Data Collection Exercise Overview


Adopt the sequential explanatory strategy. Collect qualitative data and analyze the consequent data. Collect qualitative data using cluster sampling method. To conduct cluster sampling you have to first identify the clusters to use of for the survey. After identifying the clusters, you can randomly select the clusters that you will concentrate your efforts on and then you can finally carry out your survey restricting yourself to these clusters (Mason, Lind & Marchal, 1999, 264). In data analysis, generate descriptive statistics on central tendencies using box plots. Parametric and non-parametric tests form part of the robust tests. Make it priority that qualitative data as a central guiding tool. This data orient the qualitative data collection. Then, Collect qualitative data and analyze the consequent data. Interpret data to form an inference about the study questions.

Model for Strategy
Figure Model for Strategy

Comprehensiveness of the Plan

Mixed method strategy is an amalgamation of both qualitative and quantitative research methods. In the planning of how to undertake data collection for a research study, Creswell (2009, Chapter 10) states that some three key parameters are a priority in the proposal development. The proposal will account for the preferred research strategy the study is to apply in guiding the data collection. A visual model provided in proposal illustrates how to go about the data collection procedures using the research strategy. The third imperative parameter is the procedures that will guide the data collection exercise.

According to Creswell (2009, Chapter 10), there are six prime strategies from which a procedures for data collection are influenced. This paper will incline on the explanatory strategy design in which quantitative research for data collection and analysis precede those qualitative ones. Then at a later data stage, interpretation for the pooled data follows. The other five strategies include sequential explanatory strategy, sequential transformative strategy, concurrent triangulation strategy, concurrent embedded strategy and concurrent transformative.

Research topic will initially rely heavily on the numerical on a regional data generated on the target population. Cluster sampling is preferred for sampling the population of this nature.

In a quantitative research method, data collection is through assessment surveys or using experimental designs to form an inference on how the study variable relate. Inferences formed from the data collected are requisite of the contents of the study conclusions and recommendations thereof thus, how robust the data collection procedures authoritatively dictates the quality of the study outcome. Consequently, a rigorous vetting precedes the choice of data collection procedure settled for. An anomaly in the design has detrimental effect on the study.

Cluster sampling is a cost efficient sampling technique used in surveys that cover a wide geographical area. To apply cluster sampling, the target population has to have clusters that are either naturally or artificially formed. Individuals in the target population must fall in one of these clusters. When doing cluster sampling you pick clusters and not individual members for the sample this because by picking clusters for the samples you guarantee total representation of all the members that exist in that cluster (Mason, Lind & Marchal, 1999, 264).

A qualitative approach is opted when the study features imply as naturally occurring. These study aspects have a social bearing. The research approach will in its methodology focus on what and/or why the study aspects mutually coexist within their immediate setting. The methodology of the study may have a humanistic inclination. Qualitative research strategy may contain composite methodology in its approaches mainly as a factor of the kind of study aspects considered. This can also be hinged on the fact that comparatively (to other research strategy generics) qualitative approach applies research questions with emergent design and open-ended on the concept and phenomena being studied. In addition, as the research persists the stated questions are subject to reconstitution with reference to the direction of the developments of the study. In the qualitative study approach, data collection is either by making direct observations of the study phenomena; fielding questions to respondents through interviews; screening documents such as literature materials, communication memoirs.

Steps for data analysis

The mixed method research strategy suggests a number of data analysis and validation procedures. These as given by Creswell (2010, Chapter 10) are data transformation, exploring outliers, instrument development, examining multiple levels and creating a matrix. Data transformation procedure involves either the quantification of qualitative data or the qualification of quantitative data. Explore outliers is a sequential process that involves a quantitative analysis of the data to reveal outliers. This is followed by carrying out qualitative interviews on the outliers. The instrument development procedure is sequential and involves carrying out a qualitative data collection exercise that results in themes and specific statements that are used later in a quantitative survey. Examining multiple levels procedure is a process that involves carrying out qualitative and quantitative procedures concurrently. Creating a matrix procedure involves combining qualitative data and quantitative data into a matrix.

This project uses the explore outliers data analysis and validation procedure. Below is a table that gives the steps used in analyzing the data collected for the project and how the steps apply to the project, research strategy and the data itself.

Table 1: Steps taken to analyze data

Step How the step applies to project, research strategy and data collected
Choose a data analysis approach to use in analyzing the data. For this case, an Exploratory Data Analysis (EDA) approach would be appropriate. The project at this point acquires a formal approach to use in analyzing data.
The research strategy at this point is quantitative in nature.
Data at this point forms the basis of our analysis.
Acquire appropriate software application to analyze data. Analysis software(s) give the project a fast, comprehensive and accurate analysis of the data.
The research strategy at this point is quantitative in nature.
Using analysis software means that the data as to be input into a computer that as the software installed in it.
Use software application to perform a box plot on the data At this point, the project utilizes the services of the software to prepare a box plot that identifies outliers.

