Research Methods and Analytical Procedures

Question 1

Hierarchy of Evidence (Weakest to strongest)?

Answer 1

Expert opinion

Case Reports

Cohort studies / Cross-sectional Studies

Randomised controlled trials

Systematic Reviews

Question 2

Whats a narrative review?

Answer 2

While narrative reviews are often written by experts it is likely that his/her expertise, experience and beliefs will influence which studies are included in the review

Narrative reviews do NOT influence policy makers as they are NOT ‘good science’, even if the studies they include are individually very good science.

So falls under Expert opinion, it is cherry picking

Question 3

What are systematic review?

Answer 3

Systematic reviews minimise the likelihood of such bias as the methods used to identify, select and critically appraise relevant primary research, is done in an open, explicit and systematic way.

Question 4

Aim of a systematic review?

Answer 4

Summarise the findings of ALL studies that address a very specific research question and meet clear inclusion criteria (e.g. must be RCT, >1 mth). And, if possible, quantify the average effect or finding.

Identify gaps or under-researched areas in the literature (e.g. there maybe a shortage of trials in a particular age group, ethnicity, or sport).

Question 5

Search term used when doing a systematic review? (example for assessing the impact of exercise referral schemes on physical activity and health outcomes

Answer 5

PICOS

Participants - (Has/risk of disease, old, obese)

Intervention type - (exercise referral)

Control/Compared to - (usual advice, diet)

Outcome - (BMI, BP, physical activity)

Study design - (RCT, cohort, cross-sectional)

PECOS for observational studies - P = population, and E = exposure

Question 6

Types of systematic reviews?

Answer 6

A qualitative synthesis of the studies: these are quite descriptive. They report the number or proportion of studies that report a statistically significant difference – p<0.05 (systematic review)

A quantitative synthesis of the studies. An ‘average’ intervention effect is calculated from all of the intervention effects from the included studies (systematic review and/with meta-analysis)

Question 7

Description of a Meta Analysis?

Answer 7

The general aim of a meta-analysis is to more powerfully estimate the ‘true’ intervention effect size as opposed to a less precise effect size derived in a single study.

To produce an ‘average’ intervention effect the units need to be same across all studies, or standardised:
[e.g. Diff of Means divided by Pooled SD].

If there is high heterogeneity (wide range) of effect sizes (e.g. some zero effect, some very positive) then producing a single summary estimate is not appropriate (low I2 value = low heterogeneity = effects were similar across studies). This is a score on the forest plot as well (0% is very low heterogeneity,

Question 8

Notes about a forest plot for a meta analysis?

Answer 8

Is in chronological order In terms of publication date - done in this order as pilot studies normally show more effects than in real world

Square indicates relative risk of each study. Relative risk shows the percentage increase or decrease the study had

The wings represent the 95%CI, if wings cross the line of ‘no effect’ (relative risk ratio=1) then that study did not find a statistically significant effect.

Relative risk found by (the percentage of those who met the criteria who had the intervention) / the percentage of those who met the criteria who didn’t have the intervention)

Bigger studies will have a greater weighting when determining the ‘average intervention effect’. Instead of each having equal weighting represented by a larger rectangle

The kite/diamond shape at the bottom represents the ‘weighted average’ intervention effect and 95%CI. (the amount 1 represents how much % it increases it by) - and if it crosses the line then it is not significantly different (the size of the diamond is its confidence intervals) - can read of the p value (if lower than 0.05) to check significance as well

Question 9

What differences could come up if you did a quantitative synthesis/ meta-analysis, compared to a qualitative synthesis?

Answer 9

If looked at studies qualitatively, could end up saying all the studies aren’t significant, but when you look at them qualitatively as a collective they actually make the result significantly

Question 10

Standard criteria for assessing quality/ risk of bias in forest plots?

Answer 10

e.g. Newcastle-Ottawa risk of bias scale:
0-3 = very high risk
4-6 = high risk
7-9 = low risk

Or can do a Meta regression for high heterogeneity

Question 11

If you add and minus 1 standard deviation to a normal distribution what percent of the data will you cover?

Answer 11

1 standard deviation = 68%
2 standard deviation = 95%
3 standard deviation = 99%

Question 12

How to test if you data is normally distributed?

Answer 12

Look to see if curve is bell curved - not finite
Skewness (how the curve leans) should be between -1 and 1
Kurtois = (the peakedness or flatness of a distribution) , Kurtois should be between -1 and 2
Can still go ahead and analyse if values are just outside of boundaries

Is under descriptive statistics in SPSS, you look at the statistic values in the skewness and kurtois

Question 13

Whats negative and positive skew?

Answer 13

Positive is when most of the results are bellow the middle
Negative skew is when most of the results are past the middle

Question 14

What’s mesokurtic curve?

