evidence based

Question 1

evidence based treatment

Answer 1

refers to the interventions or techniques (cognitive therapy for depression etc) that have produced therapeutic change in controlled trials.

Question 2

evidence based practice

Answer 2

broader term and refers to clinical practice that is informed by evidence about interventions, clinical expertise and patient needs, values and preferences and their integration in decision making about individual care

Question 3

t-test

Answer 3

differente between sample means memory between walkers and sitters.

Question 4

assumptions t test

Answer 4

variance in memory does not differ between groups (homoscedasticity), participants are independent of each other (not siblings, same housing group), memory (dep. var.) is normally distributed within groups

Question 5

general linear model y(i)=b0+b1*x(i)

Answer 5

y= dependent variable
b0= indep. var. 1
b1= different dep. var. 1 en 2

Question 6

assumptions regression analysis

Answer 6

variances equal for all values of x, participants were independent of each other, residuals normally distributed

Question 7

anova, assumptions

Answer 7

= can be used to test between and within subjects variables

1) variances equal in all groups
2) participants independent of each other
3) data normally distributed within groups

Question 8

ancova, 2 main goals

Answer 8

1) correct for non-random allocation
2) reduce variance within groups (by taking into acc co-variance with a third variable), promotes power to reject H0 if H0 is really false

Question 9

ancova, assumpties

Answer 9

1) variances equal in all groups
2) participants independent of each other
3) data normally distributed within groups
4) parallel regression lines (effect of SES on memory equal for sitters and walkers)

Question 10

repeated measurements analysis assumptions

Answer 10

1) variances equal in all groups
2) participants independent of each other
3) data normally distributed within groups
4) sphericity (univariate approach only) (the variances of the differences between all possible pairs of within-subject conditions are equal)

Question 11

multiple comparison problem

Answer 11

for type 1 error, if you apply 100 tests together they stand a much higher chance of false positives

Question 12

power

Answer 12

correcte rejecting the null hypothesis (1-Beta) / , researchers consider a study to be adequately powered if it has at least an 80% chance of detecting a clini- cally significant effect when one ex- ists.

Question 13

beta

Answer 13

type 2 error, false negative,

Question 14

effect size

Answer 14

Effect size is a quantitative measure of the magnitude of the experimental effec// the degree of non-overlap between sample distributions (the less overlap the larger the effect size), the probability that one could guess which group a person came from, based only on their test score (effect size d=0 -> correct guess 0.5, d=1 prob rises to acceptable levels)

Question 15

effect sizes for discrete variables

Answer 15

Cohens d, hedges g, Pearson correlation r

Question 16

odds ratio

Answer 16

an effect size for discrete outcomes

Question 17

meta regression

Answer 17

to get a summary of the literature

Question 18

disadvantages narrative review

Answer 18

focus on p-values in original studies
tempration t write things that still support your theory
how to deal with studies that differ in reliability?
often based only on published literature (file drawer)

Question 19

interpretation of intercept

Answer 19

the value. you expect if you score zero on all independent variables

Question 20

r

Answer 20

strengst of association between variables

Question 21

d

Answer 21

magnitude of the difference between treatment and comparison groups

Question 22

NNT(needed to treat)

Answer 22

the number of patients who must be treated to generate one more success or one less failure than would have resulted had all persons been given the comparisons treatment

Question 23

AUC (area under the curve)

Answer 23

represents the probability that a randomly selected subject in the treatment group has a better result than one in the comparison group

Question 24

statistical power

Answer 24

is the conditional probability that a true effect of a precisely specified size in the population will be detected in a study using such conventional significance testing

Question 25

underpowered studies

Answer 25

- provide insufficiently meaningful information - add to replicability problems (higher prob of false positives/negatives - are a waste of (vulnerable) clients (& everyone else’s) time

Question 26

overpowered studies

Answer 26

- unnecessarily exposing participants to an intervention that may not work or may have side effects - are a waste of resources - (although we really want high power!)

