Answer 394

* Continous outcome variable and continous or dichotomous predictor variables * Independence = all values of outcome variable should come from different participant * Non-zero variance as predictors should have some variation in value e.g., variance ≠ 0 * No outliers * No perfect or high collinearity * Histogram to check for normality of errors * Scatterplot of ZRES against ZPRED to check for linearity and homoscedasticity = looking for random scatter * Independent errors (Durbin-Watson)

Answer 395

undue influence on a predictor’s b coefficient

Answer 396

the partial plot shows the strong positive relationship to album sales. There are no obvious outliers and the cloud of dots is evenly spaced out around the line, indicating homoscedasticity.

Answer 397

the plot again shows a positive relationship to album sales, but the dots show funnelling, There are no obvious outliers on this plot, but the funnel-shaped cloud indicates a violation of the assumption of homoscedasticity.

Answer 398

you cannot generalize your findings beyond your sample

Answer 399

transforming the raw data – but this won’t necessarily affect the residuals!

Answer 400

logistic regression instead

Answer 401

37.4% of the variance in productivity scores was accounted for by 3 predictor variables

Answer 402

if we assumed no relation between predictor variables and outcome variable – flat regression line no association between these variables)

Answer 403

holidays had standardized beta coefficient of 0.031 whereas cake had a much higher standardized beta coefficient of 0.499 which tells us that amount of cake given out much better predictor of productivity than the amount of holidays taken For pay we have a beta coefficient of 0.323 which tells us that pay was also a pretty good predictor in the model of productivity but slightly less than cake

Answer 404

- P value for holidays is 0.891 which is not significant - P value for cake is 0.032 is significant - P value for pay is 0.012 is significant

Answer 405

baseline not M1

Answer 406

change statistics

Answer 407

M2 explains an extra 7.5% which is sig

Answer 408

contribution of that predictor.

Answer 409

For this model, the advertising budget (t(196) = 12.26, p < .001), the amount of radio play prior to release (t(196) = 12.12, p < .001) and attractiveness of the band (t(196) =4.55, p < .001) are all significant predictors of record sales. From the magnitude of the t-statistics we can see that the advertising budget and radio play had a similar impact, whereas the attractiveness of the band had less impact.

Answer 410

we are talking about a variable with a infinante number of real numbers within a given interval so something like height or age

Answer 411

variable that can only hold two distinct values like male and female

Answer 412

line of best fit in MR

Answer 413

one or two outliers then could be okay

Answer 414

are over 3 SD from the mean

Answer 415

-3 and 3 SD

Answer 416

Weight, Activity, and the interaction between them are statistically significant

Answer 417

Homoscedasticity: similar variance of residuals (errors) across the variable continuum, e.g. equally accurate. Heteroscedasticity: variance of residuals (errors) differs across the variable continuum, e.g. not equally accurate

Answer 418

your distribution

Answer 419

* 0 = errors between pairs of obsers are pos correl * 2 = independent error * 4 = errors between pairs of observs are neg correl

Answer 420

1.5 and 2.5

Answer 421

‘generalizes’ to the entire population.

Answer 422

for small N and where results are to be generalized use the adjusted R2

Answer 423

1. Standard: To assess impact of all predictor variables simultaneously 2. Hierarchical: To test predictor variables in a specific order based on hypotheses derived from theory 3. Stepwise: If the goal is accurate statistical prediction from a large number of predictor variables – computer driven

Answer 424

* Tells that OCD interpretiotn of intrustrions would have not have a significant impact on model's ability to predict social anxiety Beta value of Interpretation of Intrusions is very small, indicating small influence on outcome variable Beta is the degree of change in the outcome variable for every 1 unit of change in the predictor variable.

Answer 425

When predictor variables correlate very highly with each other

Answer 426

Normality of residuals

Answer 427

The t-statistic is equal to the regression coefficient divided by its standard deviation

Answer 428

The residual error in the prediction of fear scores when both gender and fantasy proneness are included as predictors in the model.

Answer 429

The improvement in the prediction of depression by fitting the model

Answer 430

Somewhere between −3.369 and −0.517

Answer 431

Stress from research

Answer 432

As stress from teaching increases by one unit, burnout decreases by 0.36 of a unit.

Answer 433

No, because the errors show heteroscedasticity.

Answer 434

Note that you expect 1% of cases to lie outside this area so in a large sample, if you have one or two, that could be ok

Answer 435

A record company boss was interested in predicting album sales from advertising. Data 200 different album releases Outcome variable: Sales (CDs and Downloads) in the week after release Predictor variables The amount (in £s) spent promoting the album before release Number of plays on the radio

Answer 436

observed values of the outcome, and the values predicted by the model.

Answer 437

Difference between no predictors and model 1 (a). Difference between model 1 (a) and model 2 (b). Our model 2 is significantly better at predicting the value of the outcome variable than the null model and model 1 (F (2, 197) = 167.2, p<.001) and explains 66% of the variance in our data (R2=.66

Answer 438

y = 0.09x1 + 3.59x2 + 41.12 For every £1,000 increase in advertising budget there is an increase of 87 record sales (B = 0.09, t = 11.99, p<.001). For every number of plays on Radio 1 per week there is an increase of 3,589 record sales (B = 3.59, t = 12.51, p<.001).

Answer 439

o R squared = 0.09 o F statistic = 22.54 o P value = p < 0.001

Answer 440

D - data poiints show random pattern

Answer 441

A -->The R square change in step 2 was .020,

Answer 442

A fashion student was interested in factors that predicted the salaries of catwalk models. He collected data from 231 models. For each model he asked how much they earned per day (salary), their age (age), and how many years they had worked as a model (years_modelling). The student wanted to know if the number of years spent modelling predicted the models' salary after the models' age was taken into account.

Answer 443

Somewhere between 3.369 and 0.517

Answer 444

1. One-samples t-test 2. Paired t-test 3. Independent t-test

Answer 445

Compares the mean of the sample data to a known value

Answer 446

* DV = Continous (interval or ratio) * Independent scores (no relation between scores on test variable) * Normal distribution via frequency histogram (normal shape) and Q-plot (straight line) and non significant Shaprio Wilk * Homogenity of variances

Answer 447

Is the average IQ of Psychology students higher than that of the general population (100)? A particular factory's machines are supposed to fill bottles with 150 millilitres of product. A plant manager wants to test a random sample of bottles to ensure that the machines are not under- or over-filling the bottles.

Answer 448

1. Independence. – no relationship between the groups 2. Normal distribution via frequency histogram (normal shape) and Q-plot (straight line) and non significant Shaprio Wilk 3. Equal variances 4. Homogeneity of variances (i.e., variances approximately equal across groups) via non significant Levene's test 5. DV = Interval or continuous 6. IV = Categorical 7. No significant outliers

Answer 449

Do dog owners in the country spend more time walking their dogs than dog owners in the city?

Answer 450

DV is continuous Related samples: The subjects in each sample, or group, are the same. This means that the subjects in the first group are also in the second group Normal distribution via frequency histogram (normal shape) and Q-plot (straight line) and non significant Shaprio Wilk

Answer 451

Do cats learn more tricks when given food or praise as positive feedback?

Answer 452

1. What sort of measurement = continous 2. How many predictor variables = one 3. What type of predictor variables = categorical 4. How many levels of categorical predictor = two 5. Same or different participants for each predictor level = same

Answer 453

1. What sort of measurement = continous 2. How many predictor variables = one 3. What type of predictor variables = categorical 4. How many levels of categorical predictor = two 5. Same or different participants for each predictor level = different

Answer 454

predicting an outcome based on membership of two groups

Answer 455

linear model

Answer 456

degrees of freedom - related to the sample size.

