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Week9-QuantitativeDataAnalysis.pdf
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Week 9:
Quantitative Data Analysis
Topic goals
To gain an understanding of Quantitative Analysis
To familiarize with the statistical tests for Quantitative
research.
To understand the stages involved in quantitative data
analysis
Task – Forum
Based on the given research problem, provide a research
question that can address two or more variables, using
quantitative terms, defining the variables you will use.
Discuss which statistical test you would use to answer
your research question and explain the rationale behind
your choice.
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QUANTITATIVE DATA ANALYSIS
1. Introduction
The main purpose to analyze data is to gain useful and valuable information. Data
analysis is useful to describe data, compare and find relationships or differences
between variables, etc. The researcher uses techniques to convert the data to
numerical forms.
1.1. Prepare your data
As a researcher you have to be sure that your data are correct e.g. respondents
answered all of the questions, check your transcriptions, etc. You have to identify
your missing data and then you have to convert them into a numerical form e.g.
red=1, yellow=2, green=3, etc.
1.2. Scales of measurements
Before analyzing quantitative data, researchers must identify the level of
measurement associated with the quantitative data. The type of data that you have
to use on a set of data depends on the scale of measurement of your data. The
scales of measurements are nominal, ordinal, interval and ratio.
Nominal data
Data has no logical order and can be classified into non-numerical or named
categories. It is basic classification data. The values we give are just to replace the
name and they cannot be order. Ex. Male, female, district A, district b
Example: Male or Female
There is no order associated with male or female
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Ordinal data
Data has a logical order, but the differences between values are not constant.
These data are usually used for questions that are referred to ratings of quality or
agreements like good, fair, bad, or strongly agree, agree, disagree, strongly
disagree.
Example: 1st , 2nd, 3rd
Example: T-shirt size (small, medium, large)
Interval data:
Data is continuous and has a logical order, data has standardized differences
between values, but no natural zero .
Example: Fahrenheit degrees
* Remember that ratios are meaningless for interval data. You cannot say, for
example, that one day is twice as hot as another day.
Ratio data
Data is continuous, ordered, has standardized differences between values, and a
natural zero
Example: height, weight, age, length
Having an absolute zero allows you to meaningful argue that one measure is twice
as long as another.
For example – 10 km is twice as long as 5 km
Remember that there are several ways of approaching a research question and how
the researcher puts together a research question will determine the type of
methodology, data collection method, statistics, analysis and presentation that will
be used to approach the research problem.
For each type of data you have to use different analysis techniques. When using a
quantitative methodology, you are normally testing a theory through the testing of
a hypothesis.
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1.3. Hypothesis/Null hypothesis:
A hypothesis is a logical assumption, a reasonable guess, or a suggested answer to
a research problem.
A null hypothesis states that minor differences between the variables can occur
because of chance errors, and are therefore not significant.
*Chance error is defined as the difference between the predicted value of a
variable (by the statistical model in question) and the actual value of the variable.
In statistical hypothesis testing, a type I error is the incorrect rejection of a true null
hypothesis (a "false positive"), while a type II error is incorrectly retaining a false
null hypothesis (a "false negative"). Simply, a type I error is detecting an effect (e.g.
a relationship between two variables) that is not present, while a type II error is
failing to detect an effect that is present.
1.4. Randomised, controlled and double-blind trial
Randomised - chosen by random.
Controlled - there is a control group as well as an experimental group.
Double-blind - neither the subjects nor the researchers know who is in which
group.
Variables:
An experiment has three characteristics:
1. A manipulated independent variable (often denoted by x, whose variation does
not depend on that of another).
2. Control of other variables i.e. dependent variables (a variable often denoted
by y, whose value depends on that of another.
3. The observed effect of the independent variable on the dependent variables.
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1.5. Validity, reliability and generalizability
Validity: refers to whether the researcher measures what he/she wants to
measure. The three types of validity are:
Content validity – refers to whether or not the content of the variables is right to
measure the concept.
Criterion validity – refers to the collection of information on these other measures
that can determine this.
