A Framework for Selecting Sample Size in Educational Research
on e-business Application
Andreas Ahrens
and Jeļena Zaščerinska
Hochschule Wismar, University of Applied Sciences - Technology, Business and Design,
Philipp-Müller-Straße 14, 23966 Wismar, Germany
Keywords: e-business Application, Generalization, Population, Sample, Sample Size, Measurement Procedures,
Probability Sample, Information-Oriented Sample, Case, Factor.
Abstract: Sample size has a two-fold role in research: sample size is inter-connected with statistical analysis of the
data and generalization. Therefore, sample size has attracted a lot of research efforts in all the research fields
including educational research on E-Business application. However, little attention has been given to a
framework for selecting sample size in educational research on E-Business application. The research
question is as follows: what shapes sample size in educational research on E-Business application? The aim
of the research is to analyze theoretically sample size shaping underpinning elaboration of a framework for
selecting sample size in educational research on E-Business aplication. The present research involves a
process of analysing the meaning of the key concepts statistical analysis, generalisation, population,
sample, measurement procedures, probability sample, information-oriented sample, case and factors.
Moreover, the study demonstrates how the key concepts are related to the idea of “sample size”. Explorative
research was employed. Interpretive research paradigm was used. The empirical study involved four experts
from different countries in February - April 2013: The findings of the research allow drawing the
conclusions on the framework for selecting sample size in educational research on E-Business aplication.
Directions of further research are proposed.
1 INTRODUCTION
E-Business application has been widely spreading
since its emerging as e-Business application
provides businesses with such benefits as time
saving, cost reduction, service improvement,
management facilitation, etc.
For the increase of efficiency of e-Business
application in complex and constantly self-
regenerating environments (Kardoff, 2004), a
number of issues in e-Business application has to be
taken into consideration such as context analysis,
customer needs analysis, etc. Success in dealing with
the above-mentioned issues in e-Business
application is generated by a couple of strategies.
For the synergy between theory and practice in e-
Business application, one of the strategies is focused
on the sample analysis (Mayring, 2007).
Sampling focuses on obtaining a group of
subjects who will be representative of the larger
population or will provide specific information
needed (McMillan, 1996). The goal is to select a
sample that will adequately represent the population,
so that what is described in the sample will also be
true of the population (McMillan, 1996). In
educational research, the best procedure for selecting
such a sample is to use probability sampling as non-
probability sampling does not ensure the
construction of a parameter for a population. The
primary distinction between the two domains is that
the probability sampling study findings can be
generalized to the target population while the
nonprobability sampling study findings can only be
generalized to the institution where the sample was
studied (Summers, 1991).
The key characteristic of a
probability sample is that each element in the
population has a known probability of being
included in the sample (Sweeney, 2013). The
probability sampling procedures include simple
random, systematic, stratified, and cluster
(McMillan, 1996) as demonstrated in Figure 1.
The process of sample selection reveals such an
issue as sample size. Therefore, sample size has
attracted a lot of research efforts in all the research
39
Ahrens A. and Zaš
ˇ
cerinska J..
A Framework for Selecting Sample Size in Educational Research on e-business Application.
DOI: 10.5220/0005020000390046
In Proceedings of the 11th International Conference on e-Business (ICE-B-2014), pages 39-46
ISBN: 978-989-758-043-7
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Probability sampling procedures.
fields. In farmer surveys, sampling methods which
range from the practical to the mathematical are
focused on good practical points (Coe, 1996). In
organizational research, the procedures for
determining sample size for continuous and
categorical variables have been described by use of
Cochran’s (1977) formulas (Bartlett, Kotrlik,
Higgins, 2001). In management and economics
research, the emphasis is put on analysis of factors
influencing sample size (Kamau, Guandaru, Kariuki,
Nduati, 2012). In psychological research, analysis of
selection of sample size has contributed to three
criteria of sample size (Kroplijs, Raščevska, 2004).
Psychological research and educational research
are closely inter-connected as depicted in Figure 2.
Figure 2: Inter-relationship between psychological and
educational research.
