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Evidence Based Approach > Slides Week 2 > Flashcards

Slides Week 2 Flashcards

1
Q

Research attempts to . .

A
  • Increase Understanding
  • How and Why do we behave the way we do.
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2
Q

How does research start

A
  • noting an interesting question
  • stating the question in a way that it can be answered Undergoing the scientific Method
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3
Q

There are different types of research design (2)

A
  • Non Experimental
    • True Experimental
    • Quasi Experimental
  • Experimental
    • Descriptive
    • Historical
    • Correlational
    • Qualitative
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4
Q

How are the research types different

A
  • nature of the question asked
  • method used to answer questions
  • degree of precision of the method
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5
Q

Non Experimental Research

A
  • describes relationships between variables
  • cannot test cause-and-effect relationships
    • descriptive
    • historical
    • correlational
    • Qualitative
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6
Q

Descriptive Research

A
  • describes characteristics of an existing phenomena
  • provides a broad picture
  • serves as basis for other types of research
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7
Q

Historical Research

A
  • Describes past events in the context of other past or current events
  • Primary and secondary sources of data
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8
Q

Correlational Research

A
  • Asks what several events have in common
  • Asks whether knowing one event can allow prediction of another event
  • Does not imply causation
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9
Q

Qualitative Research

A
  • Asks what several events have in common
  • Asks whether knowing one event can allow prediction of another event
  • Does not imply causation
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10
Q

Types of Research Design (Table)

A
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11
Q

What is the difference between a variable and a value?

A
  • A variable is a factor that can be measured
  • A value is a subset of a variable

Eg: height is a variable, 186cm is a value

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12
Q

Independent Variable

A
  • A group or condition in a study
  • Is what we are measuring
  • Divided into levels
  • Directly or indirectly manipulated by researcher
    • Direct Manipulation: drug treatment
    • Indirect Manipulation: school grades
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13
Q

What makes a good IV?

A
  • Not confounded
  • IV Levels do not vary systematically with other variables
  • DV is sensitive to changes in the IV
  • Called Dependant because its “Scores’ depend on experimenter manipulation
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14
Q

Dependant Variable

A
  • The thing being assessed or measured
  • Measures outcome or performance
  • Example: Amount of time looking at screens (IV); level of health and fitness (DV).
  • Needs to be operationalised
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15
Q

Operationalised

A
  • Clearly defined IV & DV
  • Specific description of how you will define and measure a variable
  • define as it is used in your study.
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16
Q

Control Variables

A
  • Variable whose influence you want to control

Ie: sex difference in thrill seeking behaviour you may control for income

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17
Q

Extraneous Variables

A
  • Confounding occurs when an extraneous variable either:
    • Varies systematically across levels of IV
    • Is correlated with the DV
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18
Q

Define True Experiment

A
  • Participants randomly assigned to groups
  • Treatment variable is controlled by researcher
  • control of potential causes of behaviour
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19
Q

Quasi-experiment

A
  • Participants are assigned to groups
  • useful when researcher cannot manipulate variables
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20
Q

What is a variable?

A
  • An entity that can be measured and can take on different measured values
    eg: height, weight, intelligence, hair colour, time, performance, income, level of depression
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21
Q

Moderator Variables

A
  • Also called mediator variables (although there is a difference between them)
22
Q

Mediator Variable

A
  • Is thought to describe the psychological process that occurs to create the relationship
23
Q

Moderator Variables

A
  • Change the strength of an effect or relationship between two variables
24
Q

Dependent Variable Definition and AKA

A

A variable that is measure to see whether the treatment or manipulation of the independent variable had an effect

AKA: Outcome variable, Results Variable, Criterion Variable

25
Q

Independent Variable Definition and AKA

A
  • A variable that is manipulated to examine its impact on a dependent variable

AKA: Treatment, Factor, Predictor Variable

26
Q

Control Variable Definition and AKA

A
  • A variable that is related tothe dependent variable, the influence of which needs to be removed

AKA: Restricting Variable

27
Q

Extraneous Variable Definition and AKA

A
  • A variable that is related to the DV or IV that is not part of the experiment

AKA: Threatening Variable

28
Q

Moderator Variable Definition and AKA

A
  • A variable that is related to the DV or IV and has an impact on the DV

AKA: Interacting Variable

29
Q

Between Subjects Design

A
  • Also known as an indepedent sample
  • each subject is exposed to one level of each IV
30
Q

Within Subjects design

A
  • Also known as a repeated measures design
  • Each subject is exposed to all levels of each independant variable
31
Q

Define Hypothesis

A
  • “if . . . then” statements
  • objective extension of the original question
  • in a testable form
  • hypotheses posit a relationship between different factors
  • data collected that will confirm or refute the hypothesis
  • hypotheses are testable not provable.
32
Q

Hypotheses are . . .

