Planning to collate and reduce the data – including rationale
Planning to record the process and tools to use for this purpose
Plans to test reliability of the analysis procedure
Plan to present findings from the analysis
Weakness and Strengths
-Quantitative Data –
Analysis and Presentation
This presentation summarises the planning of a quantitative research approach to
analysing, interpreting and presenting data gained from an online questionnaire.
The analysis of the quantitative data will be used to gauge a small group of MA
Communication students’ attitudes towards the value of using research in their
professional roles in work.
The rationale for using a descriptive statistical approach to quantitative data
analysis is explored by using evidence from similar quantitative approaches in the
research literature and through demonstrating the power of leading social sciences
data processing software used to analyse and present research findings.
The planning process further considers the benefits and caveats of certain
approaches to quantitative data analysis and their effects on attempting to conduct
an effective quantitative research process.
A conclusion brings together the various approaches taken in effective planning for
quantitative data analysis and demonstrates that careful planning can lead to more
reliable research results and outcomes.
The Data Type for Analysis
Nominal, Ordinal and Interval Data Types
Effective quantitative data analysis requires the researcher to
understand the data-type being used. Understanding and implementing
appropriate data-types determines the shape of interpretations and
conclusions drawn from the analysis (Brown 2011; Denscombe 2010).
Data in the questionnaire is represented by nominal, ordinal and
interval data-types (opposite).
Nominal data comprises the minimum amount of data content in the
questionnaire. Nominal data is represented by the occupation type and
number of occupations of each student. Minimal statistical analysis is
achievable with this data (Denscombe 2010).
Ordinal data is present in Likert items. Likert items comprise of
individual questions and are used by respondents to rate their attitudes
(Low  to High ) towards research in their working roles.
Ordinal-type Likert items are summed as each rating is recorded in the
consecutive Likert items. As Likert items are formed into Likert scales,
the subsequent data is treated from thereon as interval type data. This
data type is amenable to statistical analysis since the interval data is
comprised of units of measurement and can be statistically compared
and/or tested against each other (Black 1994; Brown 2011)
Examples of data-types apparent from the questionnaire data
Plan to Reduce and Collate Data
Carr (1994) states that some data-types (particularly
numerical data) risk misrepresenting or not upholding the
true nature of the phenomenon under examination.
Copies of the data should be backed up before work is
commenced (Parahoo 2006; Taylor-Powell and Renner
2003). . . .
Whilst having the research purpose in mind, for example
measuring the attitudes of students towards research, then
thinking through the data which will explain those attitudes
best means organising data into separate data types. This
allows the numerical data to be compared statistically.
Whereas redundant data (such as nominal data) offers no
use for statistical comparison, so reduction of data is
focused on removing nominal data-types, e.g. student
occupations (Black 1999; Sudman and Bradburn (1982).
Spread sheet applications can be used as a means to strip
out redundant data types from the original data set, thus
leaving useful numerical data for further analysis. The
remaining data can be organised in another data area for
collation, for example, in Excel spread sheet workbooks or
in statistics analysis software programs, e.g. SPSS
Excel used to cut
redundant nominal data
from the research data
Planning the Recording Process Using Tools
Resources for Data Storage and Management
To keep a project manageable, reliable and ultimately valid,
high volumes of apparently random interval data need
effective storage pending subsequent analysis of patterns or
statistical correlations existing between that data. This will
need to be made with the assistance of software applications,
for example, Excel spread sheets or/and the Statistical
Package for Social Sciences application (SPSS - opposite)
(Antonius 2003; Miles and Huberman 1994).
Storage and access should facilitate secure shared access to
data for authorised individuals and research teams (TaylorPowell and Renner 2003).
In ethical terms, research data and research participants
details stored securely should uphold confidentiality and data
protection (Denscombe 2010).
applications e.g. MS
Excel, Apple iWork
The necessity for continued consent from all participants, in
order to verify the true record of the data (Parahoo 2006).
Examples of numerical/statistical storage
and processing tools which can be used in
the data recording process
Analysis and Interpretation of Findings
Refocus on the research question before beginning analytical moves.
Revisit the data as a whole, either in notes, software or with colleagues
and decide on the analytical approach to be taken, e.g. statistical
analysis. Document all analytical moves, so that others can follow the
process too (Robson 2002).
We are trying to measure the degree of correlation between students
who believe research is useful and why and those who do not share that
view. Choosing the right analytical approach to statistics is vital in order
to gain the most reliable results from the original data (Parahoo 2006;
Black 1999). Descriptive statistics are best used in this task as we only
need a summary of the research situation due to the need to only gain
initial insights into the student responses and the availability of small
quantities of numerical data (Antonius 2003;Black 1999).
By judicially choosing one or both branches of statistics, descriptive and
inferential approaches (opposite), this will help determine the outcomes
from analysis and affect interpretations of the data (Antonius 2003).
Software tools, such as SPSS and Excel formulae, use sophisticated
statistical functions to assist in the high volume statistical analysis and
reveal the correlations between data and can also test for questionnaire
reliability too. Software may help with identifying relationships but the
researcher‟s objective stance may still also be needed to interpret the
findings more fully in time (Blaxter et al 2008).
The fundamental distinction between
descriptive and inferential statistics
- adapted from Antonius (2003)
Testing reliability of the analysis procedure
Testing reliability of the analysis
Research Instruments: Produces consistent results when used not on
just one occasion but other occasions too. Further testing and retesting of
the instrument ascertains level of reliability. In addition to reliability, validity
can be checked with split-half data comparisons which should return
consistent results (Bjorkstrom and Hamrin 2001; Blaxter et al 2008;
Different Approach: Can the approach toward analysis be generalized by
trying different statistical or other means of data analysis strategies in order
to gain the same outcomes?
