PSYC210

0.0(0)
studied byStudied by 2 people
learnLearn
examPractice Test
spaced repetitionSpaced Repetition
heart puzzleMatch
flashcardsFlashcards
Card Sorting

1/231

encourage image

There's no tags or description

Looks like no tags are added yet.

Study Analytics
Name
Mastery
Learn
Test
Matching
Spaced

No study sessions yet.

232 Terms

1
New cards
Correlation
The relationship between two or more dependent variables. Does not include independent variables. Correlation can be used to describe and predict behaviour but not to explain or infer causality.
2
New cards
Positive correlation
As one variable gets bigger, the other variable gets bigger.
Graph tilts upwards from left to right.
3
New cards
Negative correlation
As one variable gets bigger, the other variable gets smaller.
Graph tilts downward from left to right.
4
New cards
Zero correlation
No consistent relationship between variables. Scattered points are in no pattern.
5
New cards
Correlations vary in terms of strength
The stronger the correlation the better the predictability.
6
New cards
Curvilinear relationship
An increase in x results has an initial increase in y, then a decrease in y., e.g., yerkes-dodson arousal curve.
7
New cards
Pearson's r
A way of expressing correlation. Ranges from -1 to +1.
8
New cards
Things to note about Pearson's r
- Variables to be correlated must be measured on the same individuals.
- Variables must be measured on an interval or ratio scale.
- It's linear because points generally fall on a straight line.
- For every change in x there is an equal constant change in y.
9
New cards
If r \= 0 (or low r)
There may be no relationship or it may be the existing relationship is non-linear.
10
New cards
Directionality problem
It is hard to determine if one thing determines the other or the other way round.
11
New cards
Cross-lagged-panel correlation procedure
A way of dealing with the directionality problem to a certain extent. Measure the correlation of two variables with each other over time. Can also look at the diagonals. If one variable causes the other it should be more strongly related over time.
12
New cards
Inferential statistics
Used to decide about the population based on observations of the sample.
13
New cards
Parameters
Characteristics of a population we are interested in. These are the mean and the standard deviation.
14
New cards
Three steps to sampling distributions and logic
- Make a guess about the population frequency distribution (mew - population mean).
- Take a random sample.
- Decide if the sample came from a population like the one you guessed in step one (usually based on how close the mean of the sample is to the hypothesised mean).
15
New cards
Hypothesis testing
Used to determine whether a population differs from a sample because of variability or a true difference.
16
New cards
Null hypothesis
The treatment has no effect and the difference we observe is due to variability and not a true effect.
17
New cards
Alternative hypothesis
The treatment has an effect and the difference is due to a true effect.
18
New cards
Significance level
How much different the samples must be from the population mean.
19
New cards
Region of rejection
The area of the distribution where you would reject the null hypothesis if the test results fall onto that area.
20
New cards
Critical values
Values on a distribution used to decide whether or not a statistical hypothesis test is significant or not.
21
New cards
Critical values and region of rejection
area on normal distribution, if the observed statistic is in region of rejection we reject null hypothesis and accept alternate hypothesis
22
New cards
Region of rejection/critical value on one-sided t-test
one critical value/area on graph for rejecting null hypothesis
23
New cards
Region of rejection/critical value on two-sided t-test
two critical values/areas on graph for rejecting null hypothesis
24
New cards
Increasing alpha level eg to 0.1
reduces type 2 error (false negative), more accurate, increases power, increases probability of correctly rejecting null hypothesis when it is false
25
New cards
Other impacts of increasing alpha (type 1 error)
increases type 1 error (false positive), set alpha very low eg 0.01 when consequences of type 1 error are severe eg drug trials in pregnant women, set alpha higher eg 0.05 when consequences are not as severe
26
New cards
Significance level or alpha level
The probability value that defines the boundary between rejecting or retaining the null hypothesis.
27
New cards
P < alpha
Reject the null hypothesis.
28
New cards
P \> alpha
Retain the null hypothesis.
29
New cards
One-tailed test
Used when there is evidence or theory to suggest that the treatment will have an effect in one particular direction.
30
New cards
Two-tailed test
- Used when there is another directional alternative.
- Used if there is no reason to predict the direction of effect.
- Alpha is still 0.05 but divided by two so the scores need to be higher or lower to hit the region of rejection.
- More conservative and less powerful.
31
New cards
Alpha
Probability the null hypothesis is true given the data.
32
New cards
Type 1 error
Rejecting the null hypothesis when it is true (alpha, false positive, eg conclude treatment is effective when it isn’t
33
New cards
Type 2 error
Retaining the null hypothesis when it is false (beta), false negative, eg conclude treatment is not effective when it is
34
New cards
How to reduce beta/type 2 error and increase power (1-beta)
- Increase alpha (reduces type 2 error and increases power).
- Increase n (less variability).
- Use most powerful statistical test.
- Have a good experimental design.
35
New cards
Estimated standard error of the mean
A useful number but we typically don't know the standard deviation.