Construct validity - refers to the design of your instrument so that it contains
several factors, rather than just one.
(Muijs, 2010)
Reliability: “refers to the extent to which test scores are free of measurement
error” (Muijs, 2010, pg.82). The two types of reliability are:
Repeated measures or test-retest reliability - refers to the instrument that you use
if it can be trusted to give similar result if used later on time with the same
respondents.
Internal consistency - refers to whether all the items are measuring the same
construct.
Generalizability: it is about the generalization of your findings from your sample to
the population.
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2. Descriptive statistics
Descriptive statistics are summarizing data. These are used to describe variables
and the basic features of the data that have been collected in a study. They provide
simple summaries about the sample and measures of central tendency (e.g. mean,
median, standard deviation etc.). Together with simple graphics analysis, they form
the basis of virtually every quantitative analysis of data.
It should be noted that with descriptive statistics no conclusions can be extended
beyond the immediate group from which the data was gathered.
Some popular summary statistics for interval variables
Mean: is the arithmetic average of the values, calculated by adding all the values
and divided by the total number of values.
Median: the data point that is in the middle of "low" and "high" values , after put in
numerical order
Mode: The most common occurring score in a data set
Range: It is the difference between the highest score and the lowest score.
Standard deviation: “The standard deviation exists for all interval variables. It is the
average distance of each value away from the sample mean. The larger the
standard deviation, the farther away the values are from the mean; the smaller the
standard deviation the closer, the values are to the mean” (Patel, 2009, pg.5).
Minimum and Maximum value: the smallest and largest score in data set
Frequency: The number of times a certain value appears
Quartiles: same thing as median for 1/4 intervals
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(Adapted from Patel, 2009, pg. 6)
3. Data distribution
Before beginning the statistical tests, it is necessary to check the distribution of
your data. The main types of distribution are normal and non-normal.
Example
Case no Grades
1 90 2 67 3 85 4 90 5 100 6 58 7 90
Total 490
Mean: 70
Median: 90
Mode: 90
Minimum value: 100
Maximum value: 58
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3.1. The Normal distribution
When the data tends to be around a central value with no bias left or right, it gets
close to a "Normal Distribution":
The graph of the normal distribution depends on two factors i.e. the mean (M) and
the standard deviation (SD). The basics characteristics of a normal curve are: a) a
bell shape curve, b) It is perfectly symmetrical, c) Mode, median, and mean lie in
the middle of the curve (50% of the values lie to the left of the mean, and 50% lie to
the right) d) Approximately 95% of the values are found two standard deviations
away from the mean (in both directions) (Patel, 2009). The location of the center of
the graph is determined by the mean of the distribution, and the height and width
of the graph is determined by the standard deviation. When the standard deviation
is large, the curve is short and wide; when the standard deviation is small, the curve
is tall and narrow. Normal distribution graphs look like a symmetric, bell-shaped
curve, as shown above. When measuring things like people's height, weight, salary,
opinions or votes, the graph of the results is very often a normal curve.(Langley
Perrie, 2014)
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3.2. Non-Normal Distributions:
There are several ways in which a distribution can be non-normal.
4. Statistical Analysis
Statistical tests are used to make inferences about data, and can tell us if our
observation is real. There is a wide range of statistical tests and the decision of
which of them you are going to test it depends on your research design. If your data
is normally distributed you have to choose a parametric test otherwise you have to
choose non-parametric tests.
4.1. Parametric and Nonparametric Tests
A parametric statistical test makes assumptions about the parameters (defining
properties) of the population distribution(s) from which one's data are drawn,
whereas a non-parametric test makes no such assumptions. Nonparametric tests
are also called distribution-free tests because they do not assume that your data
follow a specific distribution (Frost, 2015).
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Parametric tests (means) Nonparametric tests (medians)
1-sample t test 1-sample Sign, 1-sample Wilcoxon
2-sample t test Mann-Whitney test
One-Way ANOVA Kruskal-Wallis, Mood’s median test
Factorial DOE with one factor and one
blocking variable
Friedman test
It is argued that nonparametric tests should be used when the data do not meet
the assumptions of the parametric test, particularly the assumption about normally
distributed data. However, there are additional considerations when deciding
whether a parametric or nonparametric test should be used.
