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1
Q

The empirical method

A

Empirical = through experience
Stages:
- Gathering data directly through out external senses
- Patterns and relationships within the data
- Problems:
o Without background theory is difficult
o Assumptions may influence -> not neutral

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2
Q

Planning research

A
  • Research question
  • Variables
  • Samples
  • Design
  • Analysis
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3
Q

Quantitative research

A

numerical basis (counting, categories, measuring)

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4
Q

Qualitative research

A

meaning, experiences, descriptions, verbal reports

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5
Q

Observation

A
  • Both for qualitative and quantitative research
  • Technique within traditional research design or its own research
  • Advantages:
    o Immediate data on real behaviour rather than possibly distorted self-reports
    o Gather data on behaviour
    o In field settings data is unconstrained
  • Disadvantages:
    o People’s behaviour affected by awareness of being observed
    o Time consuming
    o Identify cause and effect is difficult
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6
Q

Variables

A
  • Independent variable (IV): Variable which the experimenter manipulates in an experiment -> direct effect on dependent variable
  • Dependent variable (DV): Variable that is assumed to be directly affected by changes in independent variable
  • Confounding variable: Variable that is uncontrolled
  • Extraneous variable: any variable that could affect the results
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7
Q

True experiment:

A
  • Manipulates the IV
  • Holds all other variables constant (Inc. random allocation of conditions/ levels of the IV)
  • Measures any changes in the DV
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8
Q

Levels and Factors

A
  • IVs are terms of levels – different conditions are different levels
  • Title of the IV covers the dimension (e.g. temperature levels: hot/cold)
  • Multiple IVs are called factors – experiment have a factorial design (e.g. temperature and noise, both with at least two levels)
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9
Q

Control groups

A
  • Baseline condition in the IV can be achieved by using control groups/ placebo group
  • Placebo group might be using double-blind design
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10
Q

Strengths of experiments

A
  • Can isolate cause and effect because IV and extraneous variables are controlled
  • Alternative explanations and effects can be investigated (reproduction of the experiment)
  • Can control many extraneous influences
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11
Q

Critique of experiments

A
  • No unique view from participants unless interview after the experiment
  • Reactive effects occur because participants know they are in an experiment
  • Limited cause variables must be tight
  • Results with false credibility
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12
Q

Experimental designs

A
  • Independent sample design
    o One group of participants in experimental condition
    o Different group of participants in controlled condition
    o Participant variables can confound the experimental results
  • Repeated measures design
    o Same group of people is tested on different experimental conditions (i.e. levels of the IV)
    o Repeated on each participant under the different conditions of the IV
    o Multiple testing is not repeated measures
    o Order effects
     Effects from the order people participate
     Counterbalancing in order, conditions, stimulus
  • Matched-pairs design
    o All participants on come measure
     Highest two scores in A and B
     Second two highest score in A and B…
  • Single participant and small n design
    o Useful investigation of cognitive deficits associated with specific medical conditions
    o Useful when very long-terms training is required
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13
Q

Flied and laboratory

A
  • Field studies are in natural environment -> capture natural behaviour
  • Difference between field and lab is sometimes difficult to determine
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14
Q

Sampling for quantitative research

Sampling for research

A
  • Population sample
  • Rational
  • Target population or sampling frame – specific description of the population of interest
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15
Q

Probability based sampling methods

A
  • Equal probability selection method: producing sample; equal probability of being selected
  • Needs random selection
  • Use rng generators
    o Simple random sample
     Equal chance of selection
    o Systematic random sample
     Select every n-th case starting number randomly
    o Stratified sampling
     Large population with many subgroups
     Identify relevant subgroups
     Sample randomly
     Related method cluster sampling (choosing an entire cluster frome each strata, e.g. an entire class from each faculty)
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16
Q

Non-probability-based sampling methods

A
  • Quota sampling – define subgroups and collect data until the quota is fulfilled
  • Self-selecting sample – often used in observations, field studies and when participants are volunteers
  • Opportunity or convenience sample – test whoever is there
17
Q

Sample size

A
  • Small sample is more likely to be biased
  • Sample size relates to the statistics for the analysis
  • A larger sample size increases the power of a statistical test – more likely to detect effects
18
Q

Quantitative data analysis

Need of statistics in research

A
  • Statistics are a means to obtaining a clear, objective and fair conclusion
  • Research is usually conducted on small samples
    o Statistics relflect information about entire population
  • Helps to understand research results
  • Summarize and describe our findings in an economical way (descriptive statistics)
  • Make decision whether our results support our claims/ hypotheses (inferential statistics)
19
Q

Levels of measurement

A
  • Categorical and measured data
  • Mostly, IV is categorical and DV is measured
  • Data measured on:
    o Nominal scale
     E.g. two groups/ categories
     Distinct categories
    o Ordinal scale
     Ordinal numbers represent the rank position in a group
     Position but not distance between elemts
    o Interval scale
     Equal units
    o Ratio scale
     Intercal scale plus a absolute zero point
  • Higher level -> more information
  • Level of measurement is determined by the type of measurement
  • Level of measurement determines what kind of statistical test you can conduct
  • Change is possible but only downwards
    o Interval >ordinal > nominal
20
Q

Descriptive Statistics

A
  • Data can be described in terms of central tendency and dispersion
21
Q
  • Mean
A

o Arithmetic mean is used for interval/ratio data
o Sum of x / n
o Advantages:
 Powerful statistics to estimate the population parameter from our sample, sensitive and accurate
o Disadvantages:
 Easily distorted by outliers
 Discrete values are difficult to interpret

22
Q
  • Median
A 
o	Used for data on ordinal level
o	Central value of a set of data 
o	Not disturbed by extreme values
o	Does not account for exact distances
o	Median of 2,3,5,98,112 is 5
o	Odd number median is (N+1)/2
o	Even number take sum two central numbers / 2
23
Q
  • Mode
A

o Value that appeared most often
o Used for nominal scale value, mostly for discrete measurement scales
o Multiple mode possible

24
Q

Measures of dispersion

A
  • Describe the spread or distribution of the data around a central value
25
Q
  • The range
A

o Distance between top and bottom values
o Distorted by extreme values and is urepresentative of the distribution of values within the range
o Highest – lowest value

26
Q
  • Interquartile range
A

o Central grouping of values in a set
o Used for ordinal data
o Represents the calues that cut off the bottome and top 25% of values
o Not sensitive to outliers, but inaccurate when there are large class intervals

27
Q
  • Mean deviation
A

o Deviation value is the amount by which a particular value deviates from the mean

28
Q
  • Standard deviation and variance
A

o Most important measure of dispersion

Data Analysis (1 decks)

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