Transcript Dissertation preparation 4

Analysing your evidence
What sort of evidence/data
will you have?
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You need to plan how you will analyse the
data before you collect it
If not you may
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Produce data that you cannot easily analyse
Not make the most of the evidence you have
at your disposal
If you are going to use statistical
methods, do it properly
Statistical methods
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Are used to
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Describe a set of data in an efficient and
meaningful manner
Make decisions about a larger population
of potential observations of which the data
are a sample
Test hypotheses
Descriptive statistics
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Describe data and events refer to:
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Frequency distributions
Central tendencies or averages
Variability of the data or dispersion by examining
the range or standard deviation of scores
Graphical representations
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Useful to convey information. It is often good to look at
graphic representation prior to further analysis so you
can see patterns of data.
Bar charts, Histograms, Pie charts etc.
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Different formats can make the data look more or less
significant
Can help you to tell the story
Inferential statistics
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Inferential statistics are concerned with making
inferences about populations and hypotheses
Inferential statistics are values which are
calculated from a sample, and used to estimate
the same values for a population
Types
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Mean and Standard Deviation
Chi-Square
Correlation
T-Tests
Analysis of Variance (ANOVA)
Variables
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Any property that may vary, i.e. that may
take different values
Qualitative variables - variables which differ
only in kind
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Gender (male, female)
Nationality (English, French)
Occupation (Nurse, teacher) etc.
Variables
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Quantitative variables - variables which
differ only in amount
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Height (1.62 metres, 3 inches)
Time (2.58 seconds, 5 hours)
IQ (98,124)
Continuity versus discreteness
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Continuous scale e.g. length
Only a finite number of values (discrete) (e.g.
dress sizes, test scores, degree classifications)
Types of numerical data
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Two main kinds
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Frequencies
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Count the number of events occurring in particular
categories e.g. 12 right handed people in the room
category = right handed people in the room frequency =
12
Measurements (metric data)
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Results of giving scores to individual people, objects or
events on the basis on an underlying scale of
measurement
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Scale of measurement that already exists or one that you
design/apply
Levels of measurement
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Nominal level
Ordinal level
Interval level
Ratio level
Levels of measurement
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Nominal level
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Use of numbers or letters to classify events
differing only in kind
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BBC radio stations
Ordinal level
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Use of numbers or letters to indicate an
ordered relationship between events
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Finishing positions in a race
Grades awarded to essays, degree classifications
Levels of measurement
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Interval level
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Indicates not only the relative position of
events but also the size of the differences
between events
There is a constant unit of measurement
which means that the arithmetic difference
between 2 scores accurately represents the
size of the actual difference measured
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E.g. temperature
Levels of measurement
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Ratio level
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Is simply interval measurement with an
absolute zero (i.e. a score of 0 really
indicates the total absence of the property
being measured)
A score of 60 represents twice as much of
a property as does a score of 30
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E.g. length, mass, time and volume
Measurement data:
examining relationships
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When observing continuous variables
e.g. age or tenure, a Correlation can be
used to make inferences about
relationship between the variables
Correlations estimate the extent to
which changes in one variable are
related to or associated with changes in
another variable.
Measurement data:
examining relationships
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A correlation will examine the degree to
which two or more variables are related. A
correlation co-efficient will be calculated –
ranging from
+1.00 indicates a positive relationship
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To
-1.00 indicating a negative relationship
Scattergrams or plots are used to pictorially
identify whether there is likely to be any form
of relationship, prior to statistical testing
Examining group differences
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Descriptive or explanatory research may involve
trying to determine whether two groups differ
according to a specific quality. This may involve
examining central tendency of results or scores on
one group, and how this compares to another
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T-Test :used to examine the values/scores of two groups
ANOVA :used to examine the values/scores of more than
two groups
These tests are used to determine whether groups have
different mean values or scores
These tests carry presumptions about the type of data
e.g. based on normal distribution and equal variance in
scores between the groups
Symmetry
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Frequency distributions are not always
symmetrical about the middle of the
distribution
Many of the group difference tests rely
on data having a normal distribution
Skew – when you get bunching of
scores at one end of the distribution
Averages: Mode
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Mode
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Most frequent value when data is grouped into class
intervals
Estimated by taking the midpoint of the interval that has
the greatest frequency
Easy to calculate
It may be used for data at any level of measurement
It is the only average that can be reported when data
consists of frequencies in categories
In such cases the mode is the category having the
highest frequency
Averages: Median
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Median
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Midway point in a series of scores (i.e. 50th
percentile point)
To calculate
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Sort the scores in order of increasing value
If there is an odd number of scores
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Median = middle point
If there is an even number of scores
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Median = halfway point between the two middle values
Averages: Median
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Advantages of the Median
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It is the most appropriate average when data
is measured at the ordinal level (because the
median is based on rank order position)
It is unaffected by extreme values, therefore
With skewed distributions, the median usually
describes the most “typical” value much
better than does the mean (which is greatly
affected by extreme scores)
Averages: Mean
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Ordinary average that most people use
Calculate by adding up all the scores and dividing
by total number of scores
Advantages
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More stable from sample to sample
Uses more information than median or mode
Disadvantages
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Affected by extreme scores and not the best average to
report when the data is very skewed or truncated.
Strictly speaking it requires data measured at the
interval or ratio level
Averages: summary of
differences
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With a symmetrical, normal distribution, the
mode, median and mean all coincide exactly
With skewed distributions the mean is pulled
towards the pointed end with respect to the mode
and median
In such cases, the different averages can give
very different impressions of the data. The mean,
in particular, can be very misleading if it is
reported as reflecting a “typical” score.
It is often informative to report more than one
average
Averages: summary of
differences
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The mode indicates the most common score
The median indicates the score that is exactly
in the middle of the distribution
The mean indicates the “centre of gravity” of
the distribution
If in a particular set of date the median is
very different from the mean, this will
generally indicate that the distribution is
skewed or truncated.
Validity
“The extent to which a test, questionnaire or
other method or operation is really measuring
what the researcher intends to measure”.
Internal validity
whether procedures are standardised or
controlled
External validity
generalisability, whether the findings can be
applied to the wider population
Triangulation
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Helps with validity because
Findings are judged valid when different
and contrasting methods of data
collection yield identical findings on the
same participants and setting
Reliability
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Refers to the consistency of the findings
Concerned with whether the results can be
replicated.
In research we need to examine
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consistency over time – involves administering a
measure more than once
Internal consistency is usually concerned with the
internal coherence of a scale or measure i.e.
whether different components link together
perhaps to produce an overall score.
Qualitative data
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How are we going to analyse more
qualitative, often “free text” information
This type of information is often rich,
adding important social information
Need to plan to identify and draw out
themes, strands
Which parts of the analysis goes
in the dissertation, where?
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The appendix should be used for:
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Examples of questionnaires (blank)
Examples of completed questionnaires that
illustrate particular themes/strands (not all)
The body of the dissertation should have in it
the stages and conclusions of the analysis
e.g. scattergrams which first identified trends,
followed by the graphic representations of the
results
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Any questions?
Preparation for presentation session