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Course: PEBL 101: RESEARCH METHODOLOGY & DESIGN FOR BUSINESS > Section: UNIT 7b: DATA PROCESSING, ANALYSIS AND PRESENTATION (CTD…)

Unit 7b Learning outcomes

Indicative Content

Qualitative Data analysis, Data presentation (Tables, Diagrams, charts and Maps)

Intended Learning Outcomes for Unit 7:

By the end of Unit 7, you will be able to;

1. prepare data for analysis
2. apply statistical techniques to analyse quantitative data
3. apply appropriate procedures and techniques to analyse qualitative
4. apply computer software packages to analyse data
5. present and interpret data

Unit 7b E-tivities

Learning Outcome(s) addressed: Unit 7b supports Module Learning Outcomes 2, 3, 4, 5 & 6.

Purpose: The purpose of this part of Unit 7 is to enable you learn qualitative data analysis and application of computer software in data analysis. It builds on knowledge learnt in Unit 7a and will enable you completed Unit 8.

Over to you: Just like quantitative data is analysed using several statistical techniques, qualitative data too is analysed. The process of analyzing qualitative data does not rely on figures and calculations but anlaysis of themes. This section will also enable you learn how to uses computer software to analyse data.  In this Unit, you will describe presentation and interpretation of research findings.

Preparatory research: In order to be able to analyse qualitative data, use computers to analyse data and also interpret research findings;

1. Read minilecture about qualitative data analysis here.
2. Read minilecture about data presentation and interpretation here.
3. Read about data presentation here and here.
4. Read minilecture about data analysis and presentation using statistical computer software/ programs here.
5. Watch videos about data analysis using SPSS (Statistical Package for Social Sciences) here , here and here.

E-tivity 7.2: Using the questionnaire that you prepared for the concept paper, get one person to fill it. Get all the group’s questionnaires and enter them into SPSS. Those will be 10 (ten) questionnaires. Basing on the data you collected, do the following;

a)      Edit the questionnaires,

b)      Handle blank responses

c)      Code the data

d)     Create a data file

e)      Enter the data into SPSS software

f)       Analyze the data for measures of central tendency.

g)      Present your research findings in tables, graphs and charts and interpret the results. Keep the presentation in your portfolio by 21^st April 2019.

Link to other Units:  Unit 7b links to Units 1, 2, 5, 6 & 8.

Anticipated time required for Unit 7b activities: 8 hours

Deadline for Unit 7b activity:

E-tivity 7.1: Presentation of results: 21^st April 2019.

QUALITATIVE DATA ANALYSIS

ANALYSING QUALITATIVE DATA

This is the process of summarizing/analysing opinions/ attitudes collected during the course of qualitative research.

Qualitative data is

a) based on meanings expressed in words rather than numbers.

b) often not standardised and must therefore be classified into categories before analysis.

c) analysed through conceptualisation as opposed to quantitative analysis.

Unlike quantitative data there are no standard approaches to the analysis of qualitative data rather there are many qualitative research approaches and traditions.

The types of qualitative data and the various methods and tools used to collect them are as under;

Source: http://hsc.uwe.ac.uk/dataanalysis/qualWhat.asp

Stages of Qualitative data analysis

In general, analysis of qualitative data follows the following four stages;

1) Categorisation.

2) Unitization.

3) Recognition of relationships

4) Testing of relationships/ hyposthese

1. Categorization.

Categories result from the desegregation of the mass of qualitative data collected into meaningful and related parts. The categorization is guided by the purpose of the research as expressed through the research objectives.

 The qualitative part of the data comprising descriptions of behaviours of people, events, and situations recorded as free text must first be categorized.  This begins by reading through the raw data and placing similar responses in identifiable categories.

The categories may be:

a)The researchers own creations.

b)Observations borrowed from existing literature.

c)Terms emerging from responses in the research.

2. Unitization

Once the categories have been created, individual responses are then allocated into the categories that they fit best.

3. Recognition of relationships

In this stage the researcher or analyst studies the categories and units looking for linkages, patterns and relationships.

4. Testing of relationships/hypotheses to reach conclusions.

To check if relationship patterns and linkages exist or if they do not exist, the researcher or analyst, tests the hypotheses/ prepositions he/she had earlier formulated . He/she tests for their significance.

Techniques Used In Qualitative Data Analysis

Qualitative data may be analysed by either first quantifying it or not. The following two broad techniques are available for analysing qualitative data;

1. Quantifying Methods

2. Non-quantifying Methods

1. Quantifying Methods of analysing qualitative data

These may be

a. informal methods or

b. formal methods

a) Informal methods of analysing qualitative data

When using informal methods, the researcher looks for repetitive responses or behaviors that exhibit a recognizable pattern.

Through this, the researcher counts the frequencies of these responses / behaviors in order to determine which responses occur most and then determines whether they are important or not important.

b) Formal methods of analysing qualitative data

This is a framework for linking subjective experiences or perceptions. For example, suppose our subject of interest is the quality the assets management system, we go through the following steps to analyse the data;

.

1. Determine the focus of analysis –qualities of a good assets management system ie ability to control, practicability, acceptability, and qualified personnel available to run it.

2. From the population responses, you arrive at the key objects/abstracts of the respondents' perceptions ie what are the key elements the respondents use in assessing the qualities of good assets control focussing on Inventory, Cash and Debtors.

3. Once you establish these elements (abstracts) i.e. the qualities that measure the elements are described and then they are tabulated against the observations for each category of asset.

ELEMENTS | Inventory | Cash | Creditor | Debtor
CONSTRUCTS | control | control | control | control
System control | 5 |  |  |
Practicability |  | 7 |  |
Acceptability |  |  |  |
Qualilified personal |  |  |  |

4.Evaluate the relationship between the elements and the constructs on a scale of your choice ranging from say very important to inconsequential. It can be a four, five, six or seven point Likert scale.

The whole system is referred to as a linking mechanism in which scores are used to indicate the strength of the relationship between elements and their constructs.

2. Non-Quantifying Methods of Qualitative Data analysis

Some researchers, in particular phenomenologist, do not accept attempts to quantify qualitative data on the grounds that it loses its character and richness in the process. Therefore they advocate for the use of non-quantifying methods.

In general non quantifying methods follow the following steps;

a) Understanding the data (comprehensive)

b) Evolving patterns in the data (synthesis)

c) Developing themes based on patterns developed in stage (2 )

d) Generalizing

For more on qualitative data analysis,  watch the video here:

DATA ANALYSIS AND PRESENTATION USING STATISTICAL COMPUTER SOFTWARE/ PROGRAMS

This involves using computers in analyzing and presenting research findings.

There are three things that a researcher needs to do when using computer software to analyse data:

(1) Get data into a computer (input)

(2) Process data (analysis)

(3) Get data out of the computer, in a form which human brains can understand (output).

The data comes in two quite different varieties:

(a) The numbers and coded data produced by questionnaires

(b) The words produced by qualitative research

Since most survey questions are multiple-choice, and a respondent is asked to choose from a limited range of answers each possible answer is given a code - usually a single-digit number. 1 means the first possible answer to the question, 2 is the second listed, and so on.

For example, the survey might include the question:

Qn. 10 All cash is disbursed after being fully authorized.

Strongly agree | Agree | Not sure | Disagree | Strongly disagree
1 | 2 | 3 | 4 | 5

The numbers below the options are the codes. Instead of keying in ‘Strongly Agree’ the data entry operator keys in 1. A ‘Strongly Agree’ is set up in a computer file, which tells the survey software that "1" in Qn.10 means "not at all" - and so on.

The software turns the codes back into full wording when the results are displayed. This method of coding produces small data files, fast processing, and very fast data entry. But it's also possible to make serious mistakes.

If the first possible answer for every question is 1, you can easily miss a question or enter the same answer twice. Such errors are prevented by good coding design, and good data entry software. If the operator tries to answer "5" to that question, the software should beep, display an error message, and refuse to continue.

Most of the statistical programs allow spreadsheet-style data entry. After a survey has been done, with printed questionnaires, you can sit down and enter all the results into this spreadsheet.

Common Statistical software packages

These include

a) Statistical Package for Social Science (SPSS)

b) Epi Info

c) Statistica

d) Statview

e) Datadesk

Which program is best?

