Bbh 310 Exam 1 (Parts 1 And 2)

Front 1

Empiricism

Back 1

- States that knowledge CAN come from sense data

Front 2

Epistemology

Back 2

- The study of which beliefs get to count as known to be true
- "What's your epistemology?" is like asking "What's your standard for deciding you know something?"

Front 3

A big assumption scientists make about the external world is...

Back 3

The external world that I perceive through my senses is real
This is rejecting solipsism

Front 4

Knowledge = ______________ + _______________

Back 4

Belief
Truth

Front 5

Scientific skepticism involves doubting....

Back 5

- Sense data & empirical evidence
- Whether patterns in data are real or a mistake
- Theoretical explanations for why we have the patterns
---> However, irrational to go to great extremes of doubt

Front 6

Scientific epistemology basically is that researchers try to build comprehensive knowledge of the interacting biological and behavioral contributions to health through...

Back 6

RATIONAL interpretation of EMPIRICAL evidence

Front 7

Society offers a conflicting pile of claimed observations, so scientists weed it down by...

Back 7

Requiring documentation

Front 8

People claim all kinds of relationships, so scientists require...

Back 8

Systematic investigation and consistency

Front 9

Scientists call it a valid theory if it...

Back 9

- Unifies all the best quality/comprehensive data
- Has predictive power
---> All else being equal, the simpler the better

Front 10

Observation with documentation involves...

Back 10

- Writing down what you saw or talk to a tape record to make a record (Qualitatively)
- Measuring what you're seeing (Quantitatively/Data)
- Explain what you did and how you did it (Qualitatively/META-DATA)

Front 11

2 Problems with automatic pattern finding

Back 11

1) Nobody has seen it all
2) Even when we see a representative sample, our brains generate SPURIOUS associations (e.g. primacy effect, confirmation bias) and ignore legitimate associations (neglect of probability)

Front 12

Formal analysis of pattern finding doesn't involve thinking something co-occurs with something else, but it systematically evaluates:

Back 12

- Under what conditions
- How strongly
---> In quantitative realm, that's statistics

Front 13

Make up an explanation and test it (x correlates with y), and then if you want to find out if your explanation is useful, __________________ from your explanation a ______________________

Back 13

- DEDUCE
- Prediction (Hypothesis)

Front 14

Deduction

Back 14

- Deduce a prediction/observation, empirical hypothesis, whole experimental design, etc. from a THEORY
- Abstractions to details
- More about confirming existing ideas

Front 15

Induction

Back 15

- Induce a new observation from an observation that happened
- Details to abstractions
- More about exploring the unknown (nothing in mind to make new ideas until you get new data)

Front 16

Abstract to concrete axis:

Back 16

1. Theories
2. Laws
2. Hypotheses
3. Experimental Designs
4. Data Patterns
5. Data Points

Front 17

Investigating causation:
What are the factors in biological determinism?

Back 17

- Genetic effects (specific parts of DNA)
- Chemical exposure effects (changing cell functions)
- Pathogen effects (actions of viruses/bacteria)

Front 18

Investigating causation:
What are the factors in cultural/contextual determinism?

Back 18

- Socioeconomic effects (relationships b/t people and how job/money affects them)
- Intervention effects (who we decide to give money to or health insurance to has an effect on individuals)

Front 19

Investigating causation:
What are the factors in interactions?

Back 19

- Biological influences on cultural practices
--> Ex) Genetic predisposition to smoking
- Cultural practices can affect biology
--> Ex) Certain family situations and households have led to children going through puberty earlier

Front 20

Hard Science

Back 20

- Clear-cut questions, clear definitions, specific correct answers (yes or no answer you can trust)

Front 21

Soft Science

Back 21

- Unclear what is the real question, unclear if what is being measured represents the real thing; there's lots of truth to a whole slew of answers depending on different perspectives
--> Ex) "Will gun violence decrease if we make it harder to buy guns?" led to research about the effects after banning assault weapons, which was not profound (no control group, no extreme certainty whether it helped or not, and unclear [definition of weapon])

Front 22

Quantitative measurement starts with a concept and definitions of what we're trying to measure/how (the construct), then...

Back 22

- Create a way to represent variance in the observations along a number line (the scale)
- Devise a system to physically convert observations into specific measurements (data)

Front 23

Since no measurement is exact or perfect,
Measured Value = ____________ + _____________

Back 23

True Value + Error

Front 24

Two types of Error Variance:

Back 24

1) Consistent Errors
2) Random Errors

Front 25

Consistent Errors

Back 25

- Systematic biases
- If identified and measured, they can be removed (calibration)
- Precision
- Ex) Measuring heights and taking off measurements of shoes

Front 26

Random Errors

Back 26

- Come from an unknown source or are unpredictable (stochastic)
- Accuracy
- Averages out to true value

Front 27

In terms of variance,
True Value = ______________ ± _____________ ± ________________

Back 27

Measurement ± Bias ± Random

Front 28

Types of Measures:

Back 28

- Self-Report (very biased)
- Environmental Observations
- Behavioral Observations
- Physiological Observations (to analyze peoples' minds, only way is to ask questions)
- Public Records

Front 29

Types of Sampling Methods:

Back 29

- Simple Random
- Systematic
- Stratified
- Cluster
- Convenience Sampling

Front 30

Type 1 Error:

Back 30

False Positive

Front 31

Type 2 Error:

Back 31

False Negative

Front 32

Types of parameters (quantitative descriptors of a system or group/numerical summary of a population) are:

Back 32

- Central tendency (mean, median, mode)
- Spread (range, quartile range, standard of deviation, standard error)
- Distribution shape (skewness, kurtosis)

Front 33

Common statistical tests for comparing two variables:

Back 33

- Chi-squared test
- Independent samples T-test
- One-way ANOVA
- Correlation
---> In each instance, the alternative hypothesis is that one variable that helps us predict the other
---> Test selected based on categorical vs. quantitative variables

Front 34

Statistical tests for association:
---> What are these usually a ratio of?

Back 34

- Correlation coefficients
- T-scores
- Chi-squared test statistics
- F-ratios
---> Usually a ratio of (difference from expectation)/(variability)

Front 35

Data Dimensions:

Back 35

- Subjects
- Variables
- Time

Front 36

Common Research Designs:

Back 36

- Case study
- Case-control study
- Cross-sectional study
- Cohort study
- Quasi-expermental study
- Experimental study

Front 37

Case Study

Back 37

- Most simple BBH study
- Write up story about person's health with observations

Front 38

Case-Control Study

Back 38

- Difference b/t two groups

Front 39

Cross-Sectional Study

Back 39

- Population sample at one point in time

Front 40

Cohort Study

Back 40

- Study over a period of time