QUANTITATIVE ANd qualitative research METHODS 

Data collection methods

The general differences between quantitative data collection methods and qualitative data collection methods are summarized as below:

Some common forms of data collections methods under each approach are discussed below:

Quantitative Research Approach – Data CollectioN

In quantitative research approach, data collection relies heavily on random sampling and structured data collection methods. Each strategy of inquiry–true experiment, quasi-experiment or non-experiment employs several unique ways of data collection tools, some which are given in the following chart:

Qualitative Research Approach – Data Collection

In qualitative research approach, data collection is usually unstructured and data is collected for non-numerical analysis. Usually, the methods of data collection all the strategies of qualitative inquiry–ethnography, phenomenological, grounded theory, narrative and case studies-are similar. Also,  in qualitative research, multiple methods of data collection or collection of data from multiple sources is practiced. This is called triangulation, and is employed in order to collect data that provide sufficient data, provide more information on a phenomenon or enhance deeper analysis and understanding of a research study. Types of triangulation may include method triangulation, source triangulation, analysis triangulation and even theory triangulation (Denzin, 1978; Patton, 999). All forms of data gathering done in a research study form what is known as a bricolage. Main forms of data collection under each strategy are given in the following chart:

DATA analysis and interpretation

After identifying a research topic, doing a literature background research, establishing philosophical assumptions and focus problem, deciding on an appropriate research paradigm and methodology with specific purpose, designing a research plan and collecting sufficient data, the next step in the research process is data analysis and interpretation, which precedes reporting of research. Data analysis is, therefore, a process that involves examining, and molding collected data for interpretation to discover relevant information, draw or propose conclusions and support decision-making to solve a research problem. This involves interpreting data to answer research questions and making research findings be ready for dissemination.  Data analysis also serves as a reference for future data collection and other research activities. During data analysis (Bala, 2005):

• data collected is transformed into information and knowledge about a research performed
• relationships between variables are explored
• meanings are identified and information is interpreted.

Like other research methods, data analysis procedures in quantitative research approach are different from those in qualitative research approach. The general differences of data analysis procedures between these two approaches are summarized in the Chart (Bala, 2005) and a Table that follows:

Below, I briefly describe some specific methods of data analysis under each approach. 

Quantitative Research Approach – Data Analysis 

Statistical analysis is the usual method used in quantitative research approach. However, quantitative data can be analyzed in several ways. Data collected has a certain level of measurements which initially influences the analysis. The identification of a particular level of measurement is the usually the first step in quantitative data analysis. The four levels of measurements include (Yamashita & Espinosa, 2015):

1. nominal data: basic classification data; lack logical order - e.g. male or female
2. ordinal data: has logical order but lack constant differences between values – e.g. Pizza size (large, medium, small)
3. interval data: has logical order, is continuous, has standardized differences between values but lacks natural zero – e.g. Celsius degrees
4. ratio data: has logical order, is continuous, has standardized differences between values, and has a natural zero – e.g. height, weight, age, length

After identifying a level of measurement, the next step is now to use a specific analysis technique in analyzing data. There are several procedures that can be used to analyze data. Main ones include (Yamashita & Espinosa, 2015):

• Data tabulation ( e.g. frequency distributions & percent distributions)
• Data descriptives (e.g. Mean, medium, mode, minimum and maximum values, etc.)
• Data disaggregation (tabulation of data across multiple categories)
• Moderate and advanced analytical methods (regression, correlation, variance analysis)

The above methods can be used invariably by all different true experimental, quasi-experimental and non-experimental quantitative research strategies. 

​Quantitative Research Approach – Data Analysis

Textual data analysis in is the usual method used in qualitative research approach. This involves identifying patterns and themes in data collected and then examining and interpreting these patterns and themes to draw meaning and answer research questions. The five strategies of qualitative research mentioned–ethnography, phenomenological, grounded theory, narrative and case studies–employ different analytical procedures. However, preliminary and some general steps in data analysis are common to all. These include (Yamashita & Espinosa, 2015):

1. Immediate processing and recording of data (important information, date/time details, observations, etc)
2. Commencement of data analysis soon after collection
3. Reduction of data to meaningful information
4. “Identification of meaningful patterns and themes” via
   • Content analysis achieved by:
     • Coding the data for certain words or content
     • Identifying their patterns
     • Interpreting their meanings.
   • Thematic analysis  achieved by “grouping data into themes that answers research problem”
5. Display of data which include organizing data in forms of graphics, maps, tables, etc., to draw conclusions
6. “Drawing of conclusion and verification”

