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Final exam Flashcards

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1
Q

Difference between science and common sense

A

Science uses conceptual schemes and theoretical structures in order to empirically test questions about a topic interested to the researcher or population.
Common sense has no structure to it and it usually is subjective.

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2
Q

Science

A

Universal, empirical, replicable, cumulative, objective.

Science requires professionalism, ethics, needs to be rigorous and honest.

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3
Q

Common sense

A

The things we all believe to be true.
Can be right or wrong.
It is usually subjective and has no structure to it.

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4
Q

Why do we research?

A

Extend knowledge.

Explore, describe, explain, understand, support, disprove, modify, and refine topics of interest.

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5
Q

Ways of knowing (Philosophy of Science)

A 
Ontology
Epistemology
Axiology
Rhetoric
Methodology
Methods
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6
Q

Ontology

A

What is it that can be known.
Accepting there is either just one reality or many out there.
How you approach research.

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7
Q

Epistemology

A

What it means to know.

We have to be aware of the general knowledge in that particular field.

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8
Q

Axiology

A

What are the values that guide us.

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9
Q

Rhetoric

A

The language used.

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10
Q

Methodology

A

Research strategy / plan of action

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11
Q

Methods

A

Concrete techniques or procedures

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12
Q

Philosophy of Science deals with…

A

the underlying ideas of how is the best way to making sense of things.

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13
Q

What kind of research is more valuable?

A

New research

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14
Q

What is a Variable?

A

A characteristic or phenomenon that can change or can be changed. It can take on more than one value.

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15
Q

Types of variables

A 
Discrete
Continuous
Independent
Dependent
Control
Confounding
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16
Q

Discrete variable

A

A quantitative value which is obtained by counting, limited in the number of values they can take on. Groups.
e.g., male/female, married/unmarried

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17
Q

Continuous variable

A

A quantitative value within a certain group, the values they can take on are infinite.
e.g., heart rate, age, numbers

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18
Q

Independent variable

A

What you are manipulating

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19
Q

Dependent variable

A

What you are measuring

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20
Q

Control variable

A

What you control

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21
Q

Confounding variable

A

An uncontrolled third variable that can influence/confuse what you are measuring

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22
Q

Nominal Scale

A

Data you can count (e.g., frequency)

Use to name a category. (e.g., colour, gender)

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23
Q

Nominal Scale

A

Data you can count (e.g., frequency)

Use to name a category.

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24
Q

Ordinal scale

A

Quantitative data that you can order or rank.

e.g., 1st, 2nd, or 3rd.

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25
Q

Interval scale

A

Quantitative data where interval levels are meaningful. Distance between neighbour numbers are equal size.
No absolute zero.

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26
Q

Ratio scale

A

Quantitative data where distance between neighbour numbers are equal size, and you can determine ratios.
There is an absolute zero.

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27
Q

What are Ethics?

A

Moral principles that guide us.

28
Q

Example of issues in research’s ethics

A

Power can be asymmetrical (e.g., a policeman forced criminal to participate).
Harmful exploitation of participants.
No informed consent by the participants.

29
Q

Researchers got to…

A

Researchers got to follow ethical codes and legislation.
Meet deadlines and completion.
Be competent on the research rubric they are studying.
Reflex critically.

30
Q

Disciplines codes of ethics are set by:

A

APA - American Psychological Association.

APS (PENS) - Australian Psychological Society.

31
Q

What is a Research Design

A

Planning process that develops aspects of the research project and how they will fit together.
The Research Design is affected by the Research Question.

32
Q

Types of research

A

Descriptive and Exploratory.

Explanatory and Confirmatory.

33
Q

Descriptive and Exploratory research

A

Explores an idea or area.

Seeks to provide an accurate description of what is going on, what exists or happens.

34
Q

Explanatory and Confirmatory research

A

Why is the phenomenom happening or why does it exist?

Looking for explanations in order to test the researcher’s predictions or hypotheses.

35
Q

Descriptive Statistics

A

Statistic test that we do to describe data.

e.g., measures of central tendency, and measures of dispersion.

36
Q

Inferential Statistics

A

Making inferences/assumptions about populations from information available in samples.
Understanding of the Normal Curve underpins Inferential Statistics.
You need Inferential Statistics in order to show your results and conclusions. Is needed to find out if you should reject or accept the hypothesis.