Answer 457

lower degrees of freedom (small N studies) increased uncertainty and a higher likelihood of observing extreme values than large N studies with less heavy tails as t distribution goes to normal

Answer 458

When 2 experimental conditions and different participants are assigned to each conditiont

Answer 459

independent-samples t-test

Answer 460

Used when there are 2 experimental conditions and same participants took part in both conditions of the experiment

Answer 461

Matched pairs or paired samples t-test

Answer 462

there was no effect (i.e., null hypothesis was true)

Answer 463

error in the model

Answer 464

0 - expect differences between sample group means we colelcted to be different to 0

Answer 465

null hypothesis is rejected and two sample means differ because of experimental manipulation

Answer 466

parametric tests

Answer 467

* Sampling distribution is normally distributed - in paired it means sampling distribution of differences of scores is normal not the socres itself! * Data measured at least interval level

Answer 468

* Variances in populations are roughly equal (homegenity of variance) = Leven's test * Scores are independent since they come from different people

Answer 469

* Compares mean differences betwen our samples (--D) to the differences we would expect to find between population means (uD) which is divided by standard error of differences (sD / square root N) * If H0 is ture, then expect no difference between population means hence uD = 0

Answer 470

pairs of samples from a population have similar means to population

Answer 471

that sample means can deviate quite a lot from the populatio mean and sampling distribution of differences is more spread out

Answer 472

systematic variation in the data (represents experimental effect)

Answer 473

the difference we observed in our sample is not a chance result and caused by experimental manipulation

Answer 474

SD divided by square root of sample size

Answer 475

Standard deviation of differences divided by square root of sample size

Answer 476

ratio of systematic variation in experiment (average difference D) and unsystematic variation (standard erro of differences)

Answer 477

If the experimental manipulation creates any kind of effect,

Answer 478

If the experimental manipulation is unsuccessful then we might expect the variation caused by individual differences to be much greater than that caused by the experiment

Answer 479

critical value then conflict if reflects an effect in our IV

Answer 480

people doing well in first exam likely doing well in second exam regardless of condition they are in and significantly correlated (r= 0.664)

Answer 481

t(19) = 2.72, p = 0.012

Answer 482

First condition had smaller mean than second condition

Answer 483

* 95% of the samples (e.g., if we had 100 samples then 95 of those samples..) the constructucted CIs contain true value (population) of the mean difference * CIs tell us boundaries within which true mean difference is likely to lie * The true value of mean difference is unlikely to be 0 if Cis does not contain 0

Answer 484

Using cohen's D

Answer 485

Minus big mean from small mean divided by smallest SD (control group)

Answer 486

difference between groups is a 1/5 of SD

Answer 487

calculate effect size r (above 0.50 is large effect) by converting t-value to r-value

Answer 488

contain an equal number of people

Answer 489

other sources of variance (such as individual differences between participants' motivation, IQ etc..)

Answer 490

scores came from same participant and so individual differences were eliminated

Answer 491

* We are looking at differences between the overall means of 2 samples and compare with differences we would expect to get between means of 2 populations from which sampels come from * If H0 is true, samples drawn from same population * Therefore under H0, u1 = u2 therefore u1 - u2 = 0

Answer 492

sample group

Answer 493

variance of the sampling distribution is equal to the sum of the variances of the two populations from which the samples were taken

Answer 494

standard error for two samples

Answer 495

pooled variance estimate t-test

Answer 496

differnece in sample size by weighting the variance of each sample

Answer 497

Each variance of sample is multipled by its DF and added together and divided by the sum of weights (sum of two DFs) Larger samples better than small ones as close to population

Answer 498

number of degrees of freedom (N-1)

Answer 499

maximum value we would expect to get by chance alone in t distribution with same DFs

Answer 500

Sleep condition scored an average exam score of 66.200 and no sleep condition earned an average of 58.73 Effect size (Cohen's D) = Mean of sleep minus mean of no sleep divided by standard deviation of sleep (control grp) = 66.20-58/73/7.12

Answer 501

we got equal variance across the groups or whether the variances are unequal

Answer 502

no statistically significant difference in variances between the two groups - report results from equal variances assumed

Answer 503

variances between the 2 groups are different and they are statistically significantly different - report data from equal variances not assumed

Answer 504

* Levene's test is not significant (p = 0.970) so no stats sig differences in variance between two groups * t(28) = 2.87, p = 0.008

Answer 505

Paired t-t ests

Answer 506

unsystematic variance

Answer 507

Wilcoxon signed rank test

Answer 508

Wilcoxon rank sum test and Mank Whitney test

Answer 509

homogeneity of variance as assessed by Levene's Test for Equality of Variances (F = 1.58, p = .219)

Answer 510

Large effect

Answer 511

Research question: Which of the two diet formulas is better for puppies? Sample: 15 were randomly assigned to each of the two diets (A and B). Dependent variable: Average daily weight gain (ADG, g/day) between 12 to 28 weeks of age. Hypotheses: Ho: µA = µB Ha: µA ≠ µB. Statistical Test: Two samples independent t-test Significance level: .05

Answer 512

boxplots - no outlier here

Answer 513

histogram, q-qplot and tests of normality

Answer 514

We don’t have sig values for either group in the test of normality, histogram and plots look normal So we have normality of distribution for both independent groups Inspection of Q-Q Plots and the non-significant Shapiro-Wilk tests (p > .05) indicate that the ADG is normally distributed for both groups

Answer 515

levene's test

Answer 516

was homogeneity of variance as assessed by Levene's Test for Equality of Variances (F = 1.58, p = .219)

Answer 517

This study found that puppies in diet B had statistically significantly higher average daily weight gain (89.29 ± 9.93 g/day) between 12 and 28 weeks of age compared to puppies in diet A (60.20 ± 6.85 g/day), t(27)= -9.24, p < .001.

Answer 518

1. Pooled SD (over conditions) 2.Averaged SD 3. Control group SD

Answer 519

control group SD

Answer 520

d = (89.29 - 60.20) / 6.85 d = 4.25

Answer 521

d = 0.2 be considered a 'small' effect size, d = 0.5 represents a 'medium' effect size d = 0.8 a 'large' effect size

Answer 522

Analysis of Variance

Answer 523

Q: What sort of measurement? A: Continuous Q:How many predictor variables? A: One Q: What type of predictor variable? A: Categorical Q: How many levels of the categorical predictor? A: More than two Q: Same or Different participants for each predictor level? A: Different

Answer 524

if you are comparing more than 2 groups in IV

Answer 525

Which is the fastest animal in a maze experiment - cats, dogs or rats?

Answer 526

Doing separate t-tests inflates the type I error (false positive - e.g., pregnant man) The repetition of the multiple tests adds multiple chances of error, which may result in a larger α error level than the pre-set α level - Family wise error

Answer 527

This error rate across statistical tests conducted on the same experimental data

Answer 528

type 1 error

Answer 529

probability of making a wrong decision in accepting the alternate hypothesis = type 1 error

Answer 530

* 5% of type 1 error of falsely rejecting H0 * Probability of no. of Type 1 errors is 95% for a single test * However, for multiple tests the probability of type 1 error decreases as 3 tests together => 0.95*0.95*0.95 = 0.857 * This means probability of a type 1 error increases: 1- 0.857 = 0.143 (14.3% of not making a type 1 error)

Answer 531

ANOVA - 3 levels of categorical variable with dummy variables

Answer 532

all group means are equal

Answer 533

F statistic or F ratio

Answer 534

amount of systematic variance in data to the amount of unsystematic variance i.e., ratio of model to its error

Answer 535

overall experimental effect tells whether experimental manipulation was successful

Answer 536

groups were affected due to experimental manipulation

Answer 537

multiple regression equation for three means and models acocunt for 3 levels of categorical variable with dummy variables

Answer 538

3 or more independent groups

Answer 539

Levene's test

Answer 540

Leven's test is non-significant so equal variances are assumed

Answer 541

F(2,42) = 5.94, p = 0.005, eta-squared = 0.22

Answer 542

Between groups sum of squares divided by total sum of squares

Answer 543

830.207/3763.632 = 0.22 22% of the variance in exam scores is accounted for by the model

Answer 544

1. 0.01 = small effect 2. 0.06 = medium effect 3. 0.14 = large effect

Answer 545

then use statistics in Welch or Brown-Forsythe test

Answer 546

statistics you get and affect if p value is sig or not

Answer 547

* Full sleep vs partial sleep, p = 1.00, not sig * - Full sleep vs no sleep , p = 0.007 so sig * - Partial sleep vs no sleep = p = 0.032 so sig

Answer 548

Mean of all scores regardless pp's condition

Answer 549

difference of the participant’s score from the grand mean squared and summed over all participants

Answer 550

difference of the model score from the grand mean squared and summed over all participants

Answer 551

difference of the participant’s score from the model score squared and summed over all participants

Answer 552

explained by the model and amount of variation caused by extraneous factors

Answer 553

DF to calculate them

Answer 554

number of group (parameters), k,

Answer 555

total sample size, N, minus the number of groups, k

Answer 556

* MST = SST (N-1) * MSR = SSR (N-k) * MSM = SSM/k

Answer 557

exp manipulation explains

Answer 558

average amount of variation explained by the model (e.g. the systematic variation),

Answer 559

average amount of variation explained by extraneous variables (the unsystematic variation).