Answer 14

Kurtois is normal

Question 15

What’s a leptokurtic curve?

Answer 15

Very peaked - positive kurtois

Question 16

Whats a platykurtic curve?

Answer 16

Very flat - negative kurtois

Question 17

T test definitions?

Answer 17

Test whether there is a difference between 2 means

t-tests are used to test if the difference between the 2 means is likely to be a real difference or one that occurred by chance (sampling variation)

p-value = probability that it occurred by chance
two types of t tests

Question 18

Independent samples t tests?

Answer 18

Independent groups: Different people in each sample mean
Looks at differences between 2 different groups

Question 19

Dependent / paired samples t test?

Answer 19

One group the sample people in each sample mean (except twin studies)
Look at differences within the same group in different conditions/ time-points (before/after)

Question 20

Hypotheses types?

Answer 20

Null hypothesis = states there will be no difference between the groups
The alternative hypothesis = states there will be a difference between groups

Question 21

Are most t tests 2 directional?

Answer 21

Yes, eg. there will be a significant difference between the lowering of blood pressure from drug a to drug b
Occasionally 1 tailed = Drug A will be better than Drug B, (but if Drug B is actually better than Drug A you cannot report it as that is not what you’ve tested, and this would have important implications on people’s health). If Drug B was a newer, much cheaper drug than Drug A, then the implications would be the same whether Drug B was better or same as Drug A ….. under both circumstances we’d recommend people used Drug B. (non-inferiority study)

Question 22

What does the P value / significance level have to be below for it to be significant and in a t-test mean that the 2 means are significantly different?

Answer 22

p < 0.05 (expect when applying the Bonferroni correction)
This is when we believe the observed difference hasn’t happened due to random chance (95%)

13

Question 23

Type 1 error:

Answer 23

You reject the null hypothesis - when the null hypothesis is true (you say there is a difference when there isn’t)

Due to random sampling

Question 24

Type 2 error:

Answer 24

You fail to reject the null hypothesis when the null hypothesis is false (you say there isn’t a difference when there is)

Due to random sampling

Easy way to remember is boy who cried wolf did a type 1 error then a type 2

Question 25

Assumptions about the data that need to be satisfied for the results of a t-test to be reliable?

Answer 25

Data is continuous and is normally distributed (eg.height weight, strength, glucose)

Both groups are a random sample of a population (if testing whether males are taller than females, don’t take female basketball players and male jockeys)

Homogenity of variance - both groups have similar variances or standard deviations (can visualise this if the distribution curve is far more spread out than the other) - if the assumption is violated in SPSS it offers a an alternative corrected result

Question 26

Whats a parametric test compared to a non parametric

Answer 26

A parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one’s sample data are drawn (normally distributed continuous data)

• are preferable as provide more reliable and interpretable results, however if sample is small and clearly not normal should use non-parametric tests

Pearsons correlation, independent samples t-test, paired samples t-test, one way ANOVA, Repeated measures ANOVA

A non parametric test is one that makes no such assumptions - used when data is not applicable to a parametric test ie. your sample is small and your data is clearly not normal (ordinal /rank data)

Spearman’s rank order correlation

Mann-Whitney U-test

Wilcoxon signed rank test

Kruskal-Wallis ANOVA

Friedman’s ANOVA

Question 27

Strength of Evidence with p values?

Answer 27

p > 0.1 no evidence against null

p = 0.05 to 0.1 not quite enough evidence against the null (only relevant if this is the most significant value you got in the whole study

p < 0.05 moderate evidence against null

p < 0.01 strong evidence against the nulll

p < 0.001 very strong evidence against the null

Question 28

What do ANOVAS do?

Answer 28

Compare 3 or more means

Type of ANOVA depends on:

The number of independent variables, 1 IV = one-way, 2 IVs = two-way and so on

The type of independent variables:
Is it independent samples = groups

Or repeated measures = conditions or time-points

Question 29

Types of ANOVA?

Answer 29

One way independent sample ANOVA (one independent variable)

Two way independent sample ANOVA (two independent variables)

One way Repeated Measures ANOVA (one independent variable)

Two way Repeated Measures ANOVA (two independent variables)

Two way Mixed Design ANOVA (when you have independent samples and repeated measures) (Two independent variables)

Question 30

Why not compare several means with lots of t-tests?

Answer 30

Cannot look at several variables simultaneously:

Performing multiple t- tests inflates the type 1 error rate (as for every test have to 0.95^x, with x being the number of tests)

Question 31

Theory of independent sample ANOVA and how to work out F ratio?

Answer 31

The F-ratio = Between group variance / Within group variance

Between group variance = distance between group means (of the outcome variable) and the overall sample mean. (This is due to differences in the independent variable or experimental manipulation)

Within group variance = Distance between each participant and the mean of the group they belong to

Question 32

How to find critical values of F for 0.05?