Psychological research provides the basis for
pedagogical and, consequently, educational
developments in terms of organization of
educational environment, curriculum, institution
activities, and etc. In its turn, educational research
facilitates the promotion of psychologists’
professional knowledge, competences and behavior
aimed at ensuring new discoveries, innovations, etc.
Thus, in psychological and, consequently,
educational research, selection of sample size is
identified by the application of three criteria of
sample size (Kroplijs, Raščevska, 2004) such as
effort quantity or intensity of interaction with sample
components (subjects in educational research) or
elements; a number of topis investigated within a
particular educational research; subject matter within
a particular research. Hence, sample size has a two-
fold role in research as depicted in Figure 3: sample
size is inter-connected with statistical analysis of the
data and generalisation or theory formulation.
Figure 3: Relationship between sample size and statistical
analysis of the data and generalization in research.
In educational research, sample size has attracted
a lot of research efforts, too. Traditionally, selection
of sample size refers to empirical studies of
educational research as illustrated in Figure 4.
Figure 4: Relationship between research, empirical studies
and sample size in educational research.
However, little attention has been given to a
framework for selecting sample size in educational
research.
The research question is as follows: what shapes
sample size in educational research on E-Business
application? The aim of the research is to analyze
theoretically sample size shaping underpinning
elaboration of a framework for selecting sample size
in educational research on E-Business aplication.
The present research involves a process of
analysing the meaning of the key concepts statistical
analysis, generalisation, population, sample,
measurement procedures, probability sample,
information-oriented sample, case and factors.
Moreover, the study demonstrates how the key
concepts are related to the idea of “sample size”.
The methodological background of the present
contribution is based on the System-Constructivist
Theory. The System-Constructivist Theory is
introduced as the New or Social Constructivism
Pedagogical Theory. The System-Constructivist
Probability sampling
procedures
Cluste
r
Sample
random
Systematic
Stratified
Research
Educational
r
esea
r
c
h
Psychological
research
Research
Sample
size
Generalisation
Statistical
anal
y
sis of the
Research
Empirical study
Sam
p
le size
ICE-B2014-InternationalConferenceone-Business
40
Theory is formed by Parsons’s System Theory
(Parsons, 1976) on any activity as a system,
Luhmann’s Theory (Luhmann, 1988) on
communication as a system, the Theory of Symbolic
Interactionalism (Mead, 1973), the Theory of
Subjectivism (Groeben, 1986). The application of
this approach to learning introduced by Reich
(Reich, 2005) emphasizes that human being’s point
of view depends on the subjective aspect (Maslo,
2007):
everyone has his/her own system of external
and internal perspectives (Ahrens, Zaščerinska,
2010) that is a complex open system
(Rudzinska, 2008) and
experience plays the central role in the
knowledge construction process (Maslo, 2007).
Therein, the subjective aspect of human being’s
point of view is applicable to the present research on
selection of sample size in educational research on
E-Business application.
2 THEORETICAL FRAMEWORK
By a theoretical framework, the unity of concepts,
their definitions and existing theories that are used
for a particular study are meant. A theoretical
framework is used to identify the specific viewpoint
(framework) taken into consideration while
analysing and interpreting the gathered data.
Educational research in general and sample size
in particular is initially shaped by educational
research paradigm (Taylor, Medina, 2013). A
paradigm is a comprehensive belief system, world
view, or framework that guides research and practice
in a field (Willis, 2007). Such educational research
paradigms are identified (Taylor, Medina, 2013) as
the positivist paradigm that is commonly used
in research to test theories or hypotheses,
the post-positivist paradigm that includes the
analysis of interaction between the researcher
and his/her research participants via use of such
quantitative methods as survey research and
qualitative methods as interviewing and
participant-observation (Creswell, 2008),
the interpretive paradigm which aims to
understand other cultures, from the inside
through the use of ethnographic methods such
as informal interviewing, participant
observation and establishment of ethically
sound relationships,
the critical paradigm which involves identifying
and transforming socially unjust social
structures, policies, beliefs and practices,
the postmodern paradigm which holds that what
goes on in our minds and hearts is not directly
accessible to the world outside us,
the multi-paradigmatic research by combining
methods and quality standards drawn from two
or more of the newer paradigms, and
the new ‘integral paradigm’ that provides a
rationale for drawing upon multiple paradigms
to design new hybrid methodologies that
involve multiple epistemologies and their
accompanying quality standards.