36
New cards
Degrees of freedom
How many scores in the sample are free to vary- generally all scores except the last one.
37
New cards
Assumptions of the single-sample t-test
- The random sample comprises interval or ration scores.
- The distribution of individual scores is normal.
- The standard error of the mean is estimated using the s computed from the sample.
38
New cards
Steps for t-test
- Calculate the observed t using the estimated standard error of the mean.
- Determine the df.
- Look up the critical t in the t- table with the appropriate df (and alpha).
- If the observed t is greater than or equal to the tabled value than reject the null.
39
New cards
Two-sample t-test
Compare the difference between two sample means. Taking into account the sampling error by calculating the estimated standard error of the difference between the sample means.
40
New cards
Four goals of statistical analysis
- Describe: patterns of data.
- Explain: from hypothesis testing to real world.
- Predict: from models to future real world.
- Control: techniques to control for randomness.
41
New cards
Qualitative research
People and context.
- Interviews, observations.
- Themes.
- Results described in language.
42
New cards
Quantitative research
People and numbers.
- Patterns of numbers.
- Results described by statistics.
- Statistics translated into language.
43
New cards
Confirmatory research
- Describe
- Explain
- PREDICT
- CONTROL
Deductive, top-down.
Theory -\> hypothesis -\> pattern -\> observation.
44
New cards
Exploratory research
- DESCRIBE
- EXPLAIN
- Predict
- Control
Inductive, bottom-up.
Observation -\> pattern -\> tentative hypothesis -\> possible theory.
45
New cards
Confirmatory analysis
Prediction.
Control.
Description.
46
New cards
Central Limit Theorum
Different samples from the same population have different means.
47
New cards
Outliers
Individual data points that differ a lot from most of the others.
48
New cards
Conformity effect can be controlled by:
The independent variable.
49
New cards
Random variation and sampling error...
Cannot be controlled.
50
New cards
Controlled experimental study
- Experimenter directly controls changes in IV to observe how a DV changes.
- Exploratory.
- Confirmatory.
- Manipulation may have caused effect.
- Control for possible confounding factors or variables.
51
New cards
Correlational study
- No direct control.
- Relies on associations between variables that already exist.
- Often used in exploratory.
- Once aware of a relationship a researcher can design experiments that discover which of the two variables is causing the change in the other.
52
New cards
Multiple regression
Using more than one predictor variable.
Should improve predictive strength of our model.
Will also probably improve how well the regression equation model describes our sample data.
53
New cards
Neurons
Communication tools from one part of the brain to the other. Densely connected and have many dendrites.
54
New cards
Axons
Conduct electrical signals.
55
New cards
Myelin
A fatty substance surrounding axons. A major factor in determining the MR (magnetic resonance) signal and contrast.
56
New cards
The brain is organised into two types of 'tissues'
- Grey matter (composed cell bodies). Round the outside.
- White matter.
57
New cards
MRI is:
- Noisy. Measurements won't be perfect.
-Variable/configurable.
58
New cards
Analysis
- Based on statistics.
- Has many options/alternatives.
- Has more than one 'right' way (but many wrong).
59
New cards
Diffusion
Movement of particles across the space available to them.
60
New cards
Corpus Callosum
Connection between two hemispheres. Predominantly made up of white matter.
61
New cards
Gradient coils
Create magnetic field changes in any direction.
62
New cards
What does hydrogen do in an
MRI
Is present in the water. Hydrogen has magnetic properties and so has a magnetic moment where the ions line up in a specific direction.
63
New cards
Structural MRI
Images of gross brain anatomy at resolution
64
New cards
Structural MRI limitations
- Does not measure tissue type directly.
- The absolute values are not the same across all scanners.
- Measurement is in millimetres.
- Does not always distinguish bone from air.
- Contrast can be poor.
- A single sequence does not show all pathologies.
- Lots of artefacts and noise.
65
New cards
Complementary techniques to structural MRI
CT (with/without contrast).
- Shows bones, membranes, vessels, and tumours.
- Can't measure brain function.
Histology.
- Shows microstructure.
66
New cards
Structural vs diffusion MRI
Colours are different and distortion at front of the brain.
Structural MRI takes about five minutes to acquire one image.
Diffusion MRI takes lots of fast images (one every 1-3 seconds) for about five minutes.
Diffusion MRI has lower resolution (1-3mm).
67
New cards
Diffusion MRI
- Measures the direction of white matter fibres in the brain.
- Can give indications about integrity of the white matter.
- Can provide information on the connections between anatomical structures.
- Need to acquire many 'directions'.
68
New cards
Complementary techniques to diffusion MRI
Tracer studies (individual fibres in post-mortem brains).
Histology (myelin/axon dimensions/glia). Post-mortem only.
69
New cards
Functional MRI
- Measures brain activity by detecting changes associated with blood flow.
- Used to obtain functional information by visualizing cortical activity.
- Distortion in the front of the brain due to quick images.
- Can be undertaken when someone is performing a task or while someone is resting.
70
New cards
Structural vs diffusion vs fMRI
Structural MRI takes about five minutes to acquire one image.
Diffusion and fMRI take lots of fast images which are more susceptible to artifacts.