4.2. Reasons to Use Parametric Tests
Reason 1: Parametric tests can perform well with skewed and non-normal
distributions
Parametric tests can perform well with continuous data that are not normally
distributed if the sample size guidelines demonstrated in the table below are
satisfied.
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Parametric analyses Sample size guidelines for non-normal data
1-sample t test Greater than 20
2-sample t test Each group should be greater than 15
One-Way ANOVA If you have 2-9 groups, each group should be
greater than 15.
If you have 10-12 groups, each group should be
greater than 20.
Note: These guidelines are based on simulation studies conducted by statisticians at
Minitab.
Reason 2: Parametric tests can perform well when the spread of each group
is different
While nonparametric tests do not assume that your data are normally distributed,
they do have other assumptions that can be hard to satisfy. For example, when
using nonparametric tests that compare groups, a common assumption is that the
data for all groups have the same spread (dispersion). If the groups have a different
spread, then the results from nonparametric tests might be invalid.
Reason 3: Statistical power
Parametric tests usually have more statistical power compared to nonparametric
tests. Hence, they are more likely to detect a significant effect when one truly
exists.
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4.3. Reasons to Use Nonparametric Tests
Reason 1: Your area of study is better represented by the median
The fact that a parametric test can be performed with no normal data does not
imply that the mean is the best measure of the central tendency for your data. For
example, the center of a skewed distribution (e.g. income), can be better measured
by the median where 50% are above the median and 50% are below. However, if
you add a few billionaires to a sample, the mathematical mean increases greatly,
although the income for the typical person does not change.
When the distribution is skewed enough, the mean is strongly influenced by
changes far out in the distribution’s tail, whereas the median continues to more
closely represent the center of the distribution.
Reason 2: You have a very small sample size
If the data are not normally distributable and do not meet the sample size
guidelines for the parametric tests, then a nonparametric test should be used. In
addition, when you have a very small sample, it might be difficult to ascertain the
distribution of your data as the distribution tests will lack sufficient power to
provide meaningful results.
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Reason 3: You have ordinal data, ranked data, or outliers that you cannot
remove
Typical parametric tests can only assess continuous data and the results can be
seriously affected by outliers. Conversely, some nonparametric tests can handle
ordinal data, ranked data, without being significantly affected by outliers.
4.4. Statistical tests
One-tailed test: A test of a statistical hypothesis, where the region of rejection is on
only one side of the sampling distribution is called a one-tailed test. For example,
suppose the null hypothesis states that the mean is less than or equal to 10. The
alternative hypothesis would be that the mean is greater than 10.
Two-tailed test: When using a two-tailed test, regardless of the direction of the
relationship you hypothesize, you are testing for the possibility of the relationship
in both directions. For example, we may wish to compare the mean of a sample to a
given value x using a t-test. Our null hypothesis is that the mean is equal to x.
Alpha level (p value): In statistical analysis the researcher examines whether there
is any significance in the results. This is equal to the probability of obtaining the
observed difference, or one more extreme, if the null hypothesis is true.
The acceptance or rejection of a hypothesis is based upon a level of significance –
the alpha (a) level
This is typically set at the 5% (0.05) a level, followed in popularity by the 1% (0.01) a
level
These are usually designated as p, i.e. p =0.05 or p = 0.01
So, what do we mean by levels of significance that the 'p' value can give us?
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The p value is concerned with confidence levels. This states the threshold at which
you are prepared to accept the possibility of a Type I Error – otherwise known as a
false positive – rejecting a null hypothesis that is actually true.
The question that significance levels answer is 'How confident can the researcher
be that the results have not arisen by chance?'
Note: The confidence levels are expressed as a percentage.
So if we had a result of:
p =1.00, then there would be a 100% possibility that the results occurred by chance.
p = 0.50, then there would be a 50% possibility that the results occurred by chance.
p = 0.05, then we are 95% certain that the results did not arise by chance
p = 0.01, then we are 99% certain that the results did not arise by chance.
