Neur research final

what is the primary goal of the experimental research strategy

to establish a cause and effect relationship between variables. Changing the independent variable (IV) causes a change in the dependent variable (DV)

how is validity in experimental research strategies

• high internal validity : they are conducted in a controlled and constant lab

• limited external validity: conducted in unfamiliar environment-cant be generalized

how is validity in not experimental research strategies

• high external validity: realistic environments

• limited internal validity: environemnt is not manipulated or controlled

What are factorial designs?

Factorial experiments are experiments where multiple independent variables are manipulated

How does factorial designs correlate to the real world

In the real world, effects have multiple causes thus behavior is rarely attributed to only one variable

Why is it better to examine combinations of different potential causes in an experimental setting of Factorial designs

Examining multiple factors increases external validity and better approximates the real world

What is used for each independent variable (factor) in a factorial designs

A numeral’ the value of the numeral indicates th E number of level of the independent variable

What are 2×2 factorial designs

Usually 2 independent variables, 2 levels for each independent variable and 4 conditions

What is a 4×2 factorial designs

2 independent variables, 4 levels fr one independent variable and 2 levels for the other independent variable; 8 conditions

What’s are 5×3 factorial designs

Two independent variable, five levels for the first IV, 3 levels for the second and 15 total condtions.

What is a 2×3×2 factorial designs

3 independent variables: 2 levels for the first one, 3 for the second one and 2 for the third one; 12 total conditions

In a factorial matrix, how can you know if the main effect is on the row factor or the column factor

Calculate the average of each row and column;

1. If the row average are different, the main effect is on the row factor

2. If the column averages are different, the main effect is on the column factor

In a factorial matrix, how can you know if there is an interaction, from the graph

If the lines are parallel on the graph there is no interaction but if they aren’t then there is.

How do interactions affect the main effect in a factorial matrix

if there is an interaction, the effect of A cannot be described without taking into account the effect of B

What does an interaction mean in a factorial design

It determines how a combination of factors work together to affect behaviour. Occurs when one factor has a direct influence on the second factor/ Factor A can exaggerate/minimize the effects of Factor B

How can you find an interaction in a factorial design just from the matrix

Compare the mean difference in any individual row with the mean differences in other rows. If no interaction, then the size and direction of the differences in each row is the same as the other rows. If there is an interaction then the differences change from one row to another

What is the maximum number of main effects in a factorial design

• 2×2 - 2 main effects

• 2×3 - 2 min effects

• 2×45×8×10 - 4 main effects

• Rule of theme: max main effects = number of factors (IV)

How many interactions can happen in each factorial design

Number of interactions = 2^F - 1 - F (F=factors)

Ex. 2×2 design = 2² -1 -2 =1 interaction

What are the types of factorial designs

1. Between-subjects: independent groups; factorial experiments between different groups of people

2. Within-subjects: repeated measures; factorial experiments using different manipulations in the same individual

3. Mixed design: combination of between and within subjects; one factor is between subjects and another is within subjects

What is pure factactorial, between-group design and its pros and cons

participants are randomly assigned to each cell of the design. - one group for every conditons (4)

cons: individual differences can become confounding variables and increqase the varience of scores

pros: avoid order and sequence effects and can be done in one day

how many subjects are in a pure factorial between-groups factorial design

4 separate groups of n 

ex. 4 separate groups of n = 10 each → 40 subjects

what is pure factorial within-group design and its pros & cons

same individuals participate in ALL conditions.

cons: number of different treatment conditions can be high and time consuming for subjects;. carry-over effect; has to be done in multiple days

pros: fewer individuals needed; reduces problems related to individual differences; can compare between all conditions because all participants are in all conditions.

Which pure factorial design should be chosen if you know individual differences will be large

within group design

how many subjects will be in a pure factorial within-group design

1 group of n

ex. 1 group of n = 10 → 10 subjects total

What is the mixed factorial desgn

combines both between and within subject design; effect of manipulations on two groups of individuals

which factorial design is used in before-after situations between 2 groups

mixed factorial design

how many subjects are in a mixed factorial design

2 groups of n 

ex 2 groups of n = 10 → 20 subjects total

what are the pros and cons of a mixed factorial design

pros: control for unwanted individual differences, while investigating specific individual differences

cons: limits ability to make casual statements about the relationship between variables; limits internal validity

what are advantages of factorial designs

• increases external validity - variation of a single independent variable is unusual in real life situations

• theories with 2+ independent variables can only be tested via factorial designs

• greater experimental control: testing multiple variables in one experiment reduces random variations that arise when comparing in different experiments

what are disadvantages of factorial designs

Too many variables can result in:

• huge experiments and requirement for multiple conditions; a lot of participants

• interactions that are not easily interpreted; might test conditions that don’t naturally exist