Answer 32

Degrees of Freedom: - how many values do you have to know for you to be able to work out the final value if know the sum of squares

A = For between groups = (Number of groups - 1)
B = Within groups = (Number of people in the study - the number of groups)

So Write out represents (A,B)

Look up in the table Where A and B meet, and this is your critical value, and if F is larger than the value you find in the table, the result is significant

Question 33

How is the ANOVA an omnibus test?

Answer 33

The F-ratio can only tell us if group means are different

It does not tell us which group means differ from which

We need follow-up / ‘post-hoc’ tests to find out where the group differences ar

Question 34

Hypothesis in One-way IS-ANOVA?

Answer 34

Null = There are no differences between the group means (mean1 = mean2 = mean3)

Alternative = At least one group mean is different from the other group means (not all means are equal)

Question 35

Assumptions of IS-ANOVA

Answer 35

Random selection and representative sample

Normal distribution each group

Equal variance in each group (homogeneity) - done by Levene test - if significant homogeneity has not been met

(even if data is not normally distributed and variance is unequal, if sample sizes are large and close to equal, the F in ANOVA is reasonably robust

Question 36

What is the Sum of Squares?

Answer 36

Distances from the means

Between groups = sum ((mean of group - overall mean)^2) x number of person in each group)

Within groups = Sum((Individual values - mean of group)^2)

Total sum of squares = Sum((individual values - overall mean)^2)

Question 37

Working out degrees of freedom between groups, within groups and total

Answer 37

A = For between groups = (Number of groups - 1)
B = Within groups = (Number of people in the study - the number of groups)
C = Total = Number of people in study - 1

Question 38

Example of reporting an IS-Anova?

Answer 38

There is very strong evidence to reject the null hypothesis in favour of the alternative hypothesis. There is a significant difference in the cognitive anxiety of sports men and women depending on the competitive level at which they play (F(3,60) = 14, p<0.001)

Question 39

When looking cat SPSS output post hoc-tests how do you what is significantly different?

Answer 39

Look through and see what comparisons are significant

Example writing it:

Post Hoc independent t-tests with a Tukey’s correction to the alpha were performed in order to identify where differences in competitive level lay

Results revealed that club level athletes demonstrated significantly higher levels of cognitive anxiety than any of the other three competitive level performers (Club = 16.6, county = 12.2, national = 10.3, international = 9.1 , p <0.01). However Anxiety was not different between County, national and international level performers (all p > 0.05)

Question 40

Levels in a two-way IS-ANOVA?

Answer 40

2 x 2 = eg. gender (M & F) x Common cold (Y/N) on minutes spent ‘not concentrating in statistics lectures’

2 x 4 eg. breakfast status (Y/N) x hours of sleep (2, 4, 8 , 8) on average power output performance in 30sec Wingate test

3 x 3 = eg. weight status (normal/overweight / obese) x no of cars in house hold (0/1/2+) on how much you exercise

Question 41

Whats the issue with 2-way IS ANOVA?

Answer 41

Like any ANOVA F-ratio only can tell us not all the group means are not the same so have to do post hoc tests for follow up

However 2 way IS ANOVA is very clunky on follow up tests for sig interaction and very underpowered

Question 42

What is an interaction?

Answer 42

This is when the effect that one factor has on the outcome variables differs depending on the levels of another factor

Examples:

If one line changes as the other factor does, but the other factor doesn’t shows an interaction

Both main effects significant but not the interaction (lines parallel and sloped with a gap so gender and comp level both effect)

(or parallel and straight = just gender effect)

(or parallel and sloped put lines on top of each other means only comp level is effecting)

Question 43

How to inexpert main effects when the interaction is significant?

Answer 43

On SPSS see it on the gender*level significance for example (seen on graph from diverging towards or away from each other)

Usually if an interaction is significant we would not interpret the main effects even if they are themselves significant, as will not be true at certain levels

Sometimes can interpret main effects when the interaction is significant

Question 44

One-way RM-ANOVA?

Answer 44

The levels within the ONE factor represent conditions/time-points measured on the ONE group of people

Each participant provides outcome data for all conditions/time-points of the one factor

Question 45

Example of a One-way RM-ANOVA?

Answer 45

Each person had glucose and insulin monitored on three dats, each day under a different condition (the orders in which people did these conditions was random)

1. Sitting all day (9hrs)
2. Sitting all day apart from 2 mins of standing every 30 mins
3. Sitting all day apart from 2 mins of walking every 30 mins

Question 46

What does variance within individuals composed of?

Answer 46

‘Condition variance’ - variance due to the experimental condition (SSc)

‘Error variance’ Variance not explained by the condition (SSe)

Question 47

Strengths of a RM-ANOVA?