Peer review: Asking other researchers for another perspective. Some may
have greater knowledge of the use of statistics and suggest different or
better approaches for the use of statistics (Miles and Huberman 1994; Polit
and Beck 2006)
Triangulation: When used judicially, triangulation uses different types of
data sources or similar studies, to the compare the efficacy of the current
study. (Bjorkstrom and Hamlin 2001; Miles and Huberman 1994).
Software Analysis of Reliability: Testing the data with the „Reliability
Function‟ in the Software Package for Social Sciences (SPSS). This
function uses statistical calculations to test internal reliability of some
aspects of the analysis and reports on findings (Antonius 2003)
Example of using triangulation to
test the validity of data analysis
Presenting the findings
Findings from the data can be displayed in a number of forms. Examples include:Sophisticated Computer Representation – Statistical Software Package for
Social Science (SPSS) allows: representation of sophisticated statistical analysis.
In terms of this research aim, SPSS is a valid choice for presentation application as
findings are readily displayed from various aspects of the analysis. It can be
custom programmed to either replicate tests or adapt them if required in further
uses of the research instrument (Antonius 2003).
Tabular representation – Examples include: structured listings of the various
correlations that exist in the analysed data. Also reliability outcomes of the data
and their representation suggest where poor correlations exist in the data analysis.
A table opposite shows that the overall questionnaire is unreliable in detecting
student attitudes to research, as defined by the low Cronbach Alpha figure (Black
1999; Robson 2002).
Textual Information – Used to compliment table data. Data which is not part of the
current study can be used for comparative purposes. Examples: text may come
from or contribute towards other academic sources for use as research evidence.
Graphs and other diagrams - Representation of data trends; frequency,
distribution during analysis such as: bar, line, scatter plots. Other representations of
other research instruments used in guiding the research process (Bjorkstrom and
Hamrin 2001). Excel spread sheets are a cost effective alternative to SPSS for
presentation of small scale research projects.
Context - All representations must contextualise the findings in terms of linking
findings back to the raw data. Constructing an audit trail of the research process
enables readers to identify key research decisions (Robson 2002).
Examples of representations of research findings
Strengths and Weakness
Scientific objectivity: Quantitative data is
manipulated with statistical analysis and since
based on the principles of mathematics, in
quantitative approach is confident, objective and
1994; Denscombe 2011).
able to be
Measurement: Whereas qualitative analysis allows for
ambiguities which are a reflection of
the social reality
(Denscombe 2010) quantitative data analysis is based on
measured values and thus can be more easily checked by
others because numerical data is less open to ambiguities of
interpretation. Hypothesis can also be tested because of the use
of statistical analysis (Antonius 2003).
Generalizability: Statistics of case studies and large numbers
of participants taking part in studies can tell us findings can be
generalised to a wider population. Unfortunately, in the context
of the small-scale study which forms the basis of this research
task, the findings may not be generalized to a wider student
body (Parahoo 2006).
Rapid Analysis: Sophisticated software removes much of the
need for prolonged data analysis, especially with large volumes
of data involved (Antonius 2003)
Context: Quantitative experiments do not take place in natural
settings. Quantitative approaches also do not allow respondents to
explain their choices or the meaning questions may have for those
respondents. (Carr 1994)
Variability of data quantity: Large volumes of data are needed for
comprehensive and more accurate analysis. Small scale
quantitative studies may be less reliable because of low quantity of
data (Denscombe 2010). This also affects the level of generalizing
findings to wider populations.
Researcher competency: Poor knowledge of the application of
statistical analysis may negatively affect analysis and subsequent
interpretation. (Black 1999)
Bias: Interpretation can depend on the degree of subjectivity of the
researcher. Less pronounced than qualitative approaches, because
of the use of the scientific method but nonetheless findings can be
subtly “manipulated” by unethical researchers.(Robson 2002).
Poor research instruments: Poor choices of research instruments
risk introducing flaws into the research process and invalidating it
(Miles and Huberman 1994).
A competent and well practiced researcher is more able to gain the most from a well planned
Effective quantitative research planning involves the researcher not only knowing which data
collection method to deploy but also how to compare and use the many available software
processing systems that are available for data collection, statistical processing and subsequent
results presentation. Researchers with a knowledge of statistical analysis, both in terms of
descriptive and inferential approaches to analysis, are better able to approach methods of
analysis so as to gain the most detailed interpretation and reliable outcomes from their analysis.
Although using a proven research instrument from other research projects helps guide the shape
of the current research project, it is beneficial when conducting quantitative research that
researchers use their contemporaries in order to conduct peer reviews on approaches to data
collection, statistical analysis and interpretation. Being prepared to collaborate in this way may
add to the productivity and reliability of the quantitative research process.
Failure to plan quantitative research approaches effectively can lead the researcher into
conducting poor data analysis and misinterpreted outcomes from that analysis. Although this
research task has overall shown that reliability of the questionnaire is poor, when attempting to
accurately gauge students views about the usefulness of research at work, it only used a small
respondent sample size from which to gather data. As it typical of quantitative research, low
sample sizes can lead to unreliable test results. However, with a greater sample size and one
that is also more representative of the student and working population, the research instrument
may return more data for the researcher and therein gain more reliable and greater insights into
students views about usefulness of research in their workplace.
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Mark G. Hopewell - ID 20048791 - Quantitative data gathering and analysis assignment - Research Portfolio of Research Skills MATC 2012