There's no single answer to this question. It depends on:

- What level of analysis you need to use
- How much money you have available
- Which operating system you are using
- How much time you're willing to spend learning the program
- What skills and statistical knowledge you already have
- What software your friends are using (maybe you can ask them for help)

PRESENTATION / INTERPRETATION OF RESEARCH

PRESENTATION OF RESEARCH FINDINGS

Findings should be presented in a logical and sequential way so that conclusions can be drawn from them. When presenting findings,

1. Keep your research objective and question in mind.

2. Structure and label each presentation clearly to avoid misinterpretation.

3. It is advisable that data is presented according to the research objectives or questions.

Data can be presented in tables and figures (Tables, Charts and graphs and Figures/diagrams)

1. Tables

See APA tables

2. Charts and Graphs

They are a means of communicating data visually. Each chart or graph should be accompanied by a short paragraph that describes the information in it and its implication to the objectives of the study and include;

- · Bar charts eg simple, composite, multiple or percentage
- · Pie charts
- · Histograms
- · Frequency polygons

c) Figures/Diagrams

These include scatter diagrams which are used to represent relationship is bi-variate data.

General guidelines when using tables, Charts, Graphs, Diagrams and figures

- A clear descriptive title.
- Table/ figure number ie they should be numbered in an orderly and chronological manner
- State the unit of data.
- State the source of data in italics eg Secondary data or Primary data
- Explain abbreviations and any usual terminologies used.
- State the size of the sample on which the values in the table or diagrams are based specifically for diagrams.
- Have clear axis labels
- Use meaningful and clearly explained shading. It is conventional to use darker colours for smaller areas.
- Include a key ( legend ).
- Have clear row and column headings.
- Where applicable, let the columns and rows flow in a logical sequence.
- For each table and diagram append a descriptive discourse i.e. attach a paragraph explaining the table or diagram.
- They should be neat

Interpretation and Discussion of your findings

Interpretation is the process of drawing inference or determining meaning from data after it has been analysed or, in case of an experiment, after its completion.

Analysis brings out;

i)Specific values eg averages, dispersions, proportions, etc

ii)Boundaries of observation e.g. best and worst, highest and lowest.

iii)Trends over time

iv)Establishes distributions

Interpretation is about giving meaning to these research findings visa viz the objectives of the study.

a)Interpretation may establish, associate or link the findings of the study with already existing theories or with other research work.

b)It may generate new explanatory concepts or hypothesis.

Reasons for Interpretation.

i)To provide answers to the research questions or to confirm the research hypothesis.

ii)To provide a better understanding of why one’s research is the way it is especially in circumstances where your findings defy common knowledge or precedence.

iii)To provide comparisons with or extensions to already existing theories about the subject and the study.

iv)To pose a number of questions about areas which are not fully explained by the findings for purposes of further inquiry?In this way, you provide continuity in research.

v)To generate new insight in to the research problem for further empirical testing or to devote a quest on new discoveries through further study.

vi)To provide for a transition from exploratory research with an hypothesis to experimental research (with testable hypotheses).

PROCESS OF DATA INTERPRETATION

Data interpretation involves.

a)Describing phenomena e.g. mean production time lost to H.I.V Aids and its consequence is 200 man hours per annual.

b)Comparing the phenomena e.g. women business net works are 38% more beneficial to their members than men net works.

c)Establishing relationships between phenomena e.g. Does planned on - job training presents result into higher motivation of the force?The research must provide well reasoned explanations of the relationship found.

d)Establish points of commonality between the phenomenon or trends of events by establishing conjunctions and intersections e.g. higher productivity is attained with liberal human resource policies.

e)To establish issues of interdependence of a phenomena e.g. event X will not take place unless eventY is present or has been attained.

Use of extraneous information

Extraneous information is data collected or generated through analysis that is not directly responsible to the research questions.

This data must also be studied while interpreting findings because it may generate insight into or better under standing of the problem.It must also help in contextualising the findings ie putting the findings in perspective.

REVISION QUESTIONS

1. What does it mean if two variables have a positive correlation?

a)As one variable increases, so does the other

b)As one variable increases, the other decreases

c)The correlation between the two variables is 0

d)The correlation between the two variables is greater than 1.0

2. (a) Explain why tabulation is important in data processing and analysis.

(b) Explain why a researcher should never extend the recommendations of a research to a population from which the sample was not drawn unless it is very similar to the one from which the sample was drawn.

(c) Critically examine the following types of data analysis, pointing out the main  computations that are usually done.

 (i) Descriptive analysis

 (ii) Inferential analysis