Answer 47

High sensitivity:
Less unsystematic variance (less noise) compared to if you had 3 separate groups for example (Over 1 group doing all 3 conditions)

Greater sensitivity to sometimes subtle experimental effects, as each participant acts as his/her own control

Economical
You need less participants to achieve the same power

But they need to visit the lab more often

Question 48

Assumptions for a One-way RM-ANOVA?

Answer 48

Assumption of Sphericity - checked using Mauchly’s test

Variances of the differences between each pair of the repeated measures factor should be the same

p > 0.05 = variances of the differences are not the same, sphericity is assumed

p < 0.05 = variances of the differences are not the same, assumption of sphericity is violated

eg. variance (sit - stand) = variance (sit-walk) = variance (stand - walk)

Assumption of normality:
Outcome data should be ‘normal’ at each level of the condition or time-point

Don’t be scared of slight violations to normality, RM-ANOVAS are reasonably robust to them

Question 49

Correction factors for non-Sphericity?

Answer 49

If p < 0.05

Greenhouse-Geisser (conservative finds fewer differences)

These lower the DFs to correct for the effect of sphericity

So when looking at the critical value table your degrees of freedom will be lower, so the critical value will be higher so less likely to be larger than the F value so less likely to find a significant result

Question 50

How to report Greenhouse-Geisser corrected F-test?

Answer 50

F(corrected df value, next corrected df value) = F value, p < or > sig value

Question 51

Whats a two-way fully RM-ANOVA?

Answer 51

The levels within each of the two factors represent different conditions/time-points measured on the one group of people

Each participant provides outcome data for all conditions/ timepoints within each of the two factors

Question 52

Example of a two-way fully RM ANOVA?

Answer 52

10 cyclists undergo a stability test on the bike measuring the amount of times they go off the white stripe at timepoints 3, 6, 9,12,15 minutes were resistance increases each time

They then undergo a 6-week core stability training programme designed to improved their cycling ability

They then repeated the test, providing 3 research questions

Does the cyclists stability change with increased fatigue? (TIME)

Does the cyclist stability score change as a result of the training intervention (between the two ‘TEST’ days 6-weeks apart)

Is there a ‘TEST day x fatigue Time’ interaction does the change in stability pre and post training program differ depending on the fatigue time (3,6,9,12,15?)

Question 53

Post Hoc tests for significant interactions?

Answer 53

Only if:
The F ratio indicates a significant interaction effect

Need to be done manually in SPSS:
SPSS does not allow you to run post hoc analyses on significant interactions, so you have to reduce how best to do them

Be efficient:
If you make too many comparisons your new ‘Bonferroni corrected’ alpha level will be very small (or… your new ‘Bonferroni corrected p-values will be very high) and consequently it will be difficult to reveal sig.difference

Question 54

Methods and when to use for post hoc tests for significant interactions?

Answer 54

LSD (least sig diff) (t-test):
Use when number of comparisons of interest is small or when there are at most 3 conditions to be compared

Tukey:
Use when there are 4 or more groups in between subjects (independent samples) designs AND approximately equal numbers of subjects per condition

Bonferroni:
Use when there are 4 or more conditions in within subjects (repeated measures) designs. Or when mixed model (between and within subject variables)

Question 55

How to manually do the Bonferroni correction?

Answer 55

0.05/ number of tests = new alpha/sig threshold (compare to original p-values)

Or

p-vlaue x No. of tests = new p-value (compare these to 0.05)

So say there were 5 tests, could do 5 paired t-tests, then have to compare the p-values to 0.01 significance as we did 5 tests

This method works when only some points are significantly different and some others, if all the points are significantly can’t identify where the interaction is, so have to use a different method in comparing change

If only do a test comparing where you think the change will occur, will have done less tests, so more likely to find a significant result

So could look at the change between 2 points

Question 56

Reasons why during the cycling protocol why the training intervention might not be the only reason why they improved fatigued next time?

Answer 56

Improved from familiarity

Those who did poor practiced and got fitter over 6 weeks

This is why we need a control group

Question 57

Difference between independent samples and repeated measures?

Answer 57

Independent samples: The levels in one factor represent different groups, where each person only provides data for one of the groups

Repeated measures: The levels in one factor represent different conditions/time-points, where each person provides data at every level of this factor

Question 58

How to interpret your interaction?

Answer 58

Sometimes the between groups effects are of more interest (eg. when groups are intervention or control)

Sometimes the between groups effects are secondary (eg. when grouped by gender. The ‘experimental conditions’ are the repeated measures ie.e everyone does all experimental conditions

Question 59

Description of a two-way mixed design ANOVA?

Answer 59

Two groups of people were either told to practice putting explicitly (told instructions) or implicitly (learn by themselves) they then underwent 5 tests, the first 4 were casual and the last one was under stressed conditions

3 hypotheses tested:
Is there an interaction of learning style and trial number on putting performance?