Clearly, we want our results to be as accurate as possible, so we set our significance
levels as low as possible - usually at 5% (p = 0.05), or better still, at 1% (p = 0.01)
Anything above these figures, are considered as not accurate enough. In other
words, the results are not significant.
Now, you may be thinking that if an effect could not have arisen by chance 90 times
out of 100 (p = 0.1), then that is pretty significant.
However, what we are determining with our levels of significance, is 'statistical
significance', hence we are much more strict with that, so we would usually not
accept values greater than p = 0.05.
So when looking at the statistics in a research paper, it is important to check the 'p'
values to find out whether the results are statistically significant or not.
(Burns & Grove, 2005)
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p-value Outcome of test Statement
greater than 0.05 Fail to reject H0 No evidence to reject H0
between 0.01 and 0.05 Reject H0 (Accept H1) Some evidence to reject H0 (therefore accept H1)
between 0.001 and 0.01 Reject H0 (Accept H1) Strong evidence to reject H0 (therefore accept H1)
less than 0.001 Reject H0 (Accept H1) Very strong evidence to reject H0 (therefore accept H1)
ANOVA (Analysis of Variance)
ANOVA is one of a number of tests (ANCOVA - analysis of covariance - and
MANOVA - multivariate analysis of variance) that are used to describe/compare the
association between a number of groups. ANOVA is used to determine whether the
difference in means (averages) for two groups is statistically significant.
T-test
The t-test is used to assess whether the means of two groups differ statistically
from each other.
Mann-Whitney U-test
The Mann-Whitney U-test test is used to test for differences between two
independent groups on a continuous measure, e.g. do males and females differ in
terms of their levels of anxiety.
This test requires two variables (e.g. male/female gender) and one continuous
variable (e.g. anxiety level). Basically, the Mann-Whitney U-test converts the scores
on the continuous variable to ranks, across the two groups and calculates and
compares the medians of the two groups. It then evaluates whether the medians
for the two groups differ significantly.
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Wilcoxon signed-rank test
The Wilcoxon signed-rank test (also known as Wilcoxon matched-pairs test) is the
most common nonparametric test for the two-sampled repeated measures design
of research study.
Kruskal-Wallis test
The Kruskal-Wallis test is used to compare the means amongst more than two
samples, when either the data are ordinal or the distribution is not normal. When
there are only two groups, then it is the equivalent of the Mann-Whitney U-test.
This test is typically used to determine the significance of difference among three or
more groups.
Correlations
These tests are used to justify the nature of the relationship between two
variables, and this relation statistically, is referred to as a linear trend. This
relationship between variables usually presented on scatter plots. A correlation
does not explain causation and it does not mean that one variable is the cause of
the other.
This and other possibilities are listed below:
Variable 1 Action Variable 2 Action Type of Correlation
Math Score ↑ Science Score ↑ Positive; as Math Score improves,
Science Score improves
Math Score ↓ Science Score ↓ Positive; as Math Score declines,
Science Score declines
Math Score ↑ Science Score ↓ Negative; as Math Score improves,
Science Score declines
Math Score ↓ Science Score ↑ Negative; as Math Score declines,
Science Score improves
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The following graphs show the same relationships:
Perfect Positive Correlation
Pearson's correlation
It is used to test the correlation between at least two continuous variables. The
value for Pearson's correlation lies between 0.00 (no correlation) and 1.00 (perfect
correlation).
Spearman rank correlation test
The Spearman rank correlation test is used to demonstrate the association
between two ranked variables (X and Y), which are not normally distributed. It is
frequently used to compare the scores of a group of subjects on two measures (i.e.
a coefficient correlation based on ranks).
Chi-square test
There are two different types of chi-square tests - but both involve categorical data.
One type of chi-square test compares the frequency count of what is expected in
theory against what is actually observed.
The second type of chi-square test is known as a chi-square test with two variables
or the chi-square test for independence.
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Regression
It is an extension of correlation and is used to define whether one variable is a
predictor of another variable. Regression is used to determine how strong the
relationship is between your intervention and your outcome variables
Table for common statistical tests
Type of test Use Parametric/ Non-parametric
Correlation These test justifies the nature of the relationship between two
variables
Pearson's correlation Tests for the strength of the association
between two continuous variables
Parametric
Spearman rank
correlation test
Tests for the strength of the association
between two ordinal, ranked variables (X
and Y).
Non-parametric
Chi-square test Tests for the strength of the association
between two categorical variables
Non-parametric
Comparison of
Means:
Look for the difference between the means of variables
Paired T-test Tests for difference between two related
variables
Parametric
Independent T-test Tests for difference between two
independent variables
Parametric
ANOVA Test if the difference in means (averages)
for two groups is statistically significant. It
is used to describe/compare the
association between a number of groups.
Parametric
Regression
Assess if change in one variable predicts change in another
variable
Simple regression Tests how change in the predictor variable Parametric
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predicts the level of change in the
outcome variable
Multiple regression Tests how change in the combination of
two or more predictor variables predict
the level of change in the outcome
variable
Parametric
Non-parametric Mann-Whitney U-test Test for differences between two
independent groups on a continuous
measure
Non-parametric
Wilcoxon rank-sum
test
Tests for difference between two
independent variables - takes into account
magnitude and direction of difference
Non-parametric
Wilcoxon signed-rank
test
tests for difference between two-sampled
repeated measures - takes into account
magnitude and direction of difference
Non-parametric
Kruskal-Wallis test Tests the means among more than two
samples,
if two related variables are different –
ignores magnitude of change, only takes
into account direction.
Non-parametric
5. Power of the study
There is increasing criticism about the lack of statistical power of published
research in sports and exercise science and psychology. Statistical power is defined
as the probability of rejecting the null hypothesis; that is, the probability that the
study will lead to significant results. If the null hypothesis is false but not rejected, a
type 2 error occurs. Cohen suggested that a power of 0.80 is satisfactory when an
alpha is set at 0.05—that is, the risk of type 1 error (i.e. rejection of the null
hypothesis when it is true) is 0.05. This means that the risk of a type 2 error is 0.20.
The magnitude of the relation or treatment effect (known as the effect size) is a
factor that must receive a lot of attention when considering the statistical power of
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a study. When calculated in advance, this can be used as an indicator of the degree
to which the researcher believes the null hypothesis to be false. Each statistical test
has an effect size index that ranges from zero upwards and is scale free. For
instance, the effect size index for a correlation test is r; where no conversion is
required. For assessing the difference between two sample means, Cohen's d ,
Hedges g, or Glass's Δ can be used. These divide the difference between two means
by a standard deviation. Formulae are available for converting other statistical test
results (e.g. t test, one way analysis of variance, and χ2 results—into effect size
indexes (see Rosenthal, 1991).
Effect sizes are typically described as small, medium, and large. Effect sizes of
correlations that equal to 0.1, 0.3, and 0.5 and effect sizes of Cohen's that equal
0.2, 0.5, and 0.8 equate to small, medium, and large effect sizes respectively. It is
important to note that the power of a study is linked to the sample size i.e. the
smaller the expected effect size, the larger the sample size required to have
sufficient power to detect that effect size.
For example, a study that assesses the effects of habitual physical activity on body
fat in children might have a medium effect size (e.g. see Rowlands et al., 1999). In
this study, there was a moderate correlation between habitual physical activity and
body fat, with a medium effect size. A large effect size may be anticipated in a study
that assesses the effects of a very low energy diet on body fat in overweight women
(e.g. see Eston et al, 1995). In Eston et al’s study, a significant reduction in total
body intake resulted in a substantial decrease in total body mass and the
percentage of body fat.
The effect size should be estimated during the design stage of a study, as this will
allow the researcher to determine the size required to give adequate power for a
given alpha (i.e. p value). Therefore, the study can be designed to ensure that there
is sufficient power to detect the effect of interest, that is minimising the possibility
of a type 2 error.