Question 27

moderation

Answer 27

moderators affect the effect of treatment// example : the strength of the relationship between game playing and aggression is affected by callous unemotional traits (moderator) // treatment effect depends on variables that are themselves independent of treatment (sex; nominal moderator, intelligence; continuous moderator)

Question 28

mediation

Answer 28

mediation is said to have occurred if the strength of the relationship between the predictor and outcome is reduced by including the mediator// mediators are affected by treatment//which processes are important during an intervention? (what mechanisms underlie intervention effects) (sleep problems; nominal mediator, hours of sleep ; continuos mediator)

Question 29

logistic regression

Answer 29

regressing a dichotomous dependent variable on the basis of on or more (nominal or continuous) independent variables

Question 30

with logistic regression you can do enter or stepwise

Answer 30

enter = keep all independent variables in model (test an explicit hypothesis : confirmatory) or stepwise forward/backward = let sass find out which variables are important (no explicit hypothesis : exploratory)

Question 31

evidence based clinical practice

Answer 31

clinical practice based on the (best) available evidence combined with the practitioner’s personal expertise and experience, adapted to the patient’s needs and preferences

Question 32

intention to treat analysis

Answer 32

based on the original treatment assignment and not on the treatment eventually received /completed. intended to avoid potentially misleading artefacts that can arise in intervention research, such as nonrandom attrition of participants

Question 33

per-protocol analysis

Answer 33

a comparison of treatment groups that includes only those patients who completed the allocated treatment -> may lead to bias: depending on the distribution of dropouts, randomisation may no longer be OK. used to see if

Question 34

relative risk

Answer 34

the odds that improvements are greater in the control condition than in the intervention condition •RR = odds of impr. in control condition / odds of impr. in intervention condition–we want this ratio to be 1 or lower

Question 35

absolute risk reduction

Answer 35

absolute difference in the odds of improvement in the control condition and in the intervention condition •ARR = odds of impr. in intervention condition - odds of impr. in control condition–we want this to be 0 or lower

Question 36

relative risk reduction

Answer 36

reduction in the incidence of negative outcome•RRR = 1 – RR–we want this to be as close to 1 as possible

Question 37

multilevel modeling

Answer 37

designed to analyse data that exist at different levels / analysis of data gathered in multiple groups (intervention study in multiple nursing homes)//dependent variable : continuous, independent variable : nominal or continuous all variables : manifest(are observable)

Question 38

unit of analysis problem

Answer 38

when the moderator variable exists at a different level than the independent and dependent variables, should the analysis be done at the level of the student or at the level of the program ?

Question 39

level 1 MLM independent varibale

Answer 39

client characteristic

Question 40

level 2 independent variable

Answer 40

therapist characteristic

Question 41

individual patient meta regression

Answer 41

multiple patients from multiple studies. can incorporate : level 1 independent variables (related to individuals) and level 2 independent variables (related to studies)

Question 42

2 types of theries

Answer 42

manifest variables, latent variables, relations between manifest variables (networks)

Question 43

disadvantages of nominal latent variable based on concensus

Answer 43

high comorbidity, arbitrary limits (why 6 out of 9?), heterogeneity within groups : extreme cases 6 out of 12 symptoms (one diagnostic category may contain two completely different people)

Question 44

mixture analysis model

Answer 44

nominal

Question 45

factor analysis

Answer 45

continuous

Question 46

factor mixture analysis

Answer 46

both nominal and continuous

Question 47

advantages of nominal model

Answer 47

no comorbidity, no arbitrary limits, reduced heterogeneity within groups

Question 48

network theory

Answer 48

"this person is distracted, and therefore has trouble organising tasks" focused on the relationship between manifest variables = symptoms

Question 49

purpose of mixture and cluster analysis

Answer 49

determine latent groups (within groups equal and between groups different

Question 50

differences mixture and cluster analysis

Answer 50

mixture is a statistical method. cluster is not a statistical method (cannot test hypotheses, is exploratory, no assumptions)

