Revision

Question 1

How to report Pearson correlation

Answer 1

r (230) = 0.16, p = 0.013

OR r = 0.16, p = 0.013, n = 231

Question 2

How to report Spearman correlation

Answer 2

r s (230) = 0.1, p = .889
OR
rs = 0.01, p = .889, n = 231

Question 3

How to report a t test with Cohen’s d effect size

Answer 3

t (207) = 3.61, p < .001, d = 0.48

Question 4

How to report a Wilcoxin test

Answer 4

W = 5815, p = .098, n = 231

Question 5

How to report ANOVA (Fishers)

Answer 5

F (2, 228) = 16.2, p < .001.

Question 8

Skew (symmetry of distribution)

Answer 8

Positive is tailed right

Question 9

Kurtosis (tail ends of distribution)

Answer 9

Negative kurtosis - Platykurtic
Normal distribution - Mesokurtic
Positive kurtosis - Leptokurtic

Question 10

What is a statistical model?

Answer 10

• A statistical model uses maths to summarises a dataset relative to
  multiple variables.
• A simple description of relationships in the dataset.
• Where descriptive statistics describe the data, inferential statistics
  use statistical models. These models enable you to make
  inferences
  about the data
  , e.g. you can decide whether two variables are
  associated or whether one group is bigger than the other.

Question 11

What is parametric data?

Answer 11

• Parametric data = normal data.
• Non-parametric data = not normal or non-normal.
• So, what’s normal?
• Bell curve.
• Not too skewed (sway to left or right).
• Not too kurtotic (flat or peaky).
• No outliers (extreme values).
• Why do we care?
• Normality is an assumption of some statistical models, mathematically.
• If we violate normality and use a parametric test, we may not be able to trust the
  model estimates.

Question 12

When to use parametric tests on continuous data

Answer 12

has no outliers or they can be removed

Data is not too skewed or kurtotic

non-parametric tests used if has outliers that cannot be removed or is too skewed or kurtotic

Question 13

Testing for outliers

Answer 13

• Box plot, very easy in jamovi.
• The thick line in the middle of the box = median.
• The box itself spans from the 25th percentile to the
  75th percentile (or inter quartile range).
• Whiskers indicate acceptable values (not outliers).
• Any observation whose value falls outside this
  acceptable range is plotted as a dot and is not
  covered by the whiskers = outlier.
• Common alternative: 3 standard deviations
  (SD) from the mean (+/-). (can use z scores for this)

Question 14

What to do if there is an outlier

Answer 14

• Run a non-parametric test.
• Commonly done if it’s a “true” value. E.g. testing went well, the participant
  understood task instructions, but scored very low; this performance represents that
  participants ability.
• Remove the value and leave as missing.
• Commonly done when working with big data sets, where you’re not going to check
  participant records and have plenty of statistical power.
• Remove the value and replace with nearest acceptable value.
• Commonly done in psychological studies.
• Remove value and replace with mean.
• Historical, not commonly done these days.

Question 15

Testing skew and kurtosis

Answer 15

• Shapiro-Wilk test. Very easy in jamovi.
• Takes into account both skew and kurtosis.
• W statistic.
• Maximum value of 1 = data looks “perfectly normal”.
• The smaller the value of W the less normal the data are.
• pvalue (of W statistic).
• Typically, <.05 = non-normal data.
• Therefore, ≥.05 = normal data.

Question 16

Parametric tests

Answer 16

Pearson correlation

T-test
(between groups or within groups)

ANOVA
(IRM, we’ll work with between
groups)

Question 17

Non-parametric tests

Answer 17

Spearman correlation

Wilcoxon test
(2 groups/conditions)

Question 18

Parametric v non-parametric tests

Answer 18

• There are generally non-parametric versions of all parametric tests.
• We do non-parametric tests when our data is not normally
  distributed.
• We do parametric tests when our data is normally distributed.
• Parametric tests have more statistical power, so, they are preferred
  and are generally the default set of tests.

Question 19

Degrees of freedom

Answer 19

• Important to the mathematical
  calculations of parametric and non-parametric tests.
• Based on the quantities of data in your model, e.g. participants or factors. In the
  models we will use in IRM, degrees of freedom (df) will mostly be the number
  of participants - 1.
• For the most part, a higher df = more statistical power.