Table 3.
Small, medium and large effect sizes as defined by Cohen
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When empirical data are available, they can be used to assess the effect size for a
study. However, for some research questions it is difficult to find enough
information (e.g. there is limited empirical information on the topic or insufficient
detail provided in the results of the relevant studies) to estimate the expected
effect size. In order to compare effect sizes of studies that differ in sample size, it is
recommended that, in addition to reporting the test statistic and p value, the
appropriate effect size index is also reported.
6. Data presentation
A set of data on its own is very hard to interpret. There is a lot of information
contained in the data, but it is hard to see. Eye-balling your data using graphs and
exploratory data analysis is necessary for understanding important features of the
data, detecting outliers, and data which has been recorded incorrectly. Outliers are
extreme observations which are inconsistent with the rest of the data. The
presence of outliers can significantly distort some of the more formal statistical
techniques, and hence there is a high need for preliminary detection and correction
or accommodation of such observations, before further analysis takes place.
Usually, a straight line fits the data well. However, the outlier “pulls” the line in the
direction of the outlier, as demonstrated in the lower graph in Figure 2. When the
line is dragged towards the outlier, the rest of the points then fall farther from the
line that they would otherwise fall on or close to. In this case the “fit” is reduced;
thus, the correlation is weaker. Outliers typically occur from an error including a
mismarked answer paper, a mistake in entering a score in a database, a subject who
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misunderstood the directions etc. The researcher should always seek to understand
the cause of an outlying score. If the cause is not legitimate, the researcher should
eliminate the outlying score from the analysis to avoid distorts in the analysis.
Figure 1. A demonstration of how outliers can identified using graphs
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Figure 2. The two graphs above demonstrate Data where no outliers are observed
(top graph) and Data where an Outlier is observed (bottom graph).
6.1. Charts for quantitative data
There are different types of charts that can be used to present quantitative data.
Dot plots are one of the simplest ways of displaying all the data. Each dot
represents an individual and is plotted along a vertical axis. Data for several groups
can be plotted alongside each other for comparison (Freeman& Julious, 2005).
Scatter plots: it is a type of diagram that typically presents the values of tow
variables. The data are displayed as a collection of points. Each point position
depends of the horizontal and vertical axis.
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7. Quantitative Software for Data Analysis
Quantitative studies often result in large numerical data sets that would be difficult
to analyse without the help of computer software packages. Programs such as
EXCEL are available to most researchers and are relatively straight-forward. These
programs can be very useful for descriptive statistics and less complicated analyses.
However, sometimes the data require more sophisticated software. There are a
number of excellent statistical software packages including:
SPSS – The Statistical Package for Social Science (SPSS) is one of the most popular
software in social science research. SPSS is comprehensive and compatible with
almost any type of data and can be used to run both descriptive statistics and other
more complicated analyses, as well as to generate reports, graphs, plots and trend
lines based on data analyses.
STATA – This is an interactive program that can be used for both simple and
complex analyses. It can also generate charts, graphs and plots of data and results.
This program seems a bit more complicated than other programs as it uses four
different windows including the command window, the review window, the result
window and the variable window.
SAS – The Statistical Analysis System (SAS) is another very good statistical software
package that can be useful with very large data sets. It has additional capabilities
that make it very popular in the business world because it can address issues such
as business forecasting, quality improvement, planning, and so forth. However,
some knowledge of programming language is necessary to use the software,
making it a less appealing option for some researchers.
R programming – R is an open source programming language and software
environment for statistical computing and graphics that is supported by the R
Foundation for Statistical Computing. The R language is commonly used
among statisticians and data miners for developing statistical software and data
analysis.
(Blaikie, 2003)
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8. Statistical Symbols:
α: significance level (type I error).
b or b0: y intercept.
b1: slope of a line (used in regression).
β: probability of a Type II error.
1-β: statistical power.
BD or BPD: binomial distribution.
CI: confidence interval.
CLT: Central Limit Theorem.
d: difference between paired data.
df: degrees of freedom.
DPD: discrete probability distribution.
