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Quantitative Data Analysis
(Using SPSS)
Albard Khan, M.Ed
Saturday, 7 November 2015
Quantitative versus qualitative research
1. Quantitative research is “explaining phenomena
by collecting numerical data that are analysed
using mathematically-based methods (in
particular: statistics)” (Allaga & Gunderson, 2002).
2. Qualitative research relies heavily on “the views of
participants…collects data consisting largely of
words (or text) from participants, and analyse
these words for themes” (Creswell, 2008, p. 46).
Quantitative versus qualitative(cont.)
1. Quantitative research is concerned with numerical
data, or numbers.
2. Qualitative research is interested with words or texts.
3. SPSS is exclusively a quantitative data analysis tool.
4. What about qualitative research such as interviews,
case studies, ethnographic research, and discourse
analysis; what software is to be used?
5. For qualitative research, use other softwares such as
nVivo.
Pair Discussion
With the person next to you, discuss if the
following research is quantitative, qualitative, or
mixed?
The following excerpts were taken from
educational research reports. They are either
taken from the abstract or the full report of the
research.
1
“A set of policy documents was collected and
analysed against literature on international
schools and education decentralization.”
Kustulasari, 2009, The international school standard project in
Indonesia: A document analysis
2
“In this paper, we use data from Indonesia to
examine the effectiveness of public versus
private schools. We use labour market earnings
as our measure of effectiveness.”
Beare, 2000, The effectiveness of private versus public schools: The
case of Indonesia
3
“A survey questionnaire was designed to elicit
perceptions about the impact of prior learning,
competence in language and communication,
quality of student-staff relations and cultural
interactions on student learning.”
Ramburuth & Tani (2009), The impact of culture on learning: exploring student
perceptions. Multicultural Education & Technology Journal, Vol. 3 Iss: 3, pp.182 - 195
4
“Data were collected during one academic
semester through in-depth interviews, a focus
group interview, classroom observations, and
collection of relevant documents.”
Tatar, 2005, Classroom participation by international students
5
“In stage 1, 1209 questionnaire responses were
received from Y1 and Y2 students across these
institutions in the identified subjects. In stage 2
we interviewed the students at key decision
making moments.”
Reay, 2008, The socio-cultural and learning experiences of working
class students in higher education
6
“The project interviewed 162 first-year students
at the University of the Arts, London, a university
with a high proportion of international students. ”
Sovic, 2009, Hi-bye friends and the herd instinct: international and
home students
7
“A representative sample of undergraduate and
postgraduate international students at a large
Australian university (n = 979, 64% females)
completed a mail-back survey examining their
perceptions of social connectedness.”
Rosenthal, 2009, Social connectedness among international students
What QUANT research can answer:
1. When we want quantitative answer, e.g. how many
Tarbiyah students came from SMA? How many from
MA?
2. When we want to study numerical change, e.g. is
academic achievement going up or down this year?
3. When we want to explain phenomena with many
factors, e.g. what factors predict students’ English
performance?
4. When we want to test hypotheses, e.g. is that true that
a romantic person tends to have higher GPA?
What QUANT research CANNOT answer:
1. When we want to explore a problem in-depth.
2. When we want to develop theories and hypotheses.
We have to study the literature or conduct qualitative
study.
3. If the issues are particularly complex.
4. When we want to reveal the meaning of particular
events and circumstances.
Do you agree or do you not agree?
“What you cannot measure doesn’t exist.”
(Anonymous)
Research Design: Experimental,
Quasi-experimental and Non-
experimental
Experimental Design
Definition
“A test under controlled conditions that is made to demonstrate a known truth or
examine the validity of a hypothesis.” (Muijs, 2004, p. 13).
Experiment group vs control group
As in natural science experiments, control groups receive no treatment while
experiment groups do.
Purpose
To establish causality between variables.
Usually involves pre-tests – experiment – post-tests.
Quasi-experimental Design
Definition
This is almost the same as the experimental design in that extraneous factors are
controlled, but it differs from the experimental design as allocation of group is not
randomized.
Experiment group vs comparison group
As in natural science experiments, comparison groups receive no treatment while
experiment groups do.
Purpose
To establish causality between variables.
Usually involves pre-tests – experiment – post-tests.
Non-experimental Design
1. Survey research
2. Observational research
3. Dataset analysis
Today, our focus is on survey research, as it is the most popular and
easiest method to conduct compared to experimental and quasi-
experimental design.
In non-experimental design, we’re interested in relationship or
correlations among variables, not causality.
The online questionnaire
Let’s open the questionnaire
1. How many sections are there?
Three sections:
Demographic information (Name, GPA, Gender)
Romantic Beliefs Scale
Theories of Intelligence Scale
2. Romantic Beliefs Scale is unidimensional (measuring only one
domain/construct: romanticism).
3. Theories of Intelligence Scale is also unidimensional, measuring
whether someone has incremental or entity theory of intelligence.
Research Questions
Discuss in small groups
By looking at the questionnaire, figure out what questions
can be answered using the questionnaire/instrument!
Descriptive and Inferential Statistics
A. DESCRIPTIVE STATISTICS
1. How many respondents are male and female?
2. What is the mean of participants’ GPAs?
3. Is there a difference between male and female GPAs?
4. In general, how romantic are the respondents?
5. In particular, is there a difference between male and female
romantic belief levels?
6. In general, what is the type of respondents’ theories of
intelligence?
7. In particular, is there a difference between male and female
theories of intelligence?
Descriptive and Inferential Statistics (cont.)
B. INFERRENTIAL STATISTICS
1. Is the difference between male and female GPAs significant?
2. Is the difference between male and female romantic belief levels
significant?
3. Is there a correlation between romantic beliefs and GPA? If there
is, how strong, in what direction and how significant?
4. Is there correlation between romantic beliefs and intelligence
beliefs? If there is, how strong, in what direction and how
significant?
5. Is there a correlation between intelligence beliefs and GPAs? If
there is, how strong, in what direction and how significant?
Coding and Recoding
1. Only numbers can be analysed. You have to assign numbersto the data.
2. That’s why, we need to create a codebook.
3. MS Excel is the first place to go before SPSS.
4. In Theories of Intelligence Beliefs Scale, one half are quitethe opposite the other half. (We’ll do recoding on SPSS).
5. Types of Data: Nominal, Ordinal, Scale (separate slide,maybe later).
6. Let’s start creating a codebook.
7. Now let’s copy the data into SPSS.
Research questions answered
Let’s answer these questions (1)
A. DESCRIPTIVE STATISTICS
1. How many respondents are male and female?
2. What is the mean of participants’ GPAs?
3. Is there a difference between male and female GPAs?
4. In general, how romantic are the respondents?
5. In particular, is there a difference between male and female
romantic belief levels?
6. In general, what is the type of respondents’ theories of
intelligence?
7. In particular, is there a difference between male and female
theories of intelligence?
Let’s answer these questions (2)
B. INFERRENTIAL STATISTICS
1. Is the difference between male and female GPAs significant?
2. Is the difference between male and female romantic belief levels
significant?
3. Is there a correlation between romantic beliefs and GPA? If there
is, how strong, in what direction and how significant?
4. Is there correlation between romantic beliefs and intelligence
beliefs? If there is, how strong, in what direction and how
significant?
5. Is there a correlation between intelligence beliefs and GPAs? If
there is, how strong, in what direction and how significant?
Further topics for your personal studies...
Parametric and non-parametric tests
Factor analysis
Reliability analysis
Thank You