Is there effect of learning style on putting performance? Is there a sig difference in the overall putting score (mean of all 5 trials combined) between the ‘explicit instructions’ and ‘implicit learning’ groups

Is there an effect of trial number on putting performance? (is there a sig difference in the overall putting score (mean of both ‘learning styles’ combined) between any 2 of the 5 trials?

To find an interaction can do t-tests before and after where you think it, one difference will be sig and the other won’t (most likely) can then confirm this with a t-test looking at the change between the 2 points

Question 60

Why is Sample Size an ethical issue?

Answer 60

Too big = overpowered, you could have shown a statistical difference with fewer people, you are wasting resources and time, and put unnecessary discomfort on participants

Too small = underpowered, could fail to indetify a true meaning, unethical as wastes resources, time and could lead to misleading conclusions

Question 61

Sample Size calculation?

Answer 61

Desired Power (1-B): Should not be less than 80%. Funders may request higher

Desired significance level (a): 5% (p=0.05)

Effect size (standardised - d (t-test), n^2(analysis of variance), R^2(regression)

Question 62

What is Power?

Answer 62

(1-B) is the probability that your study will find a statistically sig effect (if the anticipated effect size is the true effect size)

E.g. Testing a drug that is known to reduce SBP by 10mmHg (Cohen’s d=1.2)
A ‘Effective Drug’ trial is conducted 100 times on 100 different samples of 8 people. Having
80% power means we would get a statistically sig. effect of the drug in 80 trials and fail to get a statistically sig. effect in 20 trials, even though the drug is ‘known’ to be effective (↓SBP=10mmHg; d=1.2).

Way to visualise this is imagine having 2 bell curves, 1 for the placebo and one for the intervention that is known to be effective (8 participants), the end of placebo curve (2.5% (which represents p < 0.05 as 2.5% at each end of curve) represents the cut off point for the intervention (80%), so even if get a result in the right direction for the intervention if does not land in that 80% is not significant

Bigger the amount of participants the tighter the normal curves become as less chance for randomness, so the overlap between intervention and placebo curves becomes smaller. So say we now have 16 people in, that overlap wouldn’t represent 80% anymore, but would represent 99%

Drug that reduces BP by 10mmHg (d=-1.2), is trialled 100 times (each with n=16), 99 trials will identify a significant effect, only 1 will not

Compared to Drug that reduces BP by 10mmHg (d=-0.8), is trialled 100 times (each with n=16), 80 trials will identify a significant effect, 20 will not. (this is because the -0.8 is less of an effect than the -1.2)

Question 63

Conclusion of a larger sample size?

Answer 63

A larger sample size can………..

give you more power to find the same effect size

and/or

give you the same power to find a much smaller effect size

Question 64

Determing the ‘size’ of effect options?

Answer 64

1.The smallest effect size that would be deemed important to health or performance (e.g. 5mmHg reduction in BP)

2.The likely effect size based on the results of a very similar trial, or data from a number of different studies (mechanistic, pilot, feasibility).

3.Pre-defined values corresponding to small moderate and large effects (if you cannot do points 1. or 2.)

Question 65

The smallest effect size that would be deemed important to health or performance details?

Answer 65

Smaller effect sizes require larger samples sizes. If you say 5sec longer in Time-To-Exhaustion endurance test (of ~10min) is important, then you may need 1000+ people to be confident that this is a real difference and not one due to random variation.

Larger effect sizes require smaller samples sizes. However, you shouldn’t choose a large effect size as a way of justifying a small sample as you might wrongly conclude your intervention was ineffective.

E.g. If you power a beetroot intervention to detect a 20% reduction in BP, and yours leads to a 10% reduction, you will conclude beetroot is ineffective at reducing BP (yet 10% is still beneficial to health)

Question 66

The likely effect size based on the results of a very similar trial, or data from a number of different studies (mechanistic, pilot, feasibility).

Answer 66

Easy if trial is very similar! (be guided by the ‘effect size’ not the ‘sample size’ as it may have been non-sig!). Better to use 2or3 studies rather than just 1.

If no trials are ‘very similar’, you can still make an informed estimate from different aspects of several ‘slightly similar’ or ‘relevant’ studies.

IT IS NOT AN ‘EXACT’ SCIENCE

The estimate of the likely effect size is based on mean effects, SDs and correlations that are themselves obtained from small studies.

However, this is still much better than the ‘always n=10’ approach.

Question 67

Pre-defined values corresponding to small moderate and large effects?

Answer 67

t-tests
Effect: d
small = 0.2
medium = 0.5
large = 0.8

ANOVA
effect: Eta^2 (n^2)
small = 0.01
medium = 0.06
large = 0.14

Regression
Effect: R^2
Small = 0.01
Medium = 0.09
Large = 0.25

Question 68

How to work out effect size dz?