Answer 560

non-significant effect

Answer 561

F ratio is less than 1 means that MSR is greater than MSM = more unsystematic than systematic

Answer 562

indicates that experimental manipulation had some effect above and beyond effect of individual differences in performance Does not tell us whether F-ratio is large enough to not be a chance result

Answer 563

MSM is greater than MSR

Answer 564

compare the obtained value of F against the maximum value we would expect to get by chance if the group means were equal in an F-distribution with the same degrees of freedom

Answer 565

by chance . Low degrees of freedom result in long tails of the distribution, so much like other statistics large values of F are more common to crop up by chance in studies with low numbers of participants.

Answer 566

differences between groups lie

Answer 567

that one or more of the differences between means is statistically significant (e.g. either b2 or b1 i statistically significant)

Answer 568

which groups differ

Answer 569

non-normality

Answer 570

affected by skew, and non-normality also affects the power of F in quite unpredictable ways

Answer 571

violations of normality

Answer 572

* Planned contrasts * Post-hoc tests

Answer 573

* compare all pairwise differences in mean * Used if no specific hypotheses concerning differences has been made

Answer 574

* because every pairwise combination is considered the type 1 error rate increases, so normally the type 1 error rate is reduced by modifying the critical value of p

Answer 575

two-tailed

Answer 576

One-tailed hypothesis

Answer 577

Bonferroni correction, which divides the standard critical value of p=0.05 by the number of pairwise comparisons performed

Answer 578

hypothesis

Answer 579

pairwise difference so are not penalized as heavily as post hoc tests that do test for every difference

Answer 580

data is collected

Answer 581

never used again

Answer 582

k (number of groups) minus 1

Answer 583

Coefficients add to 0 for each contrast (-2 + 1 +1) and once group used alone in contrast then enxt contrasts set coefficient to 0 (e.g., -2 to 0)|

Answer 584

quadratic, cubic and quartic

Answer 585

lacks statistical power (probability of type II error will be high [ false negative]) so increasing chance of missing a genuine difference in data

Answer 586

Use REGWQ or Tukey as good power and tight control over Type 1 error rate

Answer 587

Gabriel’s procedure because it has greater power,

Answer 588

if sample sizes are very different use Hochberg’s GT2

Answer 589

Games-Howell

Answer 590

Bonferroni

Answer 591

* Linear trend as dose of Viagra increases so does mean level of libido * Error bars overlap indicating no between group differences

Answer 592

SSR (unsystematci variation)

Answer 593

SSM (systematic variation)

Answer 594

* Linear trend is significant (p = 0.008) * Quadratic trend is not significant (p = 0.612)

Answer 595

with a negative weight

Answer 596

the table of weights shows that contrast 1 compares the placebo group against the two experimental groups, contrast 2 compares the low-dose group to the high-dose group

Answer 597

Signifiance value given in table is two-tailed and since hypothesis one-tail we divide by 2 for contrast 1, we can say that taking Viagra significantly increased libido compared to the control group (p = .0029/2 = 0.0145) . The significance of contrast 2 tells us that a high dose of Viagra increased libido significantly more than a low dose (p(one-tailed) = .065/2 = .0325)

Answer 598

Bonferroni Tukey

Answer 599

* Independence of data * DV is continuous; IV categorical (3 groups) * No significant outliers; * DV approximately normally distributed for each category of the IV * Homogenity of variance = Levene's test not significant

Answer 600

type 1 error

Answer 601

A differences between means of groups containing different participants when sampling distribution is normal and the groups have equal variances and data are at least interva

Answer 602

D All of these are correct

Answer 603

C As the DF increase, the distribution becomes closer to normal

Answer 604

CIt is the standard deviation of the sampling distribution of a statistic

Answer 605

BFemales and males did not significantly differ in the time spent using email,t(7.18) = –1.90,p= .099

Answer 606

CHas less power to find an effect.

Answer 607

The experimental groups are represented by a binary variable (i.e. code 1 and 0)

Answer 608

C (multiple) Regression or ANOVA (independent) as regression and ANOVA is the same Did not mention the hypothesis of prediction or it would be regression Chi-square only used when you have one categorical predictor and outcome is categorical

Answer 609

Type of intervention had a significant effect on levels of exam performance, F(4, 29) = 12.43, p < .01.

Answer 610

C. At least two of the stimulants will have different effects on the mean time spent awake

Answer 611

D. large; low

Answer 612

The researcher should accept as statistically significant tests with a probability value of less than 0.016 to avoid making a Type I error

Answer 613

D. The treatment groups had a significant effect on the depression levels,F(2, 26.44) = 4.35.

Answer 614

C. Bonferroni

Answer 615

ANSWER 1 - sum of all weights should be 0

Answer 616

Is there a statistically significant difference in Frisbee throwing distance with respect to education status IV = Education with 3 levels = high school, graduate, postgrad DV = Frisbee throwing distance

Answer 617

There was homogeneity of variance as assessed by Levene's Test for Equality of Variances (F (2,47) = 1.94, p = .155)

Answer 618

There was a statistically significant difference between groups as demonstrated by one-way ANOVA (F(2, 47) = 3.50, p = .038).

Answer 619

A Tukey post hoc test shows that the PostGrad group was able to throw the frisbee statistically significantly further than the High School group (p = .034). There was no statistically significant difference between the Graduate and High School groups (p = . 691) nor between the Graduate and PostGrad groups (p = .099).

Answer 620

IV = 1 predicto Categorical with more than 2 levels DV = 1 Continous

Answer 621

between subject

Answer 622

part of the main experimental manipulation but have an influence on the dependent variable, are known as covariates and they can be included in an ANOVA analysis.

Answer 623

When we measure covariates and include them in an analysis of variance

Answer 624

covariates

Answer 625

then we can see what effect an IV has after the effect of covariate We partial out the effect of covariate

Answer 626

* To reduce within-group error variance = if we can explain unexplained variance , SSR, in terms of other variables (covariates)then reduce SSR to accurately assess effects of SSM * Elimination of confoundd = remove bias of unmeasured variables that confound results and influence DV

Answer 627

* Independence of the covariate and treatment effect * Homogeneity of regression slopes

Answer 628

independent from the experimental/treatment effect - (IVs - categorical predictors) ( ANCOVA assumption)

Answer 629

experimental effect is confounded with the effect of covariate = interpretation of ANCOVA is compromised

Answer 630

experimental effect

Answer 631

entire dataset and ignore which groups pps fit in

Answer 632

the relationship between the outcome (dependent variable) and covariate differs across the groups then the overall regression model is inaccurate (it does not represent all of the groups).

Answer 633

imagine plotting a scatterplot for each experimental condition with the covariate on one axis and the outcome on the other and calculate its regression line

Answer 634

- exhibits the same slopes for control and 15 minute group

Answer 635

* 30 minutes of therapy exhibts a different slope compared to others

Answer 636

* ANCOVA * Independent samples-design * One IV , two conditions, interval regime and steady state * One covariate (age in years) * One DV (Race time)

Answer 637

* Age F(1,27) = 5.36, p = 0.028, partial eta-squared = 0.17 (large and sig main effect) * Regime F(1,27) = 4.28, p = 0.048, partial eta-squared = 0.14 (large and sig main effect)

Answer 638

DF for age and DF for error

Answer 639

η2 = 0.01 indicates a small effect. η2 = 0.06 indicates a medium effect. η2 = 0.14 indicates a large effect

Answer 640

* Interval has a marginal mean of race times of 56.57 * Steady state has a marginal mean of race times 62.97 * Estimated marginal means partialled out the effects of age and view mean scores of race times in interval and steady state if mean age scores (30.07) across two groups was held constant

Answer 641

* Interaction effect of regime * age has a p-value of 0.980 * Since p-value is not significant the assumption of homogeneity of regression slopes has been met

Answer 642

relationship between covariate and DV differ significantly between two groups or many groups you got and assumption is not satisfied

Answer 643

IV (e.g., regime) and covariate (e.g., age) in DV instead of covariate box

Answer 644

* P-value is not signifcant (p=0.528) so effect of variable age is not sig difference of age across training regime * and so independent variable are assumed to be independent.