37
Q

Skewness

A

Measure of the symmetry of the distribution.
Only used with interval and ratio data.
For normally distributed data, both skewness and kurtosis will be 0.

38
Q

Skewed negatively

A

Tail to the left.

Mean -> Median -> Mode

39
Q

Skewed positively

A

Tail to the right.

Mode -> Median -> Mean

40
Q

Normal Distribution

A

All measures of central tendency at the same spot.
We can use Normality tests to test normality: Shapiro-Wilks Statistics (for less than 100 cases) and Kolmogorov-Smirnov (Vodka test, for bigger samples).
Significance level is greater than your alpha (.05).

Bell shaped curve.
Symmetrical - left half is mirror image of right half.
Unimodal.
Asymptotic - descent rapidly but never actually touch the horizontal axis.
Same proportion of scores under specific locations.

41
Q

Kurtosis

A

Refers to the shape of the distribution, specifically the top of the curve.

42
Q

Values for Kurtosis.

A

Leptokurtic -> positive kurtosis, peaked distribution.
Mesokurtic -> normal
Platykurtic -> negative kurtosis, flat distribution.

43
Q

Between Subjects Design

A

Independent measures.
Participants can only participate in one group, either Control or Experimental group.
There is no contamination, and we can collect more data.
More participants and time (which can be affected by mortality).

44
Q

Within Subjects Design.

A

Repeated Measures.
One sample of participants do both independent variables at different times.
Less participants, cheaper.
There could be fatigue.

45
Q

Parametric test

A

A test of statistical inference in which assumptions are made about the underlying population distributions.
Has assumptions about normality, sampling, and levels of measurement.
More powerful than non-parametric tests.

46
Q

Non-parametric test

A

Tests that do not need assumptions about the population (normality) distribution. They are not as powerful as Parametric tests. The level of measurement used on this tests are Nominal/categorical.

47
Q

Non parametric test examples

A

Chi square test for goodness of fit.

Chi square test of Contingencies

48
Q

Validity

A

The degree to which something corresponds to a standard.

Face, construct, content, internal, external.

49
Q

Validity

A

The degree to which something corresponds to a standard.

Face, construct, content, internal, external.

50
Q

Threats to Internal Validity

A

History, maturation, testing, instrumentation, selection bias, mortality.

51
Q

Covariation

A

Two variables that vary together, so as one changes so does the other one.

52
Q

Mysterious relationships

A

confounding variables can get involved.

53
Q

Determining behavioural causal inferences

A

We need to find if covariation exists.
We need to be sure the time ordering, which is the variable that affect the other one.
Rule out alternative explanations as for why this happens.

54
Q

Basic Survey Designs

A

Cross sectional design
Time series surveys design
Longitudinal design

55
Q

Cross sectional design

A

Most common. Get data from one group once, then get data from other group around the same time.

56
Q

Longitudinal design

A

Study the same phenomena over time. More expensive.

It can be hard due to mortality.

57
Q

Longitudinal design

A

Study the same phenomena over time. More expensive.

58
Q

Qualitative research

A

Research concerned with meaning. We are interested in feelings, meanings, the experience of the participants.
Subjective ontology.

59
Q

Big Q

A

Open ended, inductive methodologies.
How questions, meaning of things.
Exploratory.
Bottom Up approach.

60
Q

Small q

A

Non-numerical data collection techniques.
Used in hypothetic-deductive research designs.
Top Down approach.

61
Q

Face Validity

A

The evidence for validity is that the measure appears “on the face of it” to measure what it is supposed to be measure.
Not sophisticated, because it involves only a judgement of whether, given the theoretical definition of the variable, the content of the measure appears to actually measure the variable.

62
Q

Construct Validity

A

The extent to which the measurement or manipulation of a variable accurately represents the theoretical variable being studied.
Adequacy of the operational definition of variables.

63
Q

Content Validity

A

Based on comparing the content of the measure with the universe of content that defines the construct.

64
Q

Internal Validity

A

The accuracy of conclusions drawn about cause and effect.

65
Q

External Validity

A

The extent to which a study’s findings can accurately be generalised to other populations and settings.

Social Science Research (1 decks)

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