Answer 68

(Change of Mean differences)/(SD of diffs)

SD of differences isn’t worked out just be subtracting one groups means from the other like the change of mean differences is done

Have to do (upper quartile - lower quartile from 95% confidence interval of the difference) / 4, then multiply this by the square root of the amount of subjects in the study

Question 69

What do you do to your sample size if you are told a drop out rate?

Answer 69

Calculate how many people this is, then add it on top of your calculated sample size

Question 70

Questions about G power sample size in the exam will only have one about the output, the rest will be more broad questions

Answer 70

ok

Question 71

If you converted a study looking at creatine supplementation on cycling effect from young people to old people what might you have to change for sample size calculation?

Answer 71

A lower correlation might happen as older people are less consistent on a Wingate, or a wider variation in the effectiveness of creatine = effect size goes down, need more participants

Pre and Post SDs might increase to 0.75 reflecting a wider range of ability in old people = effect size goes down, need more participants

Question 72

What is the correlation Coefficient (r)?

Answer 72

Summarises the strength and the direction of the linear relationship (sign of the number shows the direction, the size of the number shows the strength)

Can take any 2dp number between -1 and +1

Question 73

Different strengths of relationships?

Answer 73

1 = perfect
0.7 = strong
0.5 = moderate to strong
0.3 = moderate
0.1 = weak
0 = none

Same applies for negative

Question 74

What does SPSS report r and p as?

Answer 74

r = Pearsons

ion

p = Sig. (2-tailed)

Question 75

When you report a correlation what are you comparing to?

Answer 75

You say there is a significant difference in r compared to r being 0

Question 76

Assumptions for Pearsons correlation?

Answer 76

Continuous scale

Normal distribution - not an issue if just falls outside the -1 to +1 value if none of the other factors do, if more do fall out, should use the spearman rho or Kendall’s tau as non-parametric alternatives in same window as Pearson’s

Linear relationship - if the points tend to cluster around a line, but this is not a straight line, the relationship is described as non-linear or curvilinear

No obvious outliers - has to be evaluated (measurement error or not from population of interest)

Homogeneity of variance - homoscedasticity is good (2 parallel lines can encompass data), heteroscedascity is bad (fan or funnel shape goes around data) - have to eye ball this no formal test

Question 77

Why correlation is not causation?

Answer 77

Just because 2 variables are correlated does not mean that one causes the other, the causal factor could be a 3rd variable that you haven’t analysed or even measured

E.g. for the correlation of Fat% v CVhealth a 3rd variable ‘stress’ could be causing high levels of fat (eating more due to stress) and independently of of fat, stress could be causing poorer CVhealth because of increased cortisol heroine that causes stress

Question 78

What can regression analysis do?

Answer 78

We can see how much of the variance in one variable is ‘explained’ by variance in the other

At a basic level we square the correlation coefficient eg. r = 0.615 would become R^2 = 0.38

So in BMI vs Fat% this would mean BMI explains 38% of the variance in Fat% or visa versa as r = 0.615 and then R^2 = 0.38

So BMI explains 38% of the variance in Fat%

Question 79

Variance explained in regression?

Answer 79

100% of the variance is found by the sum of the squares (distance from line squared from each point then all added up) compared to the mean line

If you then do the same sum of squares compared to the line of best fit and work that out as a percentage of what it is compared to the mean line, then that difference will be the R^2 value

Question 80

How to get an equation to predict from a r value?

Answer 80

Y = b0 + b1X

Y = the dependent variable (outcome we are trying to predict)

b0 = the intercept of the y axis (when x = 0)

b1 = the slope or the regression coefficient

X is the value of the independent variable (the measure your using to predict the other)

Question 81

How does simple linear regression lead to multiple linear regression?

Answer 81

When you have made the equation, you will see there will still be differences between that an the data points, these are residuals, so we need to know more information to make a better prediction

Question 82

How is simple (bivariate) linear regression different to multiple linear regression?

Answer 82

Linear has one independent variable

Multiple has multiple independent variables (each variable might explain a certain amount of variance in the dependent variable, but when you put them all together will not be the sum of the variances)

(imagine it like a van diagram with circles overlapping each other)

Question 83

Multiple Linear Regression Equation?

Answer 83

Y = b0 + b1X1 + b2X2 + b3X3…

Thumb rule: more than 10 people per variable in final model

Question 84

Types of multiple regression module building processes?

Answer 84

Stepwise multiple regression (forward) SPSS selects which variables are entered into the model and in what order based on their strength of association with the outcome variable

Hierachial multiple regression
Experimenter decides the order in which variables are entered

Forced entry multiple regression
All predictors are entered into one model at the same time

Question 85

Describe Stepwise regression?