Further on, sample size is framed by the research
methodology: quantitative methodology that
requires large, representative and precise sample,
and qualitative methodology that is based on a small
and non-representative sample.
Later, selection of sample size in educational
research involves analysis of access to the sample
which is a key issue in research (Cohen, Manion,
Marrison, 2003), and resources such as time,
personnel, technical support, competences,
experiences, etc (Flick, 2004). It should be noted
that research proposals are frequently based on an
unrealistic relationship between the planned tasks
and the personnel resources that can (realistically) be
asked for (Flick, 2004).
Then, selection of sample size is based on the
analysis of the inter-relationships between
sample size and statistical analysis of the data,
sample size and generalization.
For the analysis of the relationship between
sample size and statistical analysis of the data,
statistical analysis is identified as the unity of the
collection and processing of data and the results’
interpretation as demonstrated in Figure 5.
Figure 5: Elements of statistical analysis.
Below the relationship between sample size and
each element of statistical analysis is revealed.
Data collection implies use of measurement
procedures as depicted in Figure 6.
Statistical analysis
Data collection
Results’
interpretation
Data processing
AFrameworkforSelectingSampleSizeinEducationalResearchone-businessApplication
41
Figure 6: Relationship between data collection and
measurement procedures.
Analysis of the relationship between sample size
and measurement procedures in the present research
is based on measurement procedures defined as
measurement tools and scale as shown in Figure 7.
Figure 7: Elements of measurement procedures.
Such measurement tools as questionnaires as
well as measurement scales are fixed on one sample
and tested in a new sample (Gigerenzer, 2004) as
measurement tools and/or scales have to be cross-
validated in order not to provide non-precise data.
Therein, a sample is to be of such a size as, in the
measurement phase of the educational research, the
parameters of measurement tools and scales are kept
fixed when used by sample’s further components or
elements.
Traditionally, data processing includes two
methods (Geske, Grīnfelds, 2006) as illustrated in
Figure 8: method of descriptive statistics and method
of inferential statistics.
Figure 8: Two methods of data processing.
Figure 9 demonstrates methods of descriptive
statistics (Geske, Grīnfelds, 2006).
In educational research such a method of
inferential statistics as significance test is closely
inter-connected with sample size while null
hypothesis testing relates to two measured
phenomena, and variance analysis – to
a variable's
values.
Figure 9: Methods of descriptive statistics.
Figure 10 shows elements of central tendencies
(Geske, Grīnfelds, 2006).
Figure 10: Elements of central tendencies.
In its turn, Figure 11 demonstrates methods of
inferential statistics (Geske, Grīnfelds, 2006) in
educational research.
Figure 11: Methods of inferential statistics.
For the analysis of the relationship between
sample size and significance test, a sampling
distribution is required. Sampling distribution is
connected with probability distribution: the sampling
distribution of the mean is a probability distribution
of the possible values of the mean that would occur
if we were to draw all possible samples of a fixed
size from a given population (Plonksy, 1997).
Knowledge of the sampling distribution is necessary
for the construction of an interval estimate for a
population parameter (Sweeney, 2013). This is why
a probability sample is needed; without a probability
sample, the sampling distribution cannot be
determined, and an interval estimate of a parameter
cannot be constructed (Sweeney, 2013).
Sampling distribution is usually examined by the
Kolmogorov-Smirnov test. Statistical Package for
the Social Sciences (SPSS) Exact Tests also offers
Data collection
Measurement procedures
Measurement procedures
Measurement
tools
Measurement
scale
Data processing
Descriptive
statistics
Inferential
statistics
Statistical analysis
Variance,
skewness,
k
u
r
tos
i
s
Frequencies,
percentage
Central tendency
Central tendency
Median
Mean
Inferential statistics
Null
hypothesis
testing
Significance test
Variance
analysis
Mode
ICE-B2014-InternationalConferenceone-Business
42
the asymptotic version of the Kolmogorov-Smirnov
test to reach correct conclusions with small samples
(Statistical Package for the Social Sciences, 2009).