Question 20

Sampling

Answer 20

Population-based sample
Representative of the population.
E.g. random sample of Medicare numbers.

Convenience samples
Not representative of the population.
E.g. clinic-based or through social media advertisement.

Question 21

Psychology has a WEIRD sampling problem

Answer 21

• The vast majority of published psychological research
  is on western, educated, industrialised, rich, and
  democratic (WEIRD) samples.
• Generalisation is limited when using these samples.
  They are not the norm.
• WEIRD populations represent as much as 80 percent
  of study participants, but only 12 percent of the world’s
  population.

Question 22

Cross-sectional versus longitudinal

Answer 22

• Cross-sectional designs capture data at one point in time.
• Longitudinal designs capture data at more than one point
  in time.

Question 23

Experimental versus observational

Answer 23

• Observation designs do not manipulate any variables.
• Experimental designs manipulate a variable (termed
  condition); participants are assigned to one condition at
  random.
• Quasi-experimental designs do not manipulate any
  variables participants are assigned to a condition based on non-random criteria.

Question 24

Within-subject versus between-subject

Answer 24

• Between-subject designs collect data from participants
  relative to one condition.
• Within-subject designs collect data from participants
  relative to more than one condition (usually all
  conditions). This design is also called repeated
  measures.
• A design can be mixed, with both between- and within
  subject assessments.

Question 25

Qualitative research

Answer 25

• Reminder, qualitative research uses descriptions (what people are saying) rather than numbers (numbers used by quantitative approaches).
• The descriptions are not counted. The focus is on the details of the descriptions.
• Aim is to develop a deep understanding of an issue/area.
• Detailed descriptions of attitudes, emotions, opinions, experiences, etc. More detail than standard (quantitative) scales.
• Range of fields, e.g. education, health, clinical, community.
• Typically small sample sizes (small ns).

Question 26

Reviews: evidence synthesis

Answer 26

• Instead of presenting new empirical data, reviews summarise the state of play in the published literature.
• Three main types of reviews:
• Narrative
• Systematic
• Scoping

Question 27

Research integrity

Answer 27

• Australian Code of Responsible Conduct of Research sets out a framework for responsible research conduct, provides a foundation for high-quality research,
  credibility and community trust in research.
• The University where you conduct the research has
  responsibilities.
• Then you as the researcher has responsibilities, e.g
• conduct research honestly and ethically.
• respect the rights of those affected by their research.
• promote the adoption of responsible research
  practice.
• disseminate research findings responsibly.

Question 28

Ethical obligations

Answer 28

• Detailed in the National Statement on Ethical Conduct
  in Human Research (2007), updated in 2018.
• Before we collect data and run a study, we need ethical approval from the UniSA Ethics Committee.
• National Statement on Ethical Conduct in Human
  Research - ‘Ethical conduct’ is more than simply doing the right thing. It involves acting in the right spirit, out of an abiding respect and concern for one’s
  fellow creatures.

Question 29

Good research practices

Answer 29

To better your research integrity credentials, beyond ethical obligations.

• Don’t be obsessed by the p value.
• Don’t HARK and don’t p hack.
• Report all results, including null results.
• Check, check, check.
• Be open and transparent.

Question 30

Thinking like a psychological scientist

Answer 30

• Use a critical thinking approach
• No to taking things at face value.
• Yes to questioning the validity, authenticity and truth of a report.
• Sources of knowledge
• No to superstition and intuition.
• Yes to rationalism (logical reasoning) and empiricism (via observations). That is how
  you will gain psychological scientific knowledge.
• Always keen in mind variability.
• There’s a distribution behind all averages.
  There is always uncertainty around our
  estimates.
• Variability is sometimes difficult to
  communicate with the general public, but it’s
  very important.
• Risk is not destiny.
• Mind the “gaps”. Everything is on a continuum and not dichotomous in the real world.
• Always keep in mind effect size (versus
  statistical significance).
• Just because something is statistically
  significant, does not mean it is meaningful.