Answer 85

SPSS identifies the IV which explains the most variance in the DV and puts that in the model

The IV which explains the most of the remaining unexplained variance (the residuals of the first model are now the DV) is then included in the model provided it explains a significant amount of the remaining variance

This process is repeated until there an no IVs left that explain further variance p < 0.05

IVs in the model can get taken out if they become ‘clearly no longer significant’ p>0.10 due to the entry of a new variable

When used?
Data driven, not theory driven. Researchers chose a list of variables they think are likely to be predictive but not sure as not enough research has been done to inform them. Also useful if you have lots of variables which you couldn’t possibly know which are most predictive

Question 86

Describe the hierarchal multiple regression?

Answer 86

Researcher decides what order the IV are entered

Order of entry should be based on previous research or theory

Usually possible/known confounders such as gender, age, ethnicity, social class, smoker, alcohol, birthweight

Can examine R^2 - can see the amount of additional variation that is explained every time you add another variable to the model

When used? If you want to know whether a certain variable of interest is significant when you add it to a model that already contains a ‘known’ predictor or confounder

Question 87

Describe forced entry multiple regressions?

Answer 87

All predictors are forced into one model simultaneously (irrespective of whether sig or non sig)

Should be a theoretical rationale for including all variables

When use? Does not determine the unique variance that each IV adds to the model. However you can re-run the analysis manually with the most non-sig variables removed (manual backward elimination method)

Question 88

When looking SPSS multiple regression what model do you look at to get values?

Answer 88

Look at the final model

We also aren’t concerned about looking at ANOVA box during these analyses

Question 89

Assumptions for multiple linear regression?

Answer 89

No Multi-collinearty between IVs in model - the square root of the ‘variance inflation factor (VIF)’ indicates how much larger the standard error is due to the co-correlation
No r values above 0.9
Largest VIF should be smaller than 10, if 2 are bigger than 5 remove one of them
Average VIF should should not be considerably bigger than 1

Homoscedasticity of residuals - look at the graph of regression standardised predicted values
Should be spread equally throughout the parallel lines, fuelling or fanning represents heteroscedascity - this represents high accuracy at parts where not very fanned and low accuracy where very fanned

Linearity of residuals - look at same graph of regression standardised predicted values, and imagine putting a line of best fit though it, if not violated this will be horizontal
This means the relationship is linear or that any non linear pattern in the outcome measure has been accounted for by our predictor values

Normality of residuals - looking at a normal bell shaped curve on histogram

Extra:
Casewise diagnostics
Used to identify outliers and potentially influential cases
In a normal sample 95% cases roughly + or - w, therefore about 5% will have residuals outside the + or - 2
So calculate roughly how many we expect to see fall out of the range and see if there’s actually a lot more shown in the case wise diagnostics and if this then violates the assumption

Question 90

Assumptions for simple linear regression?

Answer 90

Homoscedasticity of residuals

Linearity of residuals

Normality of residuals

Question 91

What is a moderator variable?

Answer 91

A moderator variable is one that affects the direction and/or strength of the relationship between an IV and Dv

Does the relationship between the predictor (IV) and the outcome (DV) carrying depending on the level/value of the moderator variable?

Moderator variable can be continuous or categorical (so example for social support could be amount, or we split them into people who have 3 or more up to 5, or 2 or less)

They are often categorical eg. gender 1 or 2

So its basically an interaction

Question 92

Moderator equation for the example of golf performance being effected by stress and social support?

Answer 92

So the moderation bit is Stress X Social Support

Performance = Intercept + b1(stress) + b2(social support) + b3(stress x social support)

Question 93

Why do we use hierarchal input for multiple regression looking at moderator?

Answer 93

Stepwise not suitable as if interaction is sig, then the non-sig main effects need to be in the model

Question 94

Is multi collinearity an issue when looking at moderators in multiple regression?

Answer 94

Multi-collinearity can make choosing the correct predictors to include in the model very difficult. This is because it interferes in determing the precise effect of each predictor because they are so correlated/similar

However, multi collinearity doesn’t affect the overall fit of the model or produce bad predictions!!! So it is okay to know what variables are going in to the model.

Question 95

Hinted to be in Exam, how do you work out golf score if given stress level and social support level?

Answer 95

Plug into the equation

Equation might be separated out categorically, ie a high support equation and a low support level, so need to plug numbers into relevant equation

These lines are also useful to show the interaction effect.

Question 96

Chi-square test compared to Logisitc regression?

Answer 96

Chi-square - Testing the difference in the ‘proportions of yes answers’ between 2 or more categories/groups

Logisitic regression - converting those ‘proportions differences’ into Odds Ratios (OR) for multiple ‘risk factors’ simultaneously in a model-

(Outcome/ the dependent variable must be dichotomous (Yes or No), and risk factors/independent variable can be continuous, categorical or dichotomous

Question 97

Assumptions of logistic regression?