Answer 645

f the b-value for the covariate is positive then it means that the covariate and the outcome variable have a positive relationship If the b-value is negative it means the opposite: that the covariate and the outcome variable have a negative relationship

Answer 646

* b for covariate is 0.416 * Besides other things being equal, if a a partner’s libido increases by one unit, then the person’s libido should increase by just 0.416 units * Since b is positive then partner's libido ahs pos relation with pps's libido

Answer 647

N - p -1 N is total sample size, p is number of predictors (2 dummy variables and covariate )

Answer 648

* Tukey LSD with no adjustments (not reccomended) * Bonferroni correction (reccomended) * Sidak correction

Answer 649

Bonferroni correction

Answer 650

Bonferroni correction

Answer 651

loss of power associated with Bonferroni corrected values.

Answer 652

* Contrast 1 of comparing level 2 (low dose) against level 1 (placebo) is significant (p = 0.045) * Contrast 2 of comparing level 3 (high dose) with level 1 (placebo) is significant (p - 0.010)

Answer 653

* The significant difference between the high-dose and placebo groups remains (p = .030) * high-dose and low-dose groups do not significantly differ (p = .93) * Low dose and placebo groups do not significantly differ (p value = 0.130)

Answer 654

For placebo and low dose there appears to be a positive relationship between pp's libido and that of their partner However, in the high-dose condition there appears to be no relationship at all between participant’s libido and that of their partner - shows negative relationship Doubts whether homogenity of regression slopes is satisfied as not all the slopes are the same (go same direction)

Answer 655

* eta-squared * partial-eta squared (ANCOVA) * omega squared = used when equal numbe of pps in each grp * r

Answer 656

Dividing the effect of interest SSM by total variance in the data SST

Answer 657

SS Effect/ SS Effect + SS Residual

Answer 658

This differs from eta squared in that it looks not at the proportion of total variance that a variable explains, but at the proportion of variance that a variable explains that is not explained by other variables in the analysis

Answer 659

* ANCOVA * ANCOVA is conducted to determine i f there is a statistically significant difference between different studying techniques (IV) on exam score (DV) after controlling for current grade (covariate)

Answer 660

IV, DV and covariate

Answer 661

covariate factor(s)

Answer 662

correlates with outcome DV but not with IV

Answer 663

baseline pre-test scores can be used as a covariate to control for inital grp differences on test performance

Answer 664

* IVs are categorical * Covariates are metric (quantiatively) independent of IV * DV is metric

Answer 665

1 DV: Continous 2 predictor variables with 2 levels or more that are categorical and continous

Answer 666

infinite number of possible values variables can take on e.g., interval = equal intervals on variable represent equal difference measured like diff between 600ms and 800ms is = difference between 1300ms and 1500ms e.g., ratio = same as interval but clear definition of 0 like height or weight

Answer 667

A variable that cannot take on all values within the limits of the variable - entities are divided into distinct categories e.g., nominal = 2 or more caegories e.g., whether someone is vegan or vegetarian e.g., ordinal categories have order like people who got fail, pass, merit or distinction

Answer 668

Independence of the covariate and treatment effect means that the categorical predictors and the covariate should not be dependent on each other

Answer 669

Homogeneity of regression slopes means that the covariate has a similar relationship with the outcome measure, irrespective of the level of the categorical variable - in this case the group

Answer 670

There are alternative, a bit more advanced, methods to account for such differences as they are not, in general, uninteresting, but for the ANCOVA analysis they do present an issue

Answer 671

Quote df for the effect and error, e.g. 2,26

Answer 672

The group means can be recalculated once the effect of the covariate is ‘discounted’ = impact of covariate is taken into account and adjusted into each level of predictor variable in mean column These values can differ markedly from the original group means and help with interpretation.

Answer 673

1. Control for Covariances (continuous variables you may not necessarily want to measure) 2. Study combinations of categorical and continuous variables – covariate becomes the variable of interest rather than the one you control

Answer 674

A three-way ANCOVA was conducted to determine a statistically significant difference between different study techniques on students exam scores after controlling for their current grades.

Answer 675

Independent variables should be categorical variables. The dependent variable and covariate should be continuous variables (measured on an interval scale or ratio scale.) Make sure observations are independent - don’t put people into more than one group. Normality: the dependent variable should be roughly normal for each of category of independent variables. Data (and regression slopes) should show homogeneity of variance. The covariate and dependent variable (at each level of independent variable) should be linearly related. Your data should be homoscedastic The covariate and the independent variable shouldn’t interact. In other words, there should be homogeneity of regression slopes.

Answer 676

Analysis of covariance (ANCOVA)

Answer 677

Natural Fear Level

Answer 678

D since baseline levels of stress used as covariate and use this as a control when looking at impact treatment has had over 3 month assessment Not B since grps allocated based on baseline levels of stress (covariate and IV correlated - problematic) and A and C is one-way independent ANOVA

Answer 679

- IV: Group - DV: Hangover - Covariate: Drunk

Answer 680

Q: What sort of measurement? A: Continuous Q:How many predictor variables? A: Two or more Q: What type of predictor variable? A: Categorical Q: How many levels of the categorical predictor? A: Not relevant Q: Same or Different participants for each predictor level? A: Different

Answer 681

ANOVA and ANCOVA

Answer 682

as you add more variables to the model, the proportion explained by any one variable will automatically decrease.

Answer 683

Sum of squares between (squares of effect M) divided by sum of squared total (squares of everything - effects, errors and interactions)

Answer 684

more than one IV

Answer 685

Independent Factorial ANOVA

Answer 686

When experiment has two or more IVs

Answer 687

1. Independent factorial design 2. Repeated-measures (related) factorial design 3. Mixed design

Answer 688

* There is many IVs or predictors that each have been measured using different pps (between grps)

Answer 689

* Many IVs or predictors have been measured but same pps used in all conditions

Answer 690

* Many IVs or predictors have been measured; some measured with diff pps whereas others used same pps

Answer 691

Independent factorial design

Answer 692

When we use ANOVA to analyse a situation in which there is two or more IVs

Answer 693

A one-way ANOVA has one independent variable, while a two-way ANOVA has two.

Answer 694

IV = Alcohol - 3 levels = Placebo, Low dose, High dose Iv = face type 2 levels = unattractive, attractive DV = Physical attractiveness score

Answer 695

linear model

Answer 696

* The first equation models the two predictors in a way that allows them to account for variance in the outcome separately, much like a multiple regression model * The second equation adds a term that models how the two predictor variables interact with each other to account for variance in the outcome that neither predictor can account for alone. * The interaction is important to us because it tests our hypothesis that alcohol will have a stronger effect on the ratings of unattractive than attractive faces

Answer 697

We follow the same routine , similar to one-way ANOVA, to compute sums of squares for each factor of the model (and their interaction) and compare them to the residual sum of squares, which measures what the model cannot explain

Answer 698

, we still find the total sum of squared errors (SST) and break this variance down into variance that can be explained by the experiment (SSM) and variance that cannot be explained (SSR).

Answer 699

in two-way ANOVA, the variance explained by the experiment is made up of not one experimental manipulation but two. Therefore, we break the model sum of squares down into variance explained by the first independent variable (SSA), variance explained by the second independent variable (SSB) and variance explained by the interaction of these two variables (SSA × B)

Answer 700

sum of all grps (pairing each level of IV with another) n = number of scores in each grp which is multipled by the mean value of each group subtracted by grand mean of all pps regardless of grp squared

Answer 701

placebo + attractiveness placebo + untractiveness low dose +attractiveness low dose + unattractiveness high dose +attractiveness high dose +unattractiveness - 6 grps

Answer 702

considering only two groups at a time and add together - for first IV variable (SSA) (e.g., grps of pps rated attractive and grp of pps that rated unattractive) number of pps in that grp multiplied by mean of grp subtracted by grand mean overall of all pps squared

Answer 703

DF = (g-1) so if male and female then 2 -1 = 1

Answer 704

same formula as SSA but for the second IV added for all grps of pps in second IV number of pps in one grp of secondIV(mean score of that grp subtract by grand mean of all pps regardless of grp) squared

Answer 705

number of grps in second IV minus 1

Answer 706

by the interaction of 2 variables

Answer 707

SS A X B = SSM - SSA - SSB

Answer 708

df A X B = df M - df A - df B

Answer 709

individual differences in performance or the variance that can’t be explained by factors that were systematically manipulated.

