Quasi-Experiments, Small N Designs and Big Data (Week 13) Flashcards

1
Q

What are the major differences between experimental and correlational research?

A

Exp res includes at least one manipulated variable

Exp res usually involved random assignment to different levels of a manipulated variable

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2
Q

What is a quasi-experimental design?

Provide examples

A

Involves an IV and a DV but participants are not randomly assigned to levels of an IV - there is no manipulation of the IV
Ex: studies on gender differences - gender is not randomly assigned

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3
Q

What are selection effects? - give an example 2 explain
What validity does this threaten?
How could you combat this effect?

A

When testing the success of traditional vs. nontraditional schools it is possible that higher achieving families send their children to nontraditional schools explaining effects

Internal validity

Matched groups - ensure that average SES is the same across both groups

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4
Q

What is history threat?

A

External, historical event that happens for everyone in a study at the same time as the treatment variable
ex: Autism prevalence rates increase coincides with increases in child vaccination

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5
Q

What is a demand characteristic?

Why is this often a problem with quasi-experiments?

A

Participants who know they are in a treatment group expect to get better
We cannot use a blinded design
ex: participants selected to undergo surgery and know they are in treatment group

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6
Q

What is a small-N study?

A

N usually refers to the number of participants - small-N means a small number of people
Case studies involve only one participant studied in great detail

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7
Q

What are some examples of famous case studies discussed in class?

A
  1. HM - Henry Molaison, unable to form long-term memories

2. Genie - feral child, raised in complete isolation until middle childhood

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8
Q

In evaluating treatments with small-N, what is a stable baseline? what is a reversal design?

A

Stable baseline - having a long waiting period of assessment before introducing the treatment
Reversal design - after providing treatment, the treatment is removed to see if performance decreases

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9
Q

Define qualitative research? Name the different types

Hint: there are 6

A

Research that identifies themes instead of using statistics
Types: one-on-one interview, case study research, focus groups, record keeping, ethnographic research, process of observation

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10
Q

What are the pros and cons of small-n studies?

A

Pros: high detail on a small number of participants, able to draw conclusions about a very specific population, some research topics are challenging to study and require small-n
Cons: lack of generalizability, much harder than statistics based, tends to be ignored by most mainstream psych journals

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11
Q

What are big data studies?

A

Collecting and analyzing data on thousands or millions of individuals
Data is usually collected online

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12
Q

What is machine learning?

Provide an example

A

Using algorithms and massive computing power to identify patterns in big data

  • usually involve training an algorithm to perform a task with accuracy
    ex: suicide detection based on social media
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13
Q

Are machines unbiased?

Real world example?

A

No, bias occurs in machine learning just as it does in traditional experiments
Ex: amazon recruiting tool biased against women - resumes that mentioned the word women were heavily penalized, also facial recognition works best on white men

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14
Q

What is an ethical concerns associated with big-N studies?

A

Web-scraping - the process of extracting online data usually through social media posts

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15
Q

What are the pros and cons of big data and machine learning?

A

Pros: generalizability and statistical power, easy to access billions of posts, can detect subtle trends (such as attitudes during an election)
Cons: bias may be difficult to detect, so-called “black box” effect: the solution is so complicated humans can’t understand it, privacy concerns (whether people have properly consented)

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