A sampling distribution is considered within the
framework of deviation of the empirical distribution.
Deviation of empirical distribution is significant if
Significance p or Asymp. Sig. (2-tailed) is smaller
than 0.05 (Lasmanis, 2003). The Kolmogorov-
Smirnov test identifies that if the deviation of
empirical distribution is greater than 0.05, sampling
distribution is normal, or if the deviation of
empirical distribution is smaller than 0.05, sampling
distribution is non-normal. Normal empirical
distribution implies the use of parametric methods in
the empirical study, and non-normal empirical
distribution - the use of non-parametric methods.
However, the use of normality tests does not
determine automatically whether or not to use a
parametric or non-parametric test: they can help
make the decision (GraphPad Software, 2007). For
example, non-parametric tests have little or no
power to find a significant difference if there is a
tiny sample (a few subjects in the group) (GraphPad
Software, 2007).
In comparison to theoretical sampling which
relates not to the sample size determination, but to
the analysis of the necessity in the increase of the
sample size for the enrichment of theory developed
from the data obtained in a previous sample
(Kroplijs, Raščevska, 2004) in psychological and,
consequently, educational research, empirical
sampling distributions are not true sampling
distributions, since all possible samples are not
chosen (Plonsky, 1997), as well as some differences
exist between any natural groups (Gigerenzer,
2004). Therein, a sample is to be of such a size as, in
the data processing phase of the educational
research, the tests carried out on a given set of data
allow extracting the required information in an
appropriate form such as diagrams, reports, or
tables.
For the analysis of the relationship between
sample size and results’ interpretation, interpretation
and judgement are part of the art of statistics
(Gigerenzer, 2004). Thus, a sample is to be of such a
size as, in the statistical analysis phase of the
empirical study within educational research, the
information extracted from the obtained data
processing ensures a possibility to make conclusions
and generalisations.
For the analysis of the relationship between
sample size and generalisation, generalisation is
traditionally considered as a central aim of science,
as a process of theory formulation for further
applications (Mayring, 2007). Types of
generalisation include universal laws, statistical
laws, rules, context specific statements, similarities
and differences, descriptive studies, explorative
studies, and procedures to come to results (Mayring,
2007) as illustrated in Figure 12.
Figure 12: Eight types of generalisation.
Generalisation can be arrived by different
strategies which also include analysis of total
population and samples (Mayring, 2007). Therein,
Figure 13 demonstrates that sample is part of
population.
Figure 13: Relationship between total population and
sample.
A population is a group of elements or cases,
whether individuals, objects, or events, that conform
to specific criteria and to which we intend to
generalize the results of the research (McMillan,
1996). This group is also referred to as the target
population or universe (McMillan, 1996). It is also
important to distinguish the target population from a
list of elements from which a group of subjects is
selected (McMillan, 1996). The list is termed the
survey population or sampling frame (McMillan,
1996). The sample is the group of components,
elements, or a single element, from which data are
obtained (McMillan, 1996). By an individual who
participates in a research study or is someone from
Total population
Sam
p
le
Eight types of generalization
Universal
laws
Similarities and
differences
Statistical
laws
Descriptive
studies
Context specific
statements
Procedure
Explorative
studies
Rules
AFrameworkforSelectingSampleSizeinEducationalResearchone-businessApplication
43
whom data are collected, subjects or cases are meant
(McMillan, 1996). The term subject may also
identify individuals whose behavior, past or present,
is used as data, without their involvement in some
type of treatment or intervention (McMillan, 1996).
The focus in selecting the cases has changed to
information-oriented sampling, as opposed to
random sampling (Flyvbjerg, 2006). This is because
an average case is often not the richest in
information. In addition, it is often more important
to clarify the deeper causes behind a given problem
and its consequences than to describe the symptoms
of the problem and how frequently they occur
(Flyvbjerg, 2006). Random samples emphasizing
representativeness will seldom be able to produce
this kind of insight; it is more appropriate to select
some few cases chosen for their validity (Flyvbjerg,
2006).