Answer 710

* use individual variances of each grp (e.g., attractiveness face type + placebo) and multiply by one less than number of people within the group (n - in this case 6) and do it for each group and add it together

Answer 711

number of grps you have in study(number of scores you have per group minus 1)

Answer 712

* Partial eta-squared * Omega-squared if advised

Answer 713

* There is not a simple non-parametric counterpart of factorial ANOVA * If assumption of normality is violated then use robust methods described in Wilcox's and files in R * If assumptions of homogenity of variance then implement corrections based on Welch procedure

Answer 714

- Independent samples design - Two Ivs, both 2 conditions: drug type (A, B) and onset (early, late) - One DV is cognitive performance - Two way ANOVA

Answer 715

- The levene’s test is not significant so assume equal variances

Answer 716

steps taken to equalise variances through data transformation

Answer 717

- Drug : F(1,24) = 5.58, p = 0.027, partial eta-squared = 0.19 (large effect + sig effect) - Onset: F(1,24) = 14.43, p = 0.001, partial eta-squared = 0.38 (large effect + sig effect) - Interaction Drug * Onset: F(1,24) = 9.40, p = 0.005, partial eta-squared = 0.28 (large effect + sig effect) - We got two sig main effects and sig interaction effect which are all quite large effect sizes

Answer 718

drug B has higher score on cognitive test than A and is sig main effect (CI does not contain 0 and also main effect analysis) early onset scoring higher on average than late onset (CI does not contain 0 and also main effect analysis) Important of these main effect as main effects ignoring the effec tof other IV so results for drug at top is regardless of whether late/onset for example , does not tell anything for interaction

Answer 719

* Blue line is early onset * Green line is late onset * For late onset, drug B lead to higher mean scores on test than drug A * For early onset, drug A led to slightly higher mean scores than drug B * Drug A more effective then drug b for early onset but different marginal * Drug B was substantially more effective than Drug A for late

Answer 720

sig interaction effect

Answer 721

* looks at the effect of one IV at individuals levels of other IV * Seeing whether differences margina/substantial is sig

Answer 722

variance explained by the first independent variable (SSA), variance explained by the second independent variable (SSB ) and variance explained by the interaction of these two variables (SSA × B ).

Answer 723

* One-way ANOVA have one IV categorical variable (level of educaiton - college degree, grad degree, high school) * Two-way ANOVA , you have 2 categorical IV variables - level of education (college degree, grad degree, high school) and zodaic sign (libra, pisces)

Answer 724

1 DV and 2 or more categorical predictors

Answer 725

Two-way independent ANOVA

Answer 726

IV = 3 different types anxiety medications and control grp DV: Anxiety levels after treatment of grps Covariate = anxiety before treatment, depression levels ANCOVA

Answer 727

* IV: Level of education - school, college or uni edu and gender (m, f) * DV: Political interest in questionnaire * Two-way independent ANOVA

Answer 728

* IV: Gender (m,f) , number of hrs spent practicisng * DV: Level of muscial skill after a year * Two-way independent ANOVA, not t-tests since more than one IV

Answer 729

* Is there an effect of gender overall? No, F(1,54) = 1.63, p = .207 Is there an effect of education level? Yes, F(2,54) = 147.52, p < .001 Is there an interaction effect? Yes, F(2,54) = 4.64, p = .014

Answer 730

* Main effect of Aspirin: Aspirin reduces heart attackes compard to placebo (1) * Main effect of carotene: Beta carotene reduces heart attack (2) * Interaction effect: Yes, bigger effect when aspirin and beta carotene taken together (3) - also lines drawn more its an interaction

Answer 731

A variable that shares some of the variance of another variable in which the researcher is interested.

Answer 732

C. The model sum of squares is partitioned into three parts The model sum of squares is partitioned into the effect of each of the independent variables and the effect of how these variables interact (see Section 13.2.7) D is also true, but we also do this for both one-way and two-way ANOVA (see Section 13.2.7).

Answer 733

16 because 4*4 = 16 (if it was 3x2 then would be 6)

Answer 734

A - baseline levels of stress used as covariate . We can use the baseline, pre-treatment measures as a control when looking at the impact the treatment has on the 3-month assessment.

Answer 735

A = for decaffeinated drinks there is little difference between email and no email, but for caffeinated drinks there is

Answer 736

Q: What sort of measurement? A: Continuous Q:How many predictor variables? ONE IV Q: What type of predictor variable? A: Categorical Q: How many levels of the categorical predictor? More than two Q: Same or Different participants for each predictor level? A: Same

Answer 737

the assumption of homogeneity of variance in between-group ANOVA

Answer 738

ε or circularity

Answer 739

equality of variances of the differences between treatment levels.

Answer 740

* Calculating differences between between pairs of scores for all treatment levels e.g., A-B, A-C , B-C * Calculating variances of these differences e.g., variances of A-B, A-C, B-C

Answer 741

there is some deviation from sphericity because the variance of the differences between conditions A and B (15.7) is greater than the variance of the differences between A and C (10.3) and between B and C (10.3). However, these data have local circularity (or local sphericity) because two of the variances of differences are identical. The deviation from spherecity in the data does not seem too severe (all variances roughly equal) but here assess deviation is serve to warrant an action

Answer 742

via Mauchly's test

Answer 743

variance of differences between conditions are significnatly different - must be vary of F-ratios produced by computer

Answer 744

varainces of the differences between conditions are equal and does not significantly differ

Answer 745

sample size

Answer 746

in big samples small deviations from sphericity can be significant, small samples large violations can be non-significant

Answer 747

several corrections that can be applied to produce a valid F-ratio or use multivariate test statistics (MANOVA)

Answer 748

* Greenhouse-Geisser correction ε ^ * Huynh-Feldt correction

Answer 749

1/k-1 (k is number of repeated measures conditions) and 1

Answer 750

more homogeneous the variances of differences, and hence the closer the data are to being spherical.

Answer 751

Limit of f ε^ is 1/k (number of repeated-measures conditions) so... 1/(5-1) = 1/4 = 0.25

Answer 752

Greenhouse-Geisser correction is too conservative

Answer 753

Greenhouse-Geisser correction

Answer 754

MANOVA is not dependent upon the assumption of sphericity

Answer 755

between group variance

Answer 756

residual variance (SSR) = variance produced by individual differences in performance SSR is not contaimined by experimental effect as study carried out by different people

Answer 757

the effect of experimental manipulation SSM and individual differences in performance (random factors outside of our control) - this is error SSR

Answer 758

compares the size of the variation due to our experimental manipulations to the size of the variation due to random factors has same type of variances in independent - total sum of squares (SST), model sum of squares (SSM) and a residual sum of squares (SSR)

Answer 759

repeated-measures ANOVA the model and residual sums of squares are both part of the within-participant variance.

Answer 760

big value of F ratio we can conclude that the observed results are unlikely to have occurred if there was no effect in the population.

Answer 761

* SST * SSB * SSW * SSM * SSR

Answer 762

SST = grand variance (N-1)

Answer 763

square of the standard deviation of each participant’s scores multiplied by the number of conditions minus 1, summed over all participants.

Answer 764

DF = N(n-1) number of participants multiplied by the number of conditions minus 1;

Answer 765

square of the differences between the mean of the participant scores for each condition and the grand mean multiplied by the number of participants tested, summed over all conditions. do this for each condition grp

Answer 766

DF = n-1 n is number of conditions

Answer 767

the difference between the within-participant sum of squares and the sum of squares for the model.

Answer 768

DF of SSW minus DF of SSM

Answer 769

one-way repeated ANOVA

Answer 770

individual differences between cases

Answer 771

post-hoc tests

Answer 772

Bonferroni method seems to be generally the most robust of the univariate techniques, especially in terms of power and control of the Type I error rate.

Answer 773

Tukey can be used

Answer 774

Games–Howell procedure, which uses a pooled error term, it is more preferable to Tukey’s test.

Answer 775

standard post hoc tests used for independent designs not avaliable for repeated measure designs

Answer 776

levels of the independent variable have a meaningful order e.g., meausred DV at successive time points or adminstered increasing doses of a drug

Answer 777

concerned about the loss of power associated with Bonferroni corrected values.

Answer 778

* Left shows variables represent each level of IV which is animal * Right shows descriptive statistics - higher mean time to retch when celebrity eating stick insect (8.12)

Answer 779

* P-value is 0.047 which is less than 0.05 * Thus, reject the assumption of spherecity that variances of the differences between levels are equal

Answer 780

* Since there are 4 conditions, lower limit of ε^ is 1/(4-1) = 0.333 (lower-bound estimate in table) * SPSS Output 13.2 shows that the calculated value of ε ^ is 0.533. * 0.533 is closer to the lower limit of 0.33 than it is to the upper limit of 1 and it therefore represents a substantial deviation from sphericity

Answer 781

- The value of F = 3.97 which is compared against a critical value for 3 and 21 DF and p-value is 0.026 - conclude there is significant difference between 4 animals in their capacity to induce retching when eaten

Answer 782

* The F-ratios are the same across the rows * the D.F is changed as well as critical value the F-statistic is compared with

Answer 783

* Adjustment made by multiplying the DF by the estimate of spherecity.

Answer 784

* Observed F statistic not significant using Greenhouse-Geisser ( p> 0.05) * Greenhouse-Geisser is quite conservative and miss true effects that exist * Thus, Huynh-Feldt showend F-statistic is still significant as p-value of 0.048

Answer 785

* Taking average of two significant values e.g., 0.063+ 0.048/2 = 0.056 * Thus, go with Greenhouse-Geisser correction and conclude F ratio is non-significant

Answer 786

Type 1 error (False positive) or not

Answer 787

* celebrities took significantly longer to retch after eating the stick insect compared to the kangaroo testicle (Level 1 vs. Level 2) - p-value of 0.002 * Time taken to retch was not significantly different in Level 2 vs 3 and Level 3 vs 4

Answer 788

ignored inclined to conclude main effects of animal was significant and proceed with further tetss like contrasts

Answer 789

- Repeated measures design - One IV (Incentive) , four conditions (week 1, week 2, week 3, week 4) - One DV (Sales Generated) - One-way repeated ANOVA

Answer 790

does not actually make any adjustments to p-value in terms of critical value as what post-hoc test should do

Answer 791

* sales are increasing across the weeks * Week 1 start at 427.93 and gradually rise by week 4 to 642,28 pounds * looks like incentives are having an effect and seem to generate higher sales

Answer 792

* P-value is not significant ( p = 0.080) * Assumption of spherecity is satisfied so we got equal variances between differences across conditions

Answer 793

* DF for week is 3 and 57 (spherecity assumed from week and error) * Week: F(3,57) = 26.30, p < 0.001 (p = 0.000), eta-squared is 0.58 - large effect - - There is an overall effect going on and change across weeks

Answer 794

* No sig difference betwen W1 and W2 * Sig difference between W1 and W3 = ihigher sales in W3 (538.570) compared to W1 (427.933) * Sig difference between W1 and W4 = ihigher sales in W3 (642.284) compared to W1 (427.933) *Not sig diff with W2 and W3 * Sig difference between W2 and W4 , higher sales in W4 (642.284) than W2 (481.388) * Sig difference between W3 and W4 , higher sales in W4 (642.284) than W3 (538.570)

Answer 795

* - Did sales increase from W1 to W2? = p = 0.010 significant - Did sales increase from W2 to W3? = p = 0.030 - Did sales increase from W3 to W4? = p = 0.008

Answer 796

* Post hoc has lack of power due to many multiple comparisons * By limiting comparisons in contradt we get around problem

Answer 797

more than one IV

Answer 798

4 different IV

Answer 799

* IV with 3 levels * IV with 2 levels

Answer 800

* Repeated measures design * Two IVs: alcohol (3 conditions) and sleep (2 conditions) * DV: Reaction Times * Two-way repeated measures ANOVA

Answer 801

* large number for RT means slower RT * Alcohol seem to have an effect on RT but particularly for 2 pints + no sleep

Answer 802

* Two p-values: alcohol ( p = 0.00) and alcohol * sleep [ interaction effect] (p = 0.00) -- > sig so assumption of spherecity is violated so report Grenhouse-Geisser values from main ANOVA table * No p-value for sleep as only 2 conditions and test of sphericity need more than 2

Answer 803

* Main sig effect of alcohol: F(1.16,22.06) = 51.38, p < 0.001, partial eta-squared = 0.73 * Main sig effect of sleep: F(1,19) = 88.61, p < 0.001, partial-eta-squared = 0.82 * Interaction effect: F(1.15,21.91) = 23.36, p < 0.001, partial-eta squared = 0.55

Answer 804

* condition 1 and condition 2 which was significant * Condition 1 vs 3 which was significant * Condition 2 with Condition 3 was significant * So all groups differing significantly from each other so interpret from that higher does of alcohol has more impact on RT

Answer 805

* Interaction effect is there = as line continue they cross * Most pronouned effect was in alcohol grp 3 (2 pints) * When alcohol grp 3 had full nights sleep (2), impairs their RT very slightly * When alcohol grp 3 had sleep deprivation (1) in combination with 2 pints, it impairs RT by a lot --> use simple effect analysis as well as two-way independent ANVOA to see if difference in grp 3 of blue and green line is sig

Answer 806

Can do non-parametric test called Friedman's ANOVA if only one IV There is no non-parametric counterpart for more than one IV in repeated design

Answer 807

1. Normal distribution 2. Repeated measures design (same participants) 3. Sphereicity - Mauchly's test

Answer 808

A significant effect means that corrections need to be made later on Those corrections are listed in the main ANOVA output table

Answer 809

1 DV continous and 2 or more categorical predictors with 2 or more levels with same participants in each predictor level

Answer 810

1 DV continous 1 Predictor categorical with more than 2 levels Same participants in each predictor level

Answer 811

significant main effects and interactions

Answer 812

The variables are the type of drink (Beer - Wine - Water) and the type of imagery used in the advertisement (positive - negative - neutral) The outcome is how much the participant likes the beverage on a scale from -100 (dislike very much) to 100 (like very much) Participants went two conditions

Answer 813

A mixture of between-subject and within-subject Several independent variables or predictors have been measured; some have been measured with different entities, (pps) whereas others used the same entities (pps)

Answer 814

mixed design

Answer 815

Q: What sort of measurement? A: Continuous Q:How many predictor variables? A: Two or more Q: What type of predictor variable? A: Categorical Q: How many levels of the categorical predictor? A: Not relevant Q: Same or Different participants for each predictor level? A: Both This leads us to and Factorial mixed ANOVA

Answer 816

a mixed ANOVA is often used in studies where you have measured a dependent variable (e.g., "back pain" or "salary") over two or more time points or when all subjects have undergone two or more conditions (i.e., where "time" or "conditions" are your "within-subjects" factor), but also measure DV when your subjects have been assigned into two or more separate groups (e.g., based on some characteristic, such as subjects' "gender" or "educational level", or when they have undergone different interventions). These groups form your "between-subjects" factor.

Answer 817

Theme No Tropical, Old Library, New York Café Amount of coffee consumed Within-subjects 1-way Repeated measures

Answer 818

Sale/ No Sale, Store’s layout Yes Sale-No Sale, Grid-Circular Profit BS (Sale) and WS (Layout) 2-way mixed Measures

Answer 819

Type of treatment, Type of counseling. Yes Residential or outpatient/ cognitive-behavioural, psychodynamic or client-centered. Substance-free days Between subjects 2-way independent measures ANOVA.

Answer 820

Normal Distribution Independent and Repeated Factors Homogeneity of Variance for the Independent factor + Sphericity for the Repeated factor

Answer 821

Normal Distribution Repeated Measure Design (same participants) Sphericity (Mauchly’s Test)

Answer 822

Normal Distribution Independence of Scores Homogeneity of Variance (Levene’s Test)

Answer 823

Levene’s test would likely be significant as the variance between the two groups are quite different.

Answer 824

repeated and mixed models

Answer 825

If GG < 0.75 THEN USE GG IF GG > 0.75 THEN USE HF Since GG less than 0.75 report adjusted F, DF and sig which is F(1.24, 21.00) = 212.32 , p < 0.001

Answer 826

distribution of groups are similar?

Answer 827

1. Dog breed and measurement time 2. Yes 3. Collie-German Shepard/Week 1-Week 5 4. Number of growls 5. Dog breed Between and measurement time is within 6. 2-WAY mixed ANOVA

Answer 828

1) is there an effect overall = Yes (green) 2) Is the effect bread = Yes (red) 3) Is there an interaction = Yes (blue)

Answer 829

Rule 1: Groups coded with positive weights compared to groups coded with negative weights. Rule 2: The sum of weights for a comparison should be zero. Rule 3: For a given contrast, the weights assigned to the group(s) in one chunk of variation should be equal to the number of groups in the opposite chunk of variation. Rule 4: If a group is not involved in a comparison, assign it a weight of zero Rule 5: If a group is singled out in a comparison, then that group should not be used in any subsequent contrasts.