Answer 97

Does not make any assumptions of normality, linearity and homogeneity of variance.

However you should check for very big’ outliers in the continuous risk factor/independent variables

The minimum expected count in any’ outcome x risk factor’ cell must be greater than 5

Sample size requirements:
For a large effect size, the preferred people to variables ratios are

20-1 for simultaneous or hierarchal logistic regression

50-1 for stepwise logistic regression

Question 98

Goodness of fit in logistic regression?

Answer 98

The goodness of fit of the model is measured by’-2 log likelihood’. However, this is not easily interpretable so a pseudo R^2 value is provided

The pseudo R^2 is not comparable with linear regression R^2 as predicting risk of yes/no. not a continuous value

Question 99

How to work out odds ratio
?

Answer 99

The amount of people who are yes in that category/ the amount of no in that category

Then do that for the other category

Then divide the first value by the next value if wanting an answer for the first category

Question 100

Small odds ratio?

Answer 100

Logistic Reg (odds ratio):
Larger than 1.5, or smaller than 0.67

Correlation (r and R^2):
r is smaller than 0.01 or 1%

Question 101

Moderate odds ratio?

Answer 101

Logistic Reg (odds ratio):
Larger than 3, or smaller than 0.33

Correlation (r and R^2):
r is smaller than 0.30 or 25%

Question 102

Large odds ratio?

Answer 102

Logistic Reg (odds ratio):
Larger than 5, or smaller than 0.2

Correlation (r and R^2):
r is smaller than 0.5 or 25%

Question 103

What is Exp(B)?

Answer 103

odds ratio

If Exp(B) is bigger than 1then the odds are greater in that category compared to the reference category

If Exp(B) is smaller than 1, the odds are smaller in that category compared to the reference category

To work out the percent more or less than category will do something do (odds ratio - 1) x 100 (the other gender won’t be the exact opposite of what you got from the other odds ratio)

(be careful of what value you are looking at SPSS as it depends on the reference and last category entered)

Question 104

How to work out odds ratio from chi square SPSS table

Answer 104

Say wanted to find out females

Would do odds of females in the count (yes/no)

Would do odds of males in the count (yes/no)

Divide the females value by the males value

Question 105

What does Cox and Snell ‘pseudo R^2 value’ mean?

Answer 105

Quantifies the goodness of fit’ of the model

Question 105

What does Cox and Snell ‘pseudo R^2 value’ mean?

Answer 105

Quantifies the goodness of fit’ of the model

Question 106

Whats in intra-class correlation (ICC)

Answer 106

ICC - data from the 2 variables are centred and scaled using a pooled mean and SD (so comparing the 2 measures)

Then compared to Pearson’s correlation (r) - when each variable is centred and scaled by its own mean and SD (looking at one of the measures)

Question 107

How to read/interpret an ICC in SPSS?

Answer 107

For example The ICC = 0.70 (p<0.001) indicates a moderate-to-good level of agreement between the ACC and the LOGBOOK for measuring sitting mins/day

Look for Single measures value, then the signifificance

Question 108

What are Bland-Altman plots used for?

Answer 108

Bland-Altman plots are used to evaluate the agreement between 2 measurements methods or techniques often between a new measurement instrument and a gold standard/criterion method (criterion validity). This is how ‘new’ tools/devices/questionnaires are developed (multiple interactions vs criterion measure

Bland Altman plots allow identification of any systematic difference between the measurements

Question 109

How to interpret a Bland-altman plot example?

Answer 109

On average the accelerometer recorded 15min/day less sitting than the logbook (shown from mean line)

However, the 95% LoA (mean diff +/- (1.96 x SDdiff)) show that for 95% of individuals these differences range from 147min/day less, up to 118min/day more (both shown by confidence interval lines) sitting time compared to logbook

Modify either tool to reduce 95% LoA, could apply a 15min adjustment factor

Question 110

Variables on each axis on bland Altman plot?

Answer 110

Mean of the 2 methods (Method 1 + method 2)/2

Diff between the 2 methods (Method 1 - method 2)

Question 111

When copying and pasting his answers make sure to tweak them so TurnItin doesn’t flag it

Answer 111

ok

Question 112

How to report a t-test result example?

Answer 112

is on week 4 worksheet

Question 113

Can you use overlapping confidence intervals as a sign of significance in paired or independent t tests visually?

Answer 113

Can only do it in independent, can’t do it in paired

In independent t-tests can see if crosses 0 to represent hasn’t significantly changed

Question 114

How to work out f ratio from mean squares?

Answer 114

F ratio = Mean square between groups / mean square within groups

Question 115

When getting degrees of freedom for an ANOVA where do you get them in the SPSS output?

Answer 115

You get the first one from the effect your looking at, and the other from the error row