Doing a box plot means that the research strategy at this point will be quantitative in nature.

A box plot means that data will have to be accessible to the software and well input in the software to get the desired results.

Prepare for interviews Planning for interviews is done at this stage of the project.
At this point the research strategy starts to be qualitative in nature.
Data at this point reveals the outliers who are the target of the interviews.
Conduct interviews At this point in the project, interviews are conducted on outliers.
The research strategy at this point will be purely qualitative in nature.
Data at this point reveals the outliers who are the target of the interviews.

The first step in the table involves making a choice of the data analysis approach to use in your project. An Exploratory Data Analysis (EDA) approach will be suitable for this project. Exploratory Data Analysis (EDA) is a data analysis approach that relies mostly in graphical techniques to analyze data. It is suitable for this project because it provides the right sequence of steps to help us analyze the data better. The first step of the sequence is to identify the problem the project seeks to study. The second step is to acquire the relevant data for the project in order to perform an analysis of it. The third step is to perform a graphic oriented analysis of the data. The fourth step is to model the data. The fifth and final step is to make conclusions based on the results of your analysis.

The second step in the table is acquiring a software application that will assist in the data analysis process. There are various statistical analysis software applications available in the market today; they include SPSS, EPI info, Minitab, STATA etc. SPSS will be appropriate for this project. These software applications give us a speedy, accurate and comprehensive analysis of the data.

The third step as given in the table 1 above is to use the software application to perform a box plot on the data. A box plot is a graphical technique used in descriptive statistics to give a five number summary of a set of numerical data. The five values are the minimum value, 25th percentile, 50th percentile, 75th percentile and the maximum value. The box plot is particularly a suitable graphical technique for identifying outliers in a set of data. It is due to this fact that it is a better technique to use in analyzing the data. Outliers are simply observations that lie an abnormal distance from the other values in a set of data.

The fourth step in as given in Table 1 above is preparing for interviews. This step will mark the beginning of the qualitative aspect of our research strategy. The aim of the analysis is to reveal outliers who are the target group for the interview. In order an interviewer to understand the presence of outliers in a set of data the interviewer as to prepare materially and psychologically for the interview. The interviewer must essentially seek to know from the responses given by the reason they have deviated from the rest.

The final step as given in Table 1 above is conducting interviews. Once the interviewer as prepared, the next step is to conduct the interviews. At this stage, it is important for the interviewer to stick to the topic. The interviewer achieves this through the material prepared during the interviewer’s preparation

Plan for qualitative Approach Study

Set the study boundaries on the extent of significance of concept or phenomena studied. Identify locality of the respondents or phenomena. Identify data types to base the data collection. Identify data sources and capturing techniques (Creswell, 2009, Chapter 9). From field notes: Observations from field note made by a participants; make personal observations; taking time with field participants and observers to populate field notes; observe outsiders and gather field notes on them. From Interviews (while taking notes or use audio tape for recording purposes): stage unstructured or open-ended interviews; stage semi-structured interviews while taping and stage focused group discussions to interview respondents. Other correspondence sources include email feedbacks and telephone interviews. From research documents: research journals; research diaries; personal accounts from respondents; source public reports and other documents to extract study material; screen classified sources such as videotapes, photographs, biographies, medical reports and representative charts. From audio-visual materials sources: identify evidence of the presence of materials for study; populate materials either from email, cell phone texts and sound information; screen cultural and ritual artifacts as well as use body senses of smell, taste, hear and touch to evaluate stimuli. Identify the data collection method. Data collection methods are observation, interview, documents or audio-visual materials. Collecting data through observation can be in complete participant (where researcher conceals role), participant as observer (where observation role comes second to participants role), observer as participant (where the role of researcher is known) and complete observer (where researcher observers and does not participate). Interviews can be face –to- face (persons interviewing another person), phone interviews (interview are via phone), focus group where the participants are in interviewed a group, email internet interview (interview is via Email or the internet). Collecting data though documents involves using public documents (e.g. minutes, newspapers) and/or private documents (e.g. diaries, journals). Data collection through audio- visual materials involves the use of photographs, videos, computer software and films. Identify data recording procedure: gather field notes based on observation; field interview questions mainly open-ended; and review audiovisual materials and documents. Undertake the data collection.


Creswell, J.W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). University of Nebraska, Lincoln: SAGE Publications.

Mason, M. D., Lind, D.A. & Marchal, W.G. (1999). Statistical techniques in business and economics, 10th edition. United States of America: The McGraw-Hill Companies Inc.