Question 51

steps in cluster analysis

Answer 51

``` selection of sample (theory) selection of variables (theory calculate inequalities between participants do the cluster analysis interpretation of cluster analysis choosing number of clusters validation of final cluster solution ```

Question 52

chi square test

Answer 52

relationship between two categorical variables. statistic is based on the idea of comparing the frequencies you observe in certain categories to the frequencies you might expect to get in those categories by chance

Question 53

fishers exact test

Answer 53

a solution to the fact that if the cell is <5 the test statistic is too deviant from a chi square distribution to be accurate. the fishers exact test is a way to compute the exact probability of the chi square test in small samples

Question 54

likelihood ratio

Answer 54

alternative to Pearson chi square, based on maximum likelihood theory. general idea is that you collect some data and create a model for which the probability of obtaining the observed set of data is maximised, then you compare this model to the probability of obtaining those data under the null hypothesis

Question 55

loglinear analysis

Answer 55

analyse more complex contingency tables in which here are three or more variables

Question 56

assumptions when analysing categorical data

Answer 56

independence of residuals (each person item or entity must contribute to only one cell of the contingency tabel) and expected frequencies (no expected values should be below 5

Question 57

centring variables

Answer 57

'use of grand mean centring', refers to the process of transforming a variable into deviations around a fixed point. this is used when it makes no sense that the b in the equation would be zero (like heart rate)

Question 58

simple slopes analysis

Answer 58

comparing the relationship between the predictor (time spent gaming) and outcome (agression) at low and high levels of the moderator (callous traits). the essence is that we work out the model equations for the predictor and out come at low high and average levels of the moderator

Question 59

mediation direct effect

Answer 59

pornography consumption on infidelity, the relationship between them controlling for relationship commitment (c')

Question 60

mediation indirect effect

Answer 60

the effect of pornography consumption on infidelity through relationhship commitment (a-b+c')

Question 61

network structure

Answer 61

the web of relations among symptos

Question 62

network state

Answer 62

the activation of symptoms

Question 63

causality hypothesis

Answer 63

when causal relations among symptoms are strong, the onset o tone symptom will lead to the onset of others

Question 64

connectivity hypothesis

Answer 64

strongly inter connected symptom networks are thus vulnerable to a contagion effect of spreading activation through the network

Question 65

centrality hypothesis

Answer 65

higher central symptoms (stronger inter symptom connection) have greater potential to spread symptom activation throughtou the network than do symptoms on the periphery

Question 66

comorbidity hypothesis

Answer 66

because some symptoms occur in multiple disorders, symptom activation can spread between syndromes, with symptoms bridging these syndromes playing a critical role in psychiatric comorbidity

Question 67

level of momentary experiences

Answer 67

symptoms are aggregates of moment to moment experiences. these micro processes constitute the true building blocks of psychopathology

Question 68

conditional positive manifold

Answer 68

even after controlling for shared variance among symptoms, these symptoms tend to be positively interconnected

Question 69

what is de dependent variable in meta regression analysis?

Answer 69

effect size

Question 70

when you have an intercept (so no independent variable) in meta regression analysis

Answer 70

the interpretation of the intercept will be the overall effect size across the difference in side effects between placebo and drug users

Question 71

N=1 analysis

Answer 71

is comparing the baseline before the intervention with the period of during the intervention

Question 72

AR1

Answer 72

autoregressive of order 1. correlation timepoints is only dependent on their distance 'lag'

Question 73

normative comparisons

Answer 73

comparing the score of an individual to that of a typical sample. essence of psychological assessment

Question 74

family wise error

Answer 74

chances of at least one a-typical test result , problem : FWE increases with number of tests

Question 75

interpretation of gamma zero zero parameter

Answer 75

it is the score on a test (dependent variable) in a given class if the fear of failure level of the level 1 independent variable is zero