In educational research, it is usually impractical
and unnecessary to measure all the elements in the
population of interest (McMillan, 1996). Typically, a
relatively small number of subjects or cases is
selected from the larger population (McMillan,
1996). In educational research, a sample should
include more than 30 subjects due to the “central
limit theorem” (Mayring, 2007), a sample which
involves less than 30 subjects is small (Arhipova,
Bāliņa, 2003), and a sample which consists of a few
subjects is tiny (GraphPad Software, 2007). It should
be noted that generalization can be drawn from a
single case study, too (Mayring, 2007). However, for
the result confirmation and drawing more general
conclusions, the case basis is to be widened
(Mayring, 2007) up to three-ten single cases (Yin,
2005). Therein, a sample is to be of such a size as, in
the analysis phase of the research, sample’s further
components or elements do not change conclusions
or generalisations drawn from the obtained data
(Kroplijs, Raščevska, 2004).
Hence , selection of sample size is shaped by the
following framework: in the educational research, a
sample is to be of such a size as
in the measurement phase, the parameters of
measurement tools and scales are kept fixed
when used by sample’s further components or
elements,
in the data processing phase, the tests carried
out on a given set of data allow extracting the
required information in an appropriate form
such as diagrams, reports, or tables,
in the statistical analysis phase, the information
extracted from the obtained data processing
ensures a possibility to make conclusions and
generalisations.
in the analysis phase, sample’s further
components or elements do not change
conclusions or generalisations drawn from the
obtained data (Kroplijs, Raščevska, 2004).
3 EMPIRICAL RESEARCH
The present part of the paper demonstrates the
design of the empirical research, survey results and
findings of the empirical study.
3.1 Research Design
The design of the present empirical research
comprises the purpose and question, sample and
methodology of the present empirical study.
The empirical study was aimed at evaluating
the framework for selecting sample size in
educational research on E-Business aplication.
The empirical research’s guiding question was
as follows: What is the expert evaluation of the
framework for selecting sample size in educational
research on E-Business application?
The present empirical study involved four
experts from different countries in February - April
2013. All the respondents have been awarded PhD
Degree in different fields of educational science.
As the respondents with different cultural
backgrounds and diverse educational approaches
were chosen, the sample was multicultural. Thus,
the group (age, field of study and work, mother
tongue, etc.) is heterogeneous. The sample of four
experts consisted of two researchers in the field of
educational research, Educational Research
Association, "Freie Universität" (Free University),
Berlin, Germany, a researcher in the field of
educational research, Latvia University of
Agriculture, Jelgava, Latvia and a researcher in the
field of applied research in education, CAH -
Vilentum University of Applied Sciences, Dronten,
the Netherlands. In order to save the information of
the study confidential, the respondents’ names and
surnames were coded as follows: two researchers
from Germany were given the codes of E1 (Expert
1) and E2 (Expert 2), a researcher from Latvia was
pointed as E3 (Expert 3), a researcher from the
Netherlands was considered as E4 (Expert 4).
Interpretive research paradigm that corresponds
to the nature of humanistic pedagogy (Luka, 2008)
was used in the present empirical study.
Interpretive paradigm is characterized by the
researchers’ practical interest in the research
question (Cohen, Manion, Marrison, 2003).
ICE-B2014-InternationalConferenceone-Business
44
Researcher is the interpreter.
Exploratory research was employed in the
empirical study (Phillips, 2006). Exploratory
research is aimed at generating new questions and
hypothesis (Phillips, 2006). The exploratory
methodology proceeds from exploration in Phase 1
through analysis in Phase 2 to hypothesis
development in Phase 3 as demonstrated in Figure
14.
Figure 14: Methodology of the exploratory research.
The qualitatively oriented empirical study
allows the construction of only few cases
(Mayring, 2004).
The choice of experts was based on two criteria
such as recognized knowledge in the research
topic, absence of conflict of interests (Lopez,
Salmeron, 2011) as depicted in Figure 15.
Figure 15: Criteria of choosing experts.
The number of experts depends on the
heterogeneity of the expert group: the greater the
heterogeneity of the group, the fewer the number of
experts (Okoli, Pawlovski, 2004). Thus, four is a
good number of experts for the study (Lopez,
Salmeron, 2011). Therein, the non-structured
interviews comprised four experts who were
researchers from different countries. It should be
noted that all the researchers were professors in the
fields connected with educational research. All the
four researchers had received extensive research
experience.
