Observational Methods Flashcards

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1
Q

Observational Methods

A

Useful in Psychology
Cannot determine the causal relationship between two factors.
Cannot know the direction of causality.

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2
Q

What are the different types of Observational Methods?

A

Case Studies
Naturalistic Observations
Correlational Studies

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3
Q

What is a Case Study?

A

Indepth study of one or a few individuals.

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4
Q

What are the Pros/Cons for Case Studies?

A

Pros:
Huge amount of information about the person(s)
Cons:
Genarilzability/External Validity

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5
Q

What are Naturalistic Observations?

A

Observing people in their common, everyday environment.

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6
Q

What are the Pros of Naturalistic Observations?

A

Pros:
Can be used with non verbal participants (infants)
Only method that looks at the “real world”

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7
Q

What are the Cons of Naturalistic Observation?

A

Cons:
Cannot determine cause
Infrequently occurring events
Reactance (Hawthorne) Effect

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8
Q

What is the Reactance (Hawthorne) Effect?

A

Biasing of the participants responses because they know they are being observed.
Ex: Yelling at your children

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9
Q

What is the variation of the Naturalistic Observations?

A

Participant Observation

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10
Q

What is Participant Observation?

A

Primarily observes participants but also interacts with people being studied.

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12
Q

What are the 2 variations of Participant Observation?

A

Observer as Participant

Participant as observer

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13
Q

What is Observer as Participant?

A

Researcher becomes part of the group being studied. Researcher is not really part of the group being studied.

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14
Q

What is Participant as Observer?

A

Living as part of the group. Emerged in the group being studied. Researcher becomes part of the culture by working and interacting with people being studied.

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15
Q

What are the Pros of Participant Observation?

A

Lots of info

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16
Q

What are the Cons of Participant Observation?

A

Very expensive
Very time consuming
Not necessarily accepted
Loss of objectivity (once you become part of a group you loose objectivity)

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17
Q

How are cultures like pyramids?

A

The top is fully accepted, they make the rules.
The middle is fully accepted, but they just follow the rules.
The bottom is hanging on, not fully accepted.

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18
Q

What is Time Sampling?

A

Observations of a behavior at different time periods.
Ex. how aggressive preschoolers are in AM and PM
Brothers, Lovers, Others game.

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19
Q

What is Situation Sampling?

A

Observations of the same type of behavior in different situations.

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20
Q

What are the two ways in which you might record behaviors you study?

A
  1. Qualitative

2. Quantitative

21
Q

What is qualitative?

A

Tell me a story of what happened.

Narrative description of what happens.

22
Q

What is qualitative?

A

Give me the counts, #s.

Ex: how many red lights were run.

23
Q

What is inter-observer reliability?

A

The extent to which observers agree.
Must be 85% agreeability between observers.
Must have at least two inter-observers/interrators, then calc how many times the 2 agree divided by how many times they could have agreed.

24
Q

What is a Correlational Study?

A

Investigates whether two or more variables of interest are meaningfully related.
Ex: # of drinks had and rated attractiveness of fellow bar goers.

25
Q

Scatter Plot

A

Dots are actual data points, mathematically fit on a line.

26
Q

Line of Prediction

A

The mathematically score we would have mathematically predicted for this individual, on the Scatter Plot.

27
Q

How do we interpret Correlational Data?

A

•Use the Correlational Coefficient (r)

28
Q

Correlational Coefficient (r):

A

A numerical estimate of the strength and direction of relationship.

29
Q
Correlational Coefficient (r):
Strength
A

Strength of the relationship indicated by the number (range 0 to 1).
•Weak Relationship = smaller #
(Dots are scattered more away from the line of prediction)
•Strong Relationship = larger #
(Cluster more together around line of prediction)

30
Q
Correlational Coefficient (r):
Direction:
A

Direction of the relationship indicated by the +/-.

