- Quantitative Data – Analysis and Presentation

Introduction

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

Nominal Data

Ordinal Data (Likert items)

Interval Data

(Likert Scales)

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 [1] to High [5]) 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 (Antonius 2003).

Excel used to cut redundant nominal data from the research data

Planning the Recording Process Using Tools

Resources for Data Storage and Management

Statistical Data Analysis Systems

e.g. SPSS

Spread sheet

applications e.g. MS Excel, Apple iWork

etc;

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 (Taylor-Powell and Renner 2003).

In ethical terms, research data and research participants details stored securely should uphold confidentiality and data protection (Denscombe 2010).

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 FindingsRefocus 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

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; Denscombe 2010).

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) 

Testing reliability of the analysis

Current Research Analysis

Comparative research study Z

Comparative research study Y

Comparative research study X

Example of using triangulation to test the validity of data analysis

Presenting the findingsFindings 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

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).

Scientific objectivity: Quantitative data is able to be manipulated with statistical analysis and since statistics are based on the principles of mathematics, in essence the quantitative approach is confident, objective and rational (Carr 1994; Denscombe 2011).

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)

Benefits Caveats

ConclusionA competent and well practiced researcher is more able to gain the most from a well planned research project..

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.

ReferencesANTONIUS, Rachad (2003) Interpreting Quantitative Data with SPSS Sage Publications London

BJORKSTROM, Monica E and Hamrin Elisabeth K.F (2001) Swedish nurses attitudes towards research and development within nursing Journal of Advanced Nursing 34 (5) pp 706-714

BLACK, Thomas. R (1999) Doing Quantitative Research in the Social Sciences: An integrated approach to research design, measurement and statistics Sage Publications London

BLAXTER, Lorraine, HUGHES, Christina and TIGHT, Malcolm (2008) How to Research 3rd Edition Open University Press Maidenhead BROWN, James D (2011) Likert items and scales of measurement? JALT Testing and Evaluation SIG Newsletter 15 (1) pp 10-14

CARR, Linda. T (1994) The strengths and weaknesses for quantitative and qualitative research: what method for nursing Journal of Advanced Nursing 1994 (20) pp 716-721

DENSCOMBE, Martyn (2010) The Good Research Guide for small scale social research projects 4th Edition Open University Press McGraw Hill

MILES, Matthew B. and Huberman, Michael A. (1994) An Expanded Sourcebook Qualitative Data Analysis 2nd Edition Sage California

PARAHOO, Kader (2006) Nursing Research Principles, Process and Issues 2nd Edition Palgrave MacMillan New York

POLIT, Denise. F and BECK, Cheryl. T (2006) Essentials of Nursing Research: Methods, Appraisal, and Utilization 6th Edition Lippincott Philadelphia

ROBSON, Colin (2002) Real World Research A resource for Social Scientists and Practitioner-Researchers 2nd Edition Blackwell Publishers Oxford

SUDMAN, Seymour and BRADBURN Norman. M (1982) Asking Questions Josey-Bass Publishers California

TAYLOR-POWELL, Ellen and RENNER, Marcus (2003) Analysing Qualitative Data University of Wisconsin – Extension pp 1-10

Mark G. Hopewell - ID 20048791 - Quantitative data gathering and analysis assignment - Research Portfolio of Research Skills MATC 2012