3.2 Survey Results
In order to analyse the framework for selecting
sample size in educational research on E-Business
aplication, non-structured inteviews were caried out.
Non-structured interviews with experts were
conducted in order to search for the main categories
of the research field (Kroplijs, Raščevka, 2004).
Expert 1 thanked the authors for the interesting
abstract submitted to the conference where Expert 1
was acting as a reviewer.
Expert 2 underlined that the authors had tried to
summarize a study and identify the main
characteristics of this study.
Expert 3 was interested in the continuation of the
study.
Expert 4 assumed that the factors play a key role
in forming the sample size in educational research.
3.3 Findings of the Empirical Study
Summarizing content analysis (Mayring, 2004) of
the data reveals that experts positively evaluated the
framework for selecting sample size in educational
research on E-Business aplication.
4 CONCLUSIONS
The empirical findings of the research allow drawing
the conclusions on the experts’ positive evaluation
of the framework for selecting sample size in
educational research on E-Business aplication.
The following research question has been
formulated: what are the principles of selection of
sample size in educational research?
The role of sample size it plays in research is to
be re-considered in further studies. Further research
tends to focus on empirical studies to be carried out
in other institutions. And a comparative research of
different countries could be carried out, too.
REFERENCES
Ahrens, A., Zaščerinska, J., 2010. Social Dimension of Web
2.0 in Student Teacher Professional Development. In
Proceedings of Association for Teacher Education in
Europe Spring Conference 2010: Teacher of the 21st
Century: Quality Education for Quality Teaching, 179-
186, Riga: University of Latvia.
Criteria of choosing experts
Recognized
knowledge in
the research
Absence of
conflict
interests
AFrameworkforSelectingSampleSizeinEducationalResearchone-businessApplication
45
Arhipova, I., Bāliņa S., 2003. Statistika ekonomikā:
risinājumi ar SPSS un Microsoft Excel. /Irina
Arhipova, Signe Bāliņa. Rīga: Datorzinību centrs.
Bartlett, J. E., Kotrlik J. W., Higgins, C.C., 2001.
Organizational Research: Determining Appropriate
Sample Size in Survey Research. Information
Technology, Learning, and Performance Journal, Vol.
19, No. 1, 43-50, Spring 2001.
Cochran, W. G., 1977. Sampling techniques (3
rd
ed.). New
York: John Wiley & Sons.
Coe, R., 1996. Sampling Size Determination in Farmer
Surveys. ICRAF Research Support Unit Technical
Note No4. ICRAFWorld Agroforestry Centre Nairobi,
Kenya.
Cohen, L., Manion, L., Marrison, K., 2003. Research
Methods in Education. London and New York:
Routledge/Falmer Taylor & Francis Group.
Creswell, J., 2008. Educational research: Planning,
conducting and evaluating quantitative and qualitative
research (3
rd
ed.). Upper Saddle River, NJ: Pearson
Prentice Hall.
Flick, U., 2004. Design and Process in Qualitative
Research. In: U. Flick, E. Von Kardoff and I. Steine
(Eds), A Companion to Qualitative Research, pp. 146.-
152. SAGE, UK, Glasgow.
Flyvbjerg, B., 2006. Five Misunderstandings About Case-
Study Research. Qualitative Inquiry,12(2), 219-245.
Geske, A., Grīnfelds, A., 2006. Izglītības pētniecība.
Latvijas Universitātes Akadēmiskais apgads.
Gigerenzer, G., 2004. Mindless Statistics. The Journal of
Socio-Economics, Volume 33, Issue 5, pp. 587-606.
Elsevier Inc.
GraphPad, 2007. Statistics. GraphPad Software Inc.
Groeben, N., 1986. Handeln, Tun, Verhalten als Einheiten
einer verstehend-erklärenden Psychologie. Tübingen:
Francke.
Kamau, K., Guandaru, C., Kariuki, Nduati, S., 2012.