Answer 830

C Two IVs = Group (Control, Anroexic, Bullimic) and Domain (Food, Friends, Physical Laws) Group is between Each partiicpant underwent domains so within DV = Participannts measured

Answer 831

The assumption of sphericity has been met, indicated by Mauchly’s test (p > .05). There was a significant main effect of distraction (F(2, 36) = 45.95, p < .001). This effect tells us that if we ignore the effect of age, driving accuracy was significantly different in at least two of the distraction groups.

Answer 832

A = Mauchly’s test was non-significant, so we can report the result in the row labelled ‘sphericity assumed’

Answer 833

a = see from the means in the Descriptive Statistics table that positive singing resulted in the highest number of goals scored and negative singing resulted in the least number of goals score

Answer 834

A = We can read the results in the row labelled ‘sphericity assumed’, as we can see from the output of Mauchly’s test that the assumption of sphericity has been met, p > .05. However, we would need to do some follow-up tests to investigate exactly where the differences between groups lie

Answer 835

Q: What sort of measurement? A: Categorical (in this case counts or frequencies) Q:How many predictor variables? A: One Q: What type of predictor variable? A: Categorical Q: How many levels of the categorical predictor? A: Not relevant Q: Same or Different participants for each predictor level? A: Different This leads us to and Chi-square test for independence of groups

Answer 836

pass or fail, pregnant or not pregnant, win, draw or lose

Answer 837

contributes to the frequency or count with which a category occurs

Answer 838

* could they dance - yes * could they dance - no * food as reward * affection as reward

Answer 839

Row totals give frequencies of dancing and non-dancing cats

Answer 840

The column totals give frequencies of food and affection as reward These are the numbers in each group

Answer 841

One categorical DV (because of frequencies) with one categorical IV with different participants at each predictor level

Answer 842

up on the basis of expected frequencies, four all four variable combinations, based on the idea that the variable of interest has no effect on frequencies

Answer 843

whether there is a relationship between two categorical variables.

Answer 844

mean or any similar statistic hence cannot use any parametric tests

Answer 845

observed frequencies from the data with frequencies which would be expected if there was no relationship between the two variables.

Answer 846

frequencies (number of items that fall into combination of categories)

Answer 847

We have a list of movie genres; this is our first variable. Our second variable is whether or not the patrons of those genres bought snacks at the theatre. Our idea (or, in statistical terms, our null hypothesis) is that the type of movie and whether or not people bought snacks are unrelated. The owner of the movie theatre wants to estimate how many snacks to buy. If movie type and snack purchases are unrelated, estimating will be simpler than if the movie types impact snack sales.

Answer 848

Data values that are a simple random sample from the population of interest. Two categorical or nominal variables. Don't use the independence test with continuous variables that define the category combinations. However, the counts for the combinations of the two categorical variables will be continuous. For each combination of the levels of the two variables, we need at least five expected values. When we have fewer than five for any one combination, the test results are not reliable

Answer 849

We have a simple random sample of 600 people who saw a movie at our theatre. We meet this requirement. Our variables are the movie type and whether or not snacks were purchased. Both variables are categorical. But last requirement is for more than five expected values for each combination of the two variables. To confirm this, we need to know the total counts for each type of movie and the total counts for whether snacks were bought or not. = check later

Answer 850

50 + 125 + 90 +45 = 310 75 + 175 + 30 + 10 = 290 50 + 75 = 125 125 + 175 = 300 90 + 30 = 120 45 + 10 = 55 310 + 290 = 600

Answer 851

1. Calculate the difference from actual and expected for each Movie-Snacks combination. 2. square that difference. 3. Divide by the expected value for the combination. 4. We add up these values for each Movie-Snacks combination. This gives us our test statistic.

Answer 852

e.g., for action and snacks it would be column total (310) * row total (125) divided by grand total of 600 = 65

Answer 853

For this it would be 65.03

Answer 854

Independence Each item or entity contributes to only one cell of the contingency table. The expected frequencies should be greater than 5. In larger contingency tables up to 20% of expected frequencies can be below 5, but there is a loss of statistical power. Even in larger contingency tables no expected frequencies should be below 1.

Answer 855

1. Set your significance level = .05 2. Calculate the test statistic -> 65.03 3. Find your critical value from chi-squared distribution table based on df & significance level 4. Degrees of freedom: df (r – 1) x (C-1) For the movie example this is; Df = (4-1) x (2-1) = 3 -> 7.815 5. compare test statistic with critical level 65.03 > 7.82 so reject the idea that movie type and snack purchases are independent

Answer 856

Research question: Does the area of psychology that a person prefers depend on whether they would select a cat or a dog as a pet? Hypotheses: H0: The area of interest in psychology and type of pet preferred are independent of each other. H1: The area of interest in psychology and type of pet preferred are not independent of each other. That is the primary area of interest in psychology depends on whether you prefer a cat or a dog. Significance level: α = .05

Answer 857

Here we see that all the expected counts in the cat group and one expected count in the dog group are below 5. We also have one in the cat group that is below 1. So, SPSS has flagged that we have 60% of the expected counts falling below 5. So assmption of expected frequencies greater than 5 is not assumed

Answer 858

We should use Fisher’s Exact Test which can correct for this.

Answer 859

A chi-square independence test was performed to examine whether there was a relationship between their area of studies in psychology and their preference for cats or dogs. The relationship between these variables was not significant, χ²(4, N = 46) = 1.46, p = .834, so we fail to reject H0.

Answer 860

A = only when you have 2 variables to compare and can't do non-directional in chi-square have to use loglinear or goodness of fit tests

Answer 861

If we are just comparing pet preferences between males and females, we can make a directional hypothesis (2 x 2 – male/female, cats/dogs). Males prefer cats or females prefer dogs. However, when we start adding variables to the design it gets complicated. If we wanted to compare drink preferences at different times of the day for students/lecturers, we couldn’t form a directional hypothesis. This is because we have 3 main effects and several interactions to consider. We need to use loglinear analyses to do this.

Answer 862

can determine complex interactions in multidimensional contingency tables with more than two categorical variables.

Answer 863

there’s no clear distinction between response and explanatory variables

Answer 864

Think of chi-square like t-tests (2 groups) and log-linear like ANOVA (more than 2 groups).

Answer 865

Research question: Is the new treatment associated with improvements in health in cats and dogs? Hypotheses: H0: Treatment, type of animal and improvements are independent of each other. H1: Treatment, type of animal and improvements are associated with each other. Significance level: α = .05

Answer 866

Independence Expected counts > 5

Answer 867

Here we have 3 things we are comparing: animal (cat/dog), treatment (yes/no) and improvement (yes/no) all of which are categorical. We look and see that all of the expected counts are above 5. So met assumption of independence and expected counts

Answer 868

all terms present (all main effects and all possible interactions main effects: Animal, Treatment and Improvement interactions: Animal * Treatment, Animal * Improvement, Treatment * Improvement and Treatment* Animal* Improvement

Answer 869

Remove a term and compares the new model with the one in which the term was present. Starts with the highest-order interaction (including max number of variables/categories) Uses the likelihood ratio to ‘compare’ models below: If the new model is no worse than the old, then the term is removed and the next highest-order interactions are examined, and so on.

Answer 870

We can see that the model selection worked in a way that it first tried to remove the 3-way interaction. However, we can see here that it * affected the fit of the model, so it was left in. Since removing the highest-order interaction made a * difference to the fit of the model, we get a final model that is the saturated model (it contains all main effects and interactions).

Answer 871

we are using the likelihood ratio here because that’s how we compare the models to find the best fit . We see that all main effects and interactions are significantly contributing to explaining the variance in the data

Answer 872

K represents the level of the terms. For example, K=1 would be the main effects, K=2 would be our 2-way interactions and K=3 is our 3-way interaction.

Answer 873

There is a significant three-way interaction between animal, treatment and improvement, as well as two significant two-way interactions between animal and improvement and treatment and improvement (p < .001) a * 3-way interaction between animal, treatment and improvement as well as two * 2-way interaction between animal/improvement and treatment/improvement. Like our post-hoc tests, this is telling us where the * differences are.