All the steps are should usually be accompanied by references to literature, triangulation, keeping the research purpose on focus and constant back-and-forth analysis between and among the steps.
In most cases, analysis in qualitative research commences with “emic” analysis (analysis based on what a researchers knows or brings to a research study) and ends with “etic” analysis (analysis to derive meaning based on negotiations between what a researchers knows and what others or participants know). This means that subjectivity, training and personal experiences of a researcher, as well as other factors may influence the research process. For this reason, there is a need for innovative ways of research approaches to allow objectivity and comfortability.

Some unique analysis procedures are briefly outlined under each strategy of qualitative research.

Ethnography Research
Ethnography theory research approach focuses analysis on processes, patterns and trends that occur in individuals or across various groups regarding their cultural behavior but keeping in view the purpose of a research  While achieving the steps a research analysis adopts an iterative process in which cultural ideas that arise during active involvement during the research study are used to write a document while bearing in mind the context, norms, frequency and other factors (Krueger, 1994).
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Phenomenological Research
Phenomenological theory research approach involves the process which allows the analysis method to follow the nature of data itself. This is called emergent strategy (Walters, 2015). Analysis focuses on the details that can be appreciated through an experience a subject lived. Some emergent strategies may include:

• Narratives or interviews from artistic depictions, photovoice, etc., while paying attention to aspects such as:
  • Objects involved
  • Activities and their results
  • Time
  • Descriptive elements
  • Physical surroundings,
  • Characters or their aspects such as relationships
  • Social interactions
  • Results of activities  

​Grounded Theory Research
Grounded theory research approach involves the process of moving in and out of data collection and analysis processes, a process called constant comparative analysis or ‘iteration’. After this process, a researcher begins to develop a theory regarding the questions and data collected (Strauss & Corbin, 1994). This process of theory generating is also based on theoretical sampling which may incorporate background research. The process of theory generation is proceeded in three levels of data coding:

• LEVEL 1 DATA CODING (OPEN CODING/ MEMOING/CATEGORY DEVELOPMENT)
  • Involves memoing– taking notes – giving names. A research may:
    • Look for language such as action verbs or gerunds and points of dissonance which may denote an important experience or past time
    • Employ biasness
    • Focus on obtaining content and platform
    • Use structured questions
    • Read background after interview
    • Collect and let data speaks then proceed to level
•  LEVEL 2 DATA CODING (AXIAL CODING/COLLAPSING/CATEGORIZING/RENAMING)
  • Involves renaming and forming categories
  • Ask semi-structured questions
• LEVEL 3 DATA CODING (SELECTIVE CODING/THEME & THEORY CONSTRUCTION)
  • Involves formation of themes and theories
  • Results in formation of grounded theory
• The process of coding is usually a cycle
  • Normally, different kinds (at least two) of interviews are used; first interview which may include L1 Coding; second interview which may include L2 Coding (using semi-structured questions “smart bombs”); third second interview which may include L3 Coding (using unstructured questions–smart bombs).

Narrative Research
Narrative theory research analysis approach involves shaping, transforming, and uncovering an experience (usually human experience) in a story format–understood through a stirring linguistic depiction. Therefore it is characterized by in-depth discourses and may involve subjective intuitions. Analysis process converts experiences into words in form of writing, vernal or other communicable representation. Narrative analysis is aimed to derive a theme in an account of one’s life.

Case Study Research
Narrative theory research analysis approach involves in-depth examination of a case under study. Analysis is usually unusual and concurrent with data collection. It includes an iterative process in which initial analysis of data shapes subsequent data collection and analysis. ‘The principle data analysis method for case studies is referred to as OTTR, which stands for “observe,” “think,” “test,” and “revise.”’ Texas State Auditor's Office, Methodology Manual, rev. 5/95 (2015). 

data Interpretation 

The usual step proceeding data analysis is interpretation. “Interpretation involves attaching meaning and significance to the analysis, explaining descriptive patterns, and looking for relationships and linkages among descriptive dimensions. Once these processes have been completed the researcher must report his or her interpretations and conclusions” (Krueger, 1994).