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PSYC 210 > Module 1 > Flashcards

Module 1 Flashcards

1
Q

Scientific Method

A

consists of learning assumptions, goals and procedures for creating and answering questions.

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2
Q

What are the 4 goals of science?

A
  1. Description - what happened - describe a behaviour and the conditions under which it occurred.
  2. Explanation - why it happened - causes of behaviour.
  3. Prediction - what will happen next?
  4. Control - how to make it happen.
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3
Q

What are the 4 approaches to understanding?

A
  1. Authority approach
  2. Analogy approach
  3. Rule Approach
  4. Empirical approach
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4
Q

Authority approach

A

seeking out knowledge from sources that are believed to be reliable and valid.
However, you can’t follow it blindly

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5
Q

Analogy approach

A

analogy between some new events and a more familiar understandable event.
The problem with it is that it is open to a number of interpretations

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6
Q

Rule approach

A

try to establish laws or rules that cover a variety of different observations. It can save time and effort but if followed blindly it can threaten the advancement of understanding.

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7
Q

Empirical approach

A

testing ideas against actual events - observing behaviour and drawing conclusions.

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8
Q

How can we achieve Causation?

A

In an experiment where one factor directly affects another factor.
It must demonstrate that changing the first thing produces a change in the second and that there is no other possible cause for the change in the second thing

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9
Q

What are the components of an experiment?

A
  1. population sample
  2. dependent variable
  3. operational definition
  4. reliability and validity
  5. bias
  6. floor and ceiling effects.
  7. data types and scales of measurement
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10
Q

Population Sample

A

Population - members of a specific group and defined by the purposes of the experiment.
Sample - relatively small subset of a population that is selected to represent the population.
Representative Sample - characteristics and behaviour of the sample reflect those of a population and it ensures generalisability.

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11
Q

How do you achieve a representative sample?

A

through random sampling:

Random sampling - select members have an equal chance of selection in an unbiased manner.

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12
Q

Descriptive Statistics

A

summarises the data collected from samples

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13
Q

Inferential Statistics

A

generalises the sample to the population

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14
Q

Dependent Variable

A

It is the measurement taken.

It is what you record - depends on what the participant does.

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15
Q

Operational Definition

A

Where in many cases there is no direct way to measure what you want so you have to think about 2 things:

  1. property of interest (PI)- what you are trying to measure
  2. dependent variable - a measurable value the must indirectly reflect the PI
    so. ..the operational definition is the specification of how the property of interest will be measured.
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16
Q

Validity

A

Validity - a DV is valid if it measures what is is supposed too - threat to validity arises from any unintended component that is related in a score

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17
Q

Reliability

A

Reliability - a DV is reliable if, under the same conditions, it gives the same measures and contains a minimum of measurement error - unreliable data reflect error and provide a biased perspective.

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18
Q

Bias

A

a biased DV is consistently inaccurate in one direction - always high or always low

19
Q

Floor and Ceiling Effects

A

Ceiling Effect - when a task is so easy that all the scores are very high.
Floor Effect - when a task is so difficult that all the scores are very low.

20
Q

Data types and Scales of Measurement

A

the data type determines what sorts of analyses you can perform on your data and the conclusions you can draw.

either numerical (interval or ratio scales)
or categorical - which goes into ordered (ordinal scale) or unordered (nominal scale)
21
Q

Nominal Scale

A

categorises without ordering.
numbers that substitute for names.
e.g. 1 = female

22
Q

Ordinal Scale

A

categorises and orders the categories.
Bigger means more.
Distance between points on the scale is not considered equal.
e.g. rugby team standings

23
Q

Interval Scale

A

Categorises, orders and establishes and equal unit of measurement on the scale.
Knowing how much more.
Distance between points on scale is considered to be equal.
e.g. celcius temperature

24
Q

Ratio Scale

A

Categorises orders and establishes an equal unit on the scale.
True zero point.
Allows ratio statements

25
Q

Independent Variable

A

the experimental factor (s) that distinguishes your groups
Manipulated by the experimenter.
Has two or more levels.

26
Q

What happens if you have a Quasi experiement

A

if you have a quasi experiment you MAY NOT conclude that there is a casual relationship between the IV and the pattern of results

27
Q

Confounding Variable

A

If participants are not randomly assigned to condition, there may be differences between groups other than the DV.
this means you can draw conclusions that aren’t valid

28
Q

Important issues regarding IVs

A
  1. Control groups
  2. Placebo effects
  3. Choosing levels properly
  4. single and double-blind studies
  5. demand characteristics
29
Q

Control Group

A

Comparison group.

Differs from experimental groups by the absence of the experimental treatments

30
Q

Placebo Effects

A

A special control group that believes it is receiving treatment when its not.
The performance in this group gives an estimate of the size of the placebo effect.

31
Q

Single and Double-blind Studies

A 
Single-blind = when a control group is used to measure the placebo effect - essential that the participant doesn't know what group they are in.
Double-blind = neither experimenter or participants knows the condition that the participant is assigned to
32
Q

Demand Characteristics

A

cues in a new situation that people interpret as “demands” for a particular behaviour.
either from participants, experimenter or environment = biases

33
Q

Between Subject Design (IV)

A

Each participant is tested in only one level of the IV.
There may be confounds and to stop this use matching - which is a way to control extraneous subject variables - matched to treatment conditions.

34
Q

Within-subject Design (IV)

A

each subject is test in every level of the IV - each participants serves as their own control.
This design reduces error variance which makes it easier to detect small systematic differences between treatment conditions.

35
Q

What is the PROBLEM with within-subject design?

A
  1. Order Effects - order in which participants experience levels
36
Q

Order Effects

A

Order in which participants experience levels can be a problem - can be fixed with counter-balancing.
Counter-balancing = each treatment condition is equally exposed to the practice effects and demand characteristics.

37
Q

What is it important to control

A

subject variables, demand characteristics and experimental materials.

38
Q

Multiple Independent Variables

A

More than 1 IV.

Allows determination of the effect of each IV on the DV and also how they interact.

39
Q

Factorial Design

A

Where you have a number of IVs in your design and you collect data in all combinations of the levels of your IV your design is fully crossed.

40
Q

What is the Main Effect of Factorial Design

A

the effects of one IV on the DV, ignoring the other IVs in the study.
there is a main effect for each IV

41
Q

Interaction Effect

A

The effects of one IV on the DV taking into account the other IV(s) in the study.
In graphs: if the lines diverge or intersect then there is an interaction.
if the lines are parallel then there is no interaction

42
Q

How to calculate midpoint

A

lower exact limit + 1/2 class interval width

43
Q

Shapes of Frequency Distribution

A

uniform or rectangular distribution

PSYC 210 (1 decks)

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