A
  • Brief, declarative statement
  • predict the outcome of a study
  • Posed as a priori
  • Are well educated guesses
33
Q

Define a Priori

A
34
Q

A complete well written Hypothesis should . . .

A
  • be stated in declarative form
  • posit a relationship between variables
  • reflect a theory or body of literature upon which:
    • they are based
  • be brief and to the point
  • be testable
35
Q

Why do we need hypotheses?

A
  • Karl Popper
  • Falsification
  • Hypotheses can be falsified/rejected
36
Q

Falsification

A

The process by which something can be demonstrated to be false

37
Q

Why is falsification important?

A
  • Important to Poppers philosophy and how scientific knowledge progresses
  • Hypotheses are refined and subsequent theories are developed
  • Hypothesise develop strength and credibility
    *
38
Q

The Null Hypothesis

A
  • A statement of no difference/relationship/effect
  • nothing is going on
  • the starting point for evaluating research
  • Evaluating research assumes null to be true
  • then attempts to collect evidence to knock that down.

H0: μ1 μ2

39
Q

The Alternative Hypothesis

A
  • A statement that something is going on
  • Demonstrates relationship, effect or difference
  • Can be non directional or directional
    • depends on level of confidence - but is very important
  • Non Directional: H1: μ1 ≠ μ2
  • Directional: H1: μ1 > μ2
40
Q

Populations

A
  • A collection of units (people, cats, plants)
  • What we wish to generalise in our research findings
  • Populations large or narrow
  • We aim to infer about general populations
41
Q

Samples

A
  • A smaller colleciton of observations from a population (people, cats, plants)
  • used to infer characteristics about the population
  • Bigger samples are more likely to be accurate
  • results may vary across samples
  • on average will be similar
42
Q

Testing the whole population

A
  • It is expensive and time consuming to test an entire population
  • Instead we use samples: Mini Populations
  • Randomly selected samples from a population can be said to reflect the entire population.
  • Based on samples, we can generalise to the population
43
Q

Field’s Model Analogy

A

The Real World

→

1) Good Fit
2) Moderate Fit
3) Poor Fit

44
Q

Descriptive Statistics

A

Aim to capture the essential features of the results in an easily comprehensible form.

  • Violent Video Group: Average aggressive acts = 9.49
  • Neutral Video Group: Average violent acts = 6.22

But: Surely when we run our experiment we need more than this to test our hypothesis?

45
Q

Statistical Significance

A
  • How do we know if the difference is large, small or a chance result?
  • there are Three Important questions
    • Is there a statistically significant difference between the two groups
    • what do these results tell us? Do they support, or fail to support hypothesis?
    • are these sample results an accurate reflection of the population
46
Q

Esitmation or Inference

A
  • When we test a hypothesis statistically we want to reach a conclusion.
  • What is going on in our population of interest
  • We use sample data to give us information to form a conclusion
47
Q

What is a p value . . . ?

A
  • Statistical significance testing allows us to test for differences between groups.
  • Also allows us to test relationships we observe
  • a process that tells us if we can reject the null hypothesis or if we can retain it.
  • Significance level = risk associated with not being 100% certain that null hypothesis is incorrect
  • Calculated as a p value
48
Q

P Value

A
  • A hypothesis test that is used to determine the significance of the results from a study.
  • It is the probability that the results from an experiment or study are due to chance and not the experimental conditions.
  • also known as calculated probability
  • P value only tells you about probability; not meaningfulness.
49
Q

Null Hypothesis Significance Testing

A
  • Most used method for testing research questions with statistics
  • Fisher and the Lady Tasting Tea
    • claims that we should calculate the probability of an event
    • then evaluate this within the research context
      *
50
Q

What is a population?

A
  • a collection of units we want to generalise in research findings
51
Q

Why do we use descriptive Statistics?

A

To capture the essential features of the results in an easy to comprehend form

52
Q

What is the most common method for testing research questions with statistics

A

Null-hypothesis significance testing.

Evidence Based Approach (15 decks)

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