Answer 874

Based on the raw data, there seems to be indication that the cats responded better to treatment than dogs, this should be followed up by chi-square tests separately for cats and dogs to determine whether the association between treatment and improvement is present in both cats and dogs

Answer 875

hypothesis that frequencies predicted by model (expected frequencies) are sig different from actual frequencies in data (obsevered)

Answer 876

our model was significantly different from our data (i.e., the model is a bad fit to the data).

Answer 877

non-parametric methods

Answer 878

When data violate the assumptions of parametric tests we can sometimes find a nonparametric equivalent eg. normality of distribution

Answer 879

randomization or ranking the data for each group

Answer 880

outliers and skew

Answer 881

Add up the ranks for the two groups and take the lowest of these sums to be our test statistic The analysis is carried out on the ranks rather than the actual data.

Answer 882

Mann-Whitney or Wilcoxon rank-sum test

Answer 883

Wilcoxon signed-rank test

Answer 884

Kruskall-Wallis or (for trends) Jonckheere-Terpstra

Answer 885

Friedmanʼs ANOVA

Answer 886

Loglinear analysis (categorical outcome, with participants as a factor)

Answer 887

Spearman’s Rho or Kendall’s Tau

Answer 888

two independent groups of scores

Answer 889

two dependent groups of scores

Answer 890

> 2 independent groups of scores

Answer 891

> 2 dependent groups of scores

Answer 892

two continuous variables are related (pattern of responses across variables)

Answer 893

Step 1: Get some not normally distributed data Step 2: Rank it (regardless of group) Step 3: Significance testing Does one of the groups have more of the higher ranking scores than the other?

Answer 894

(r-1)(c-1)

Answer 895

small sample sizes

Answer 896

df = (r-1)(c-1)

Answer 897

1 DV = Ordinal (e.g., high school, bachelors, order is meaningful) or continous 1 IV = Categorical and 2 levels Different partiicpants Does not meet assumption of parametric

Answer 898

same procedure and used to compare two independent groups and assess whether samples come from same distribution

Answer 899

Rank all the data on the the basis of the scores irrespective of the group compute the sum of ranks of each group

Answer 900

the lower of the two sums of ranks

Answer 901

sum of ranks for group 1, R1, as follows

Answer 902

Here we have data for two groups; one taking alcohol, the other ecstasy. The scores for a measure of depression. Scores were obtained on two days; Sunday and Wednesday. The drugs were administered on Saturday.

Answer 903

The graphic here shows how we can list the scores in order and as a result assign each score a rank. When scores tie, we give them the average of the ranks. If we ensure we keep track of the group the scores came from we can relatively easily add the ranks up for each group. Note, that if there was little difference between the groups the sums of their ranks would be similar, as they are for the data shown her for Sunday. However, the sum of ranks differ considerably for the data obtained on Wednesday.

Answer 904

The first terms involving n1 and n2 actually compute the maximum possible sum of ranks for group 1. U is zero when all those in group one have scores that exceed the scores of those in group 2.

Answer 905

effect size so r = z / square root of N (number of pps

Answer 906

1 IV categorical with 2 levels Same participants in each predictor level 1 DV - Ordinal or continous Does not meet assumption of parametric tests

Answer 907

1. Compute the difference between scores for the two conditions 2. Note the sign of the difference (positive or negative) 3. Rank the differences ignoring the sign and also exclude any zero differences from the ranking 4. Sum the ranks for positive and negative ranks

Answer 908

The table shown here has the Depression Scores taken on Sunday and Wednesday for those taking ecstasy on Saturday. Data for Sunday are in the first column and Wednesday in the second column. The third column shows the difference between scores obtained on Sunday and Wednesday. NOte some could be negative, some positive. In this example however the difference is always positive apart from two values when the difference is zero. The fourth column notes the sign of the difference or notes it is going to be excluded because the difference was zero. The fifth column ranks the differences in terms of their size, but not sign. The sixth and seventh column list the ranks that were for positive and negative differences, respectively. It is these two columns that are summed to get the relevant statistics, called T+ and T-. Because T+ and T- are not independent, we take only the T+ value.

Answer 909

1 DV of continous or ordinal 1 IV categorical predictor of more than 2 levels Diff participants in each predictor level Does not meet assumption of parametric

Answer 910

Rank all the data on the the basis of the scores irrespective of the group Compute the sum of ranks of each group, Ri , where i is the group number

Answer 911

1 DV continous or ordinal 1 IV predictor categorical with more than 2 levels Same participants in each predictor level Doesnot meet assumption of parametric tests

Answer 912

Rank the scores or each individual - that means you will have ranks varying from 1 to the number of conditions the participants took part in Compute the sum of ranks, Ri , for each condition

Answer 913

K = conditions N = number of pps

Answer 914

- In this example, they wanted to look at whether attendance at lectures had an impact on their exam performance on whether they passed or failed - Attendence was coded as 1 if participants generally attended lectures , barirng illness, and 2 if they did not attend - Exam was scored as 1 = Pass and 2 = Fail

Answer 915

- Attendence, Attended Lectures , Count = this is people who attended lecture and number of people who passed was 84 and people who failed was 29 - % Within attendence give same info so 74.3% passed and 25.7% failed when attended lectures -Going to didn’t attend lectures, 22 people passed and 35 failed and below is in percentages: - Easier using percentages writing up

Answer 916

- At top row, pearson chi-squared which chi-square statistic which was 20.617, DF which is 1 and p-value was 0.000 - 0 cells have a count less than 5  met assumption of chi-square test that expected counts greater than 5

Answer 917

0 cells have a count less than 5  met assumption of chi-square test

Answer 918

- x^2 (1) = 20.62, p < 0.001 - Cramer's V = 0.35 , indicating a medium effect size

Answer 919

Small effect = 0.1 Medium effect = 0.3 Large = 0.5 and above

Answer 920

correlation coefficient:

Answer 921

odds of passing/failing for students who attended lecture = no. of students who attended and passed (84) / no. of students who attedned and failed (29) = 2.897 odds of passing/failing for students who did not attend = no. of students who did not attend lectures and passed (22) / no of students who did not attended and fail (35) Odds ratio = odds of P/F of attended/ odds of P/F of not attended = 2.897/ 0.629 = 4.606 saying for an individual who attended lectures lead them to be more likely to pass exam

Answer 922

- Independent sample design - One IV, two conditions = existing vs new medication - One DV (symptoms) but this time on ordinal (scale from 1 to 5) and got combination of non-normally distributed data and small sample size (very problematic for t-tests) - Mann Whitney U Test

Answer 923

- This box summarises the p-value ( p = 0.026) and tells you whether to accept or reject the null hypothesis.

Answer 924

- Mann Whitney U test statistic is 166.000 to report and also people report standardised test statistic 2.292 which is z score so handy to report as know if its above +/-1.96 then p-value we get out of test is significant - P-value of exact significant is p = 0.026 - This is significant difference between the 2 groups

Answer 925

- For existing treatment, median score was 3. And new treatment the median score was 4. It suggests new treatment was more effective in reducing symptons than the existing treatment

Answer 926

- Again we got ordinal data for DV not sure distances between levels is going to be the same - Related design - One IV, 3 conditions - One DV (level reached to video game) - Friedman’s ANOVA = more than 2 groups in related design

Answer 927

- We got total sample size which is 30 and test statistic which is 21.788, DF = 2 and p value is 0.000 so significant difference between the 3 groups

Answer 928

post hoc tests for pairwise comparisons to look where the differences are

Answer 929

- First one is Joy stick vs Vyper Max - Second one is Joystick vs Evo Pro etc… - Notice it gives two p-values of sig and adjusted sig - Adjusted sig control for multiple comparison and make correcitons to p-value (use this - Difference between joystick vs Vyper Max was sig at p = 0.005 - Difference between Joystick vs Evo Pro was sig at p = 0.00 - Difference between VyperMax vs Evopro is non-significant as p = 0.660

Answer 930

to detect sig effects compared to parametric effects so maybe issue of dealing with power so may have median scores higher in one then another but not sig

Answer 931

A = non parametric have fewer assumptions than parametric

Answer 932

A = correct, because it is false. Chi-square can be used on categorical variables only

Answer 933

A = If our model is a good fit of the data then the observed and expected frequencies should be very similar (i.e., not significantly different

Answer 934

B = 34-28/3

Answer 935

B = 0.50 squared

Answer 936

A = k (number of grps) - 1 = 5 - 1 = 4

Answer 937

C = 2^k - 1 = 2^3 - 1 = 7

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