Factors Influencing Sample Size for Internal Audit
Evidence Collection in the Public Sector in Kenya.
International Journal of Advances in Management and
Economics. Mar.-April. 2012 | Vol.1 | Issue 2|42-49.
Kardoff, E., 2004. Qualitative Evaluation Research. In: U.
Flick, E. Von Kardoff and I. Steinke (Eds). A
Companion to Qualitative Research, pp. 137-142.
SAGE, UK, Glasgow.
Kroplijs, A., Raščevska, M., 2004. Kvalitatīvās
pētniecības metodes sociālajās zinātnēs. Rīga: RaKa.
Lasmanis, A., 2003.Māksla apstrādāt datus: pirmie soļi.
Rī
ga.
Lopez, C., Salmeron, J., 2011. A Framework for
Classifying Risks in ERP Maintenance Projects.
Proceedings of International Conference on e-
Business (ICE-B 2011), July 18-21, 2011, 201-204.
SciTePress - Science and Technology Publications,
Seville, Spain.
Luhmann, N., 1988. Erkenntnis als Konstruktion. Bern:
Benteli.
Luka, I., 2008. Students and the educator's co-operation as
a means of development of students' ESP competence.
European Conference on Educational Research,
University of Goteborg, Goterborg, Sweden.
Maslo, E., 2007. Transformative Learning Space for Life-
Long Foreign Languages Learning. In International
Nordic-Baltic Region Conference of FIPLV Innovations
in Language Teaching and Learning in the
Multicultural Context, 38-46, Rīga: SIA "Izglītības
soļi". Riga, Latvia.
Mayring, P., 2004. Qualitative Content Analysis. Flick,
U., von Kardoff, E., Steinke, I. (Eds.) A Companion to
Qualitative Research, pp. 266-269. Glasgow: SAGE.
Mayring, P., 2007. On Generalization in Qualitatively
Oriented Research. Forum: Qualitative Social
Research, 8(3), Art. 26, 1-8.
McMillan, J. H., 1996. Educational Research
Fundamentals for the Consumer. Second Edition.
Virginia Commonwealth University, HarperCollins
College Publishers.
Mead, G. H., 1973. Geist, Identitat, und Gesselschaft.
Frankfurt. A. M.
Okoli, C., Pawlovski, S., 2004. The Delphi Method as a
Research Tool: an example, design considerations and
applications. Information and Management,42(1), 15-
29.
Parsons, T., 1976. Theorie sozialer Systeme. Opladen:
Westdeutscher Verlag.
Phillips, D., 2006. Comparative Education: method.
Research in Comparative and International
Education, Volume 1, Number 4, 304-319.
Plonsky, M., 1997. Psychological Statistics. Retrieved
31/01.2014 from
http://www4.uwsp.edu/psych/stat/10/hyptestc.htm#I1.
Reich, K., 2005. Systemisch-konstruktivistische
Pädagogik. Weinheim u.a., Beltz, (2005).
Rudzinska, I., 2008. The Quality of Aim Setting and
Achieved Results in English for Specific Purposes-
Study Course in Lecturers and Students’ Opinion. In
Proceedings of the ATEE Spring University Conference
Teacher of the 21st Century: Quality Education for
Quality Teaching, 366-373. Riga: University of Latvia.
Statistical Package for the Social Sciences, 2009. SPSS
Exact Test 10.0. Retrieved 12/03/2009 from
ftp://ftp.spss.cpm/pub/web/specs/SET10SPC-0799.pdf.
Summers, S., 1991.
Selecting the sample for a research
study. .J Post Anesth Nurs. 1991 Oct; 6(5):355-8.
Sweeney, D., 2013. Statistics: The Normal Distribution. In
Encyclopædia Britannica.
Taylor, P. C., & Medina, M. N. D., 2013. Educational
Research Paradigms: From Positivism to
Multiparadigmatic. The Journal of Meaning-Centered
Education . Volume 1, Article 2.
Willis, J. W., 2007. Foundations of qualitative research:
Interpretive and critical approaches. Thousand Oaks,
CA: Sage Publications.
Yin, R., 2005. Case Study Research: Design and Methods
(3
rd
edition). Thousand Oak: SAGE.
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