E = margin of error.
f = frequency (i.e. how often
something happens).
f/n = relative frequency.
HT = hypothesis test.
Ho = null hypothesis.
H1 or Ha: alternative hypothesis.
IQR = interquartile range.
m = slope of a line.
M: median.
n: sample size or number of trials in
a binomial experiment.
σ : standard error of the proportion.
p: p-value, or probability of success in
a binomial experiment, or population
proportion.
ρ: correlation coefficient for a
population.
: sample proportion.
P(A): probability of event A.
P(AC) or P(not A): the probability that A
doesn’t ha en.
P(B|A): the probability that event B
occurs, given that event A occurs.
Pk: kth percentile. For example, P90 =
90th percentile.q: probability of failure in
a binomial or geometric distribution.
Q1: first quartile.
Q3: third quartile.
r: correlation coefficient of a sample.
R²: coefficient of determination.
s: standard deviation of a sample.
s.d or SD: standard deviation.
SEM: standard error of the mean.
SEP: standard error of the proportion.
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N: population size.
ND: normal distribution.
σ: standard deviation.
σ : standard error of the mean.
t: t-score.
μ mean.
ν: degrees of freedom.
X: a variable.
χ 2 : chi-square.
x: one data value.
: mean of a sample.
z: z-score.
Accessed: http://www.statisticshowto.com/statistics-symbols/
9. Task – Forum
Read carefully the following research problem:
“Research studies suggest that teachers’ attitudes towards the inclusion
of students with disabilities are influenced by a number of interrelated
factors. For example, some earlier studies indicate that the nature of
disability and the associated educational problems presented influence
teachers’ attitudes. These are termed as ‘child-related’ variables. Other
studies suggest demographic and other personality factors which can be
classified as ‘teacher-related’ factors. Finally, the specific context is
found to be another influencing factor and can be termed as
‘educational environment-related’ (Avramidis & Norwich, 2002).
Based on this research problem, please provide a research question that
can address two or more variables. Bear in mind that the research
question needs to use quantitative terms, defining the variables you will
use.
Finally, discuss which statistical test you would use to answer your
research question and explain the rationale behind your choice.
EDU730: Research Practices and Methods
Page 27 EDU730: Research Practices and Methods
Further Reading and Study
Book
Muijs, D. (2010). Doing quantitative research in education with SPSS. Sage.
References:
Avramidis, E., & Norwich, B. (2002). Teachers' attitudes towards
integration/inclusion: a review of the literature. European Journal of Special
Needs Education, 17(2), 129-147.
Blaikie, N. (2003). Analyzing quantitative data: From description to
explanation. Sage.
Burns N, Grove SK (2005). The Practice of Nursing Research: Conduct, Critique,
and Utilization (5th Ed.). St. Louis, Elsevier Saunders
Eston, RG, Fu F. Fung L (1995). Validity of conventional anthropometric
techniques for estimating body composition in Chinese adults. Br J Sports Med,
29, 52–6.
Freeman, J. V., & Julious, S. A. (2005). The visual display of quantitative
information. Scope, 14(2), 11-15.
EDU730: Research Practices and Methods
Page 28 EDU730: Research Practices and Methods
Frost J. (2015). Choosing Between a Nonparametric Test and a Parametric Test.
Retrieved from http://blog.minitab.com/blog/adventures-in-statistics-
2/choosing-between-a-nonparametric-test-and-a-parametric-test
angley , Perrie Y (2014). Maths Skills for Pharmacy: Unlocking
Pharmaceutical Calculations. Oxford University Press.
Muijs, D. (2010). Doing quantitative research in education with SPSS. Sage.
Patel, P. (2009, October). Introduction to Quantitative Methods. In Empirical
Law Seminar.
Rosenthal R. (1991.). Meta-analytic procedures for social research (revised
edition). Newbury Park, CA: Sage,
Rowlands A.V, Eston R.G, Ingledew D.K. (1999). The relationship between
activity levels, body fat and aerobic fitness in 8–10 year old children. J Appl
Physiol, 86, 1428–35.