• make the experiment too long/unfeasible

why do we need statistics?

to know why a result is significant; to see relationships and do comparisons. Allow for:

• accurate conclusions to be drawn (if results happened by chance vs statistically significant)

• specific quantification of observations

• for observations to be summarised 

what are the two types of statistics

1. descriptive: to describe, organise and summarise data 

2. inferential statistics: to make interpretations from data; whether one result was more significant than another to make conclusions.

what are examples of descriptive statistics

1. organize data: frequency distribution - way to organize/visualize data, doesnt tell mean or median, just the mode

2. summarize data:

• measures of central tendency (mean, median, mode)

• measures of disperdions (standard diviation, variance, range)

• measures of relationship (pearson, spearman)

what is the first step after collecting data

making a frequency distribution

What is frequency distribution

organizes a set of scored by grouping them into a display (graph)

eligible for all sets of measurments

preliminary method of statistical analysis

define mean, median and mode

1. mean: average of scores (balance point of data)

2. median: score in the middle (for odd number of scores, take the average of the two middle scores) - applicable if outliers are present

3. mode: most frequent/standard value -not commonly used

what are advantages and disadavnteages of the mean

Advantages: easily understood and useful for inferential stats

disadvantages: does not produce a representative value when there are outliers

Which measure of central tendency represents the amount each individual would receive if the total were divided equally among them?

mean (add all scores and divided them by the number of individuals)

what is the statistically capability of a nominal scale (categorical info)

only has frequency distributions and mode

what is the statistically capability of a ordinal scale ( rank) 

has only frequency distributions, mode, median and range

what is the statistically capability of an interval scale ( rank with distances) 

has frequency distributions, mode, median, range, standard diviation and can add/substract values

what is the statistically capability of a ratio scale

has ALL: frequency distributions, mode, median, range, standard diviation, can add/substract values, multiple/divide values and has a true zero

What are the different measure od dispersions

1. range: distance from smallest to biggest score ( range = max-min)

2. standard deviation: typical distance away from the mean ( how far each score is from the mean) - S = √variance [distance from mean]

3. standard error: spread of sample means (SE = s/√n)

what is Simple quota sampling

little is known about the characteristics of a targer population but an equal number of participants are selected. 

what are some characteristics of a range

only based of 2 scores but not very informativeas it ignores most of the data

what is the relationship between the mean and the standard deviation

SD uses the mean as a reference point then measure the distance between each score and the mean.

if scores are wide apart from the mean, SD is large

if if scores are close to the mean, SD is small

What can the standard error of the mean provide

a measure of how much difference is reasonabke to expect between a sample and its population

which measure of varience measure how accuratly your smaple reflects the population. “ how certain am I that the sample ean accuratly estimates the population mean”

standard error of variance

how are measures of variance used in practice

1. standard deviation is used when trying to assess sample of identify outlier

2. standard error to report final mean

what are outliers

fall out of normal distribution and can be removed from data set. values 2 standard deviations away from mean are considered outliers

what are inferential statistics

allow researchers to infer and generalize observations to the larger population “how likely it is that the differences between groups is due to random chance”

what is the max p-value allowed in science

5% to be statistically significant

what is a p-value 

the threshold of statistical significance; the arbitrary line between sufficient and not sufficient. ranges fro 0 (results mostly likely not due to chance) and 1 (results most likely due to chance)

what does a low p-value allow

allows to makes inferences (generaliztion) from sample to population bc it is not due to chance

what is the null hypothesis

Start by assuming you’re wrong, then conduct a study to reject null hypothesis.

states that there is no effect; any pattern in the sample os just due to chance (p-value = 1); goal is to show that a treatment has real effect and not due to chance → want a low p-value

what boundary of p-value can reject or fail to reject null hypothesis

• if p<0.05, reject null hypothesis

• if p>0.05, fail to reject null hypothesis

what are some ways to commnicate research

• paper

• posters

• presentations

• textbooks

• podcasts

• theses

what is the most common form of presentation and honours thesis

paper poster up to 4 feet (height) X 6 feet. (width), allows for collaboration

what are the different types of presentation

1. symposium: collection of x minutes talks on the same topic by several people (mini lectures)

2. datablitz or lightning talks: 1-3 minutes

what are some pros and cons of datablitz or lightning talks

pros:

• get people interested in your research

• get to the bottom line fast

• number of graphs shown are minimised

cons:

• How do you fit all the info in 1-3 minutes?

what are things to consider when writing a manuscript

1. type of scientific article

2. choose and appropriate journal with peer reviews

3. work must be consistent with the aims and scopes of journal

what are aims and scopes of a journal

tells us what type of articles does the journal accept (review articles, data,etc)

What is the difference between a primary and a secondary source

primary: study conducted by you and publish your own raw data with all details needed to replicate study

secondary:summarizes info from primary sources and the author’s opinion (invalid) on what primary source is doing.