Diffusion and fMRI have lower spatial resolution than structural MRI.
71
New cards
The Haemodynamic response
The blood response to underlying neuronal activation.
72
New cards
Activated state
Oversupply of oxygenated blood to an area.
Increased cerebral blood flow and increase in cerebral blood volume, increase in oxygen supply, increase in MRI signal.
73
New cards
Experimenting with fMRI
Three types of events namely word generation, word shadowing, and null event. The null event acts as the baseline to compare with stimulation. The null event contains a visual component but no word component. Different tasks can be done and repeated to see what part of the brain lights up.
74
New cards
Verb fMRI experiment
If given a noun had to generate a verb, if given a verb had to shadow the verb.
Quite different areas of activation for the two tasks.
Generation activation \> shadowing activation.
Due to activation of Broca's area.
75
New cards
Broca's area
Contributes to speech production and fluency.
76
New cards
Limitations to functional MRI
- Does not measure electrical activity.
- Does not measure metabolic activity.
- Does not measure changes in blood oxygen levels resulting from electrical and metabolic activity of active neurons.
- BOLD (Blood oxygenation level-dependent) -fMRI is qualitative (change from baseline is important, not baseline itself).
- Sensitive to fast imaging artefacts.
77
New cards
Complementary techniques to fMRI
- Positron Emission Tomography (PET): measures brain metabolism directly. Can use radioactive tracers. Slower temporal resolution than fMRI. Reduced spatial resolution than fMRI.
- Electroencephalography (EEG): measures electrical brain activity directly. Much faster temporal resolution than fMRI (milliseconds). Reduced spatial resolution than fMRI.
78
New cards
Qualitative data
- Quotes or images.
- Capturing the original quality of the data.
- Non-numeric.
- Primarily involves the human experience.
79
New cards
Theme
A patterned response or meaning within the data set.
- Category.
- Dominant discourse.
Not simply a topic discussed by the participant.
80
New cards
Ontology
Views on human reality
81
New cards
Quantitative research (realism)
There is one 'true' reality (independent of perception)
82
New cards
Qualitative research (relativism)
People's realities differ (relative to perception).
Our knowledge of reality is never a simple reflection of the way the world actually is, but is created and sustained through subjective social processes.
83
New cards
Epistemology
What we know and how we know it
84
New cards
Quantitative research (positivism)
Knowledge and meaning is waiting to be discovered and is then considered 'true' until disproven (through research).
85
New cards
Qualitative research (social constructionism)
Knowledge and meaning is being generated by attempts to explain the human world (including research)
86
New cards
Epistemology key points
Researchers should know which epistemology they are using.
Different approaches to research generate different knowledge.
There are more than these two.
87
New cards
Why use qualitative research?
If it involves people then the meaning matters.
Numbers can't always do this.
88
New cards
Five key elements of qualitative research questions
- State a goal.
- Define the population sample.
- Define the setting.
- Identify the primary topic.
- Be precise enough to be feasible.
89
New cards
Closed questions
Imply fixed answer choices.
90
New cards
Open-ended questions
Invite expansion.
91
New cards
Three types of interview structure
- Structured (quantitative)
- Unstructured (research development and pilot studies)
- Semi-structured (qualitative).
92
New cards
Structured interviews
- Closed questions.
- Very fixed topic and fixed order of questions.
- Very clear roles.
- Expansion allowed only if pre-defined \= branching.
Outcome: specific answers + numerical data \= quantitative.
93
New cards
Semi-structured interviews
- Open-ended questions (or probing following closed questions).
- Very open around a topic and question order can vary.
- Almost equal roles.
- Expansion encouraged (on topic).
Outcome: open-ended answers + verbal data \= qualitative.
94
New cards
One-on-one interviewing
- Most common source of qualitative data.
- Smaller samples are better.
- Smallest possible sample is a case study (n\=1).
- Case studies allow exploration of the one case in depth.
- Most one-on-one interview studies have more than one participant.
95
New cards
De Visser and Smith (2006) aim
Discourse: patterns of language about a concept.
Identity: notions of self that draw upon available discourses.
96
New cards
De Visser and Smith (2006) three key issues
- Relationships between different discourses of masculinity.
- Health-related behaviour in the performance of masculine identities.
- Meanings of masculine behaviour and masculine identity to young men.
97
New cards
De Visser and Smith (2006) key concept
Hegemonic masculinity: expected patterns of behaviour among men.
- Playing or watching sport.
- Drinking alcohol.
- Predatory masculinity.
- Interpersonal violence.
98
New cards
De Visser and Smith (2006) research questions
How do men come to identify with a particular discourse of masculinity?
What does it feel like for a man to reject hegemonic masculinity and manage an alternative masculine identity?
99
New cards
De Visser and Smith (2006) participants
Sample \= one participant (case study).
Rahul: first year undergrad (19) studying in London.
Born in the UK (parents in India).
Not rich but went to a private school.
100
New cards
De Visser and Smith (2006) method
Semi-structured interview about how Rahul spends his free time. Prompts about:
- Drinking alcohol.
- Sexual activity.
- Exercise and sport.