31
Q
Correlational Coefficient (r):
The Perfect Relationship
A

[If r is 1.00 you will always be right but it never happens.]
r: tells us how good we would be at predicting an individual is standing on one variable by knowing how they are standing on the other variable.
Ex:
Var 1: Drinking & Accidents
r=.45 *Weaker correlation
Var 2: Phone Use & Accidents
r=.68 *Stronger correlation

32
Q

The Direction of the Relationship:

+ Positive + = r < 1

A

+ Positive + = r < 1
Variables go in the same direction:
Both Up or Both Down
Ex: Less drinks = Less accidents

33
Q

The Direction of Relationship:

- Negative - = r > 1

A
  • Negative - = r > 1
    Variables go in opposite directions:
    One goes Up, One goes Down
    Ex: More heat = Less starbucks purchases
34
Q

The Direction of the Relationship:

ø No Relationship ø

A

No pattern or cluster around line of prediction.

35
Q

The Direction of the Relationship: Curvilinear

A

As one Variable goes Up the other Variable goes Up & Down at the same time.
U or Horseshoe Shape Line of Prediction.
•r represents a linear relationship, dots make a line.
If you have a curvilinear line the r is going to underestimate the relationship between two variables and assumes normality.

36
Q

Drawbacks of Correlational Design

A
1. CANNOT DETERMINE CAUSALITY:
	•Third variable confound
	•Reciprocal causation
	•Temporal ordering
2. Statistical Issues:
	•Linearity of relationship
	•Restriction of range
	•Size of the sample
37
Q

CANNOT DETERMINE CAUSALITY:

Third Variable Confound

A

•Evaluating the strength by absolute
#. The +/- ONLY tell you what side.
•Reduces the size of r.

38
Q

CANNOT DETERMINE CAUSALITY:

Reciprocal Causation

A

•Both variables cause each other.
•Reduces the size of r
•If restrict the range of values for either or both variable r will inaccurately estimate the true relationship of two variables.
Ex: You are STRESSed so you DRINK more, DRINKing causes STRESS.

39
Q

CANNOT DETERMINE CAUSALITY:

Size of the Sample

A
  • Size of the sample won’t change r size.

* The smaller sample size the less likely we are to find r statistically significant.

40
Q

Critical Thinking

A

Ex: Ice-Cream sales are strongly correlated with crime rates.
Does ice-cream cause crime?
No. There must be a third variable.

41
Q

The Third Variable

A

Just because two things occur together doesn’t mean they are casually related.
Another variable could explain the relationship.

42
Q

How to Interpret a Correlational Design

A

Write the Hypothesis: either:
•Non-Directional (two-tailed) hypothesis:
•Directional (one-tailed) hypothesis

43
Q

How to Interpret a Correlational Design:

Non-Directional (two-tailed) Hypothesis

A

There IS a relationship between X and Y. (One effects the other)
•H0: r = 0 (Null)
•H1: r ≠ 0 (Alternative)

44
Q

How to Interpret a Correlational Design:
Directional (One-tailed) Hypothesis:
Positive Relationship

A
  • Positive Relationship: Higher levels of X are associated with higher levels of Y. One goes Up, One goes Down.
    • H0: r ≤ 0
    • H1: r > 0
45
Q

How to Interpret a Correlational Design:
Directional (One-tailed) Hypothesis:
Negative Relationship

A

Negative Relationship: Higher levels of X are associated with lower levels of Y. One goes Down, One goes Up
•H0: r ≥ 0
•H1: r < 0

46
Q

How to determine Statistical Significance of Correlational Studies

A
•DF: Degrees of Freedom: n -2:
(n = number of pairs), to find p value on SPSS or Table.
	•If p is < .05, significant than
Hypotheses:
	•One-tailed (directional)
	•Two-tailed (non-directional)
47
Q

If you have significance then compute

A

Effect size measures
•r2 = Square of r.obtained
•Coefficient of Determination:
What % of the variance in X is explained by Y: 100X… = %

48
Q

Special Considerations

A
•Pearson's product moment correlation:
*Only use for Interval or Ratio data
•Spearman's:
*Only use for Ordinal (rank) data
•Point Bi-Serial:
*Only use for One Dichotomous= Yes/No or On/Off
•The Phi-Coefficient:
*Only use if Both Dichotomous= 2 Options Only