distinguish between PMID and DOI

PMID: how to find the article in PubMED

DOI: link to the article in any database on the internet 

from the abstract of a paper how can you know if it is primary or secondary

primary has its abstract broken down in sections (background, methods, results, conclusion,…)

What is the correlation between high impact factor and citation number

high impact factor papers will have lots of citations. but low impact factor does not indicate unimportant work (more niche journals naturally get less citations)

what counts as a citation

• For the web of Science: any citations that are then published in the web of Science are counted, anything else isn’t - biased but vetted for accuracy 

• for Google Scholar: anywhere the article has been cited in the entire internet - true reflection of impact factor

what is the process to submit an article

1. write an artice

2. either release a preprint (if impatient) or submit to journal

3. after submiting to journal it either gets declined or the journal is interested

4. if the article is declined, submit to a different journal and restart the loop

5. if journal is interested, itll undergo peer review

6. after peer review, the jounal gets accepted of resent to be revised based on reviewer comments

7. after the article has been revised based on reviewer comments, it get submited to journal again to either be declined or accepted (another peer review loop)

what is a preprint and its pros and cons

research paper that is shared before peer review

Pros: feedback from community, visibility and credit

Cons: not peer reviewed and can be scooped ( someone takes your findings for theur own research and get it published before you)

what is the equation for impact factor

(#citations over past 2 years)/(#articles in the journal in the past 2 years)

How much more than the attrition rate must you recruit

50% more than the attrition rate

what are the most important elements of power analyisis

1. effect size ( difference between mean or proportions of two groups

2. standard deviation (variability within a sample)

how can financial compensation for participation affect study

can introduce confounding variables

What P-value is considered significant

P=0.05

How does biased sample happen

can occur by change or as a result of selection bias

What are the four basic elements of an experimental research strategy?

1. Manipulation

2. Measurement

3. Comparison

4. Control.

What is the purpose of Manipulation in an experiment?

To manipulate the independent variable (IV), which helps determine which variable is the cause and which is the effect.

What is the purpose of Measurement in an experiment?

To measure the dependent variable (DV) and obtain scores for each treatment condition

What is the purpose of Comparison in an experiment?

To compare the scores obtained in one condition with the scores obtained in another condition

What is the purpose of Control in an experiment?

To control all other variables so they do not influence the two variables being examined - involves eliminating or controlling confounding variables

What is a Confounding Variable?

A variable that changes with the independent variable and can affect the dependent variable.

what happens to a obscuring variable when it influences the dependent variable (the result)

it becomes confounding

What is an Obscuring Variable?

A variable that makes changes in the dependent variable hard to observe

what do obscuring variables lead to

Lead to measurement error and noisy data, reducing internal validity.

How can researchers control obscuring variables?

1. By ensuring the manipulation is effective (adequate strength)

2. reducing measurement error (e.g., improving instrumentation or training)

3. and minimizing excessive variation in data (often due to individual differences)

What is the Directionality Problem?

A challenge that occurs when a research study establishes a relationship between two variables, but does not explain the direction of the relationship (which one causes the other)

What are the two critical components of an experiment?

The Independent vs. Dependent variable and the Experimental vs. Control group

In experimental terminology, what is a Factorial design?

A design that includes more than one Independent Variable (IV)

In experimental terminology, what is a Multivariate design?

A design that includes more than one Dependent Variable (DV)

List three methods used for control in experiments.

Holding variables constant (easy for things like light, temperature, noise); Matching; and Randomization

What is the downside of holding variables constant or matching for control?

it may limit generality (external validity)

What is a Control group?

The group NOT exposed to the manipulation

What is a No-Treatment control group?

A control group where subjects do not receive the treatment being evaluated

What is a Placebo control group?

A control group where participants receive a placebo (e.g., a sugar pill) instead of the actual treatment. This is typically used in clinical studies

Define the Placebo effect.

Responses to the fake medication, meaning people experience improvement even though they received a dummy sugar pill. This effect involves the power of expectation and care

Define the Nocebo effect.

Experiencing negative symptoms due to expected side effects - opposite of placebo effect

How does the experimental research strategy typically balance validity?

It allows careful control of the environment, leading to high internal validity, but often uses unfamiliar environments, which limits external validity

What is Internal Validity?

The confidence that the research produces an unambiguous explanation for the relationship between two variables, demonstrating that change in the DV MUST be due to change in the IV within the experiment.

What is External Validity?

The extent to which your experimental result holds true outside your specific study. This involves generalizing results to different samples, settings, or measurements

What is a major threat to internal validity?

Any factor that raises doubts or allows alternative explanations for the relationship between variables (ex. assignment bias, environmental variables, and time-related variables)