Paper-Qualitative Data Analysis Section

Paper-Qualitative Data Analysis Section



Paper-Qualitative Data Analysis Section

Narrative Summary Analysis

Unlike the quantitative data analysis that deals predominantly with numbers, the qualitative approach uses observations and texts to come up with findings. Analysis of data qualitatively requires a logical and resourceful approach in order to attain correct and useful information (Gibbs, 2002). Various methods are relevant in this approach of data analysis. However, this is dependent on certain influential factors such as the availability of needed resources, the objectives of the study as well as the requirements of those who will use the findings of the study. One such method is the narrative summary analysis. This method is also known as content analysis. This method is relevant in analyzing data obtained from interviews and observations from either a number of people or one person (Grbich, 2007).

 The first step in analysis of data by use of this method is to understand the data. One needs to go through the data obtained in order to identify any bias information in it. In the context of online learning in North Carolina, the analyst of the raw data ought to scrutinize the information obtained from the study before starting the analysis process. Moreover, the analyst must keep in mind the reasons for the data analysis and the people who will benefit from these findings. In this case, the main beneficiaries of the data analysis are proprietors of these online learning sites. Analysis of this data will aid in solving the problem of low retention rates of students from online education centers in North Carolina (Yanow, 2002). Resolving this problem will result in increased income for these managers of these sites.

The analysis should address the objectives of the study as well as the information provided by each student. In terms of the study’s goals, the analyst should analyzer needs to individual responses in the questionnaires. The data depends on the questions with close examination of responses from all respondents. This is crucial in identifying the differences and similarities of individual responses to certain questions. By use of this technique, one is able to identify the major issues causing the increased dropout rate of students from online education. Moreover, one should consider analysis of the raw data from a group perspective (O’Regan, 2001). This entails the focus of the analysis towards grouping the responses according to the group of students with similar characteristics. This could be in terms of gender or age.

The students could have different reasons for dropping out of online education and this could vary depending on the age of the respondent. Various age groups have different requirements in their education system and lack of these components may result to dropping out of these learning sites. Moreover, female and male students may raise different issues that lead to their dropping out of these educational centers. Categorizing their responses based on gender would aid the managers of these online institutions to develop strategies that will cater for both genders (Wells, 2011). Consequently, retention of online students as well as enrollment of new ones into these centers will provide additional income to their owners. Integration of these two aspects of concern will improve the analysis process.

The next step in the narrative summary analysis is the categorization of information. Under this stage, the analyst should classify the obtained information in categories that will aid in summarizing the raw data into useful content. Each category contains a tag. Moreover, the categories should be specific on its content. Moreover, the categories should include codes that will ease the organization process. Information included in these categories depends on the responses in the questionnaires as well as the observations. For example, in the questionnaire, a question such as, “why do you prefer face-to-face learning?” is relevant in creating a category named as ‘benefits of face-to-face learning’.

In categorizing of data, one can start with either the preconceived concepts or those that reappear in the collected statistics. In the use of preset categories, ideas from the desktop research play part in identifying similar responses from the data obtained in the field. On the other hand, categories based on emerging themes may include ideas that had not appeared in the literature review. Nevertheless, then best way of creating categories is the incorporation of these two approaches. This involves the use of the predetermined groupings in the beginning and adding other categories as they appear. The topics’ list is in categories to interpret the data with ease (Wells, 2011).

After categorization of this data, it is vital for the analyst to classify the occurring patterns and relationships between different categories as well as within the groups. This will aid to illustrate the similarities and differences of various agendas in the study that influence a certain relationship among the topics. Where one intends to review data related to one theme illustrates differences and similarities of the responses within one group, categorizing of data in these classes is vital. This will aid in summarizing the pattern in a certain category. For example, description of data categorized in ‘benefits of face-to-face learning’ will help the managers identify key issues that need addressing in order to attract more students to their online educational centers.

Moreover, the analyst should integrate bigger groups that consist of many subgroups. This entails use of the subgroups to analyze the larger clusters (Wells, 2011). This strategy aids in describing the relationship between several categories. The importance of a certain subject is dependent on the number of times it recurs in the study as well as the respondents who answer it in a certain way. Although this is not accurate in quantitative analysis, it is helpful in showing the existing patterns in qualitative studies. In certain cases, the occurrence of one theme in the analysis leads to that of another. For example, the respondents may associate their dropping out of online learning sites to lack of proper supervision from the tutors. As such, one may deduce that lack of proper supervision is one of the reasons why students do not prefer these online sites. However, the analyst should consider other factors that may influence such responses before making any assumptions. For example, some students may drop out of online schools due to peer influence as opposed to lack of proper supervision from the instructors.

Upon classifying of various relationships identified during the study, the analyst should interpret the obtained information in order to ease the understanding of the data by its beneficiaries (Wells, 2011). The topics identified in the analysis process help in explaining the findings. Proper presentation of the interpreted data attracts individuals who can aid in filling the gap identified in the study. For example, appropriate illustration of the problems leading to a high dropout rate of online students from the centers will aid the proprietors to identify strategies that can curb their predicament.  However, one should refrain from generalizing the findings or making unsupported assumptions. For instance, assuming that the main reason of the high rate of dropouts in North Carolina is peer pressure, with no supportive facts, is not right.

Role of Qualitative Data Analysis Software

This software eases the analysis process by researchers from all fields of expertise (Edhulund, 2011). Moreover, it ensures unity between the functions of the method used in the study as well as the software’s function. This results to efficiency in the analysis process. Additionally, the differences in the process are limited to the software’s design as opposed to issues related to the actual methodology used in the study. This software is also appropriate to use with a wide variety of data such as texts, pictures or recorded information. This makes the analysis process convenient for all data types (Edhulund, 2011). Moreover, it makes it possible to classify and evaluate various patterns occurring in the data. This makes it easy for the analyst to scrutinize developing issues throughout the analysis process.

In addition, this software eases the arrangement and access to qualitative information. Moreover, the analyst limits any errors that may arise by controlling the whole analysis process. The analyst is also able to direct the process in accordance to the way he intends to analyze and present his data. In addition, the software does not limit its user to a certain format. The program also brands any wording that has no code for easy classification of the information (Edhulund, 2011). This emphasize on the text identifies all crucial elements in it for easy analysis. Moreover, one is able to evaluate important topics present from the study. It also makes it possible to integrate diagrams in the interpretation of the findings.

The Qualitative Data Analysis Software is capable of performing various tasks that man cannot execute. To start with, the program ensures smoothness of the analysis process (Bezeley, 2013). This is due to its elasticity in performing various tasks. The transition from one section of the process to another contains no hitches. The management of the entire process is usually easier with the use of this software. It is not only convenient in running the analysis process but is also reliable hence quickening the process. In conclusion, this software is vital in the data analysis procedure. It makes the analysis process, which is very demanding, produce reliable findings within a short period (Bezeley, 2013).


Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with NVivo. London: SAGE Publications

Edhlund, B. M. (2011). Nvivo 9 essentials: Your guide to the world’s most powerful qualitative data analysis software. Stallarholmen, Sweden: Form & Kunskap AB.

Gibbs, G. (2002). Qualitative data analysis: Explorations with NVivo. Buckingham [Eng,: Open University.

Grbich, C. (2007). Qualitative data analysis: An introduction. London: SAGE Publications.

          O’Regan, P. (2001). Financial information analysis. Chichester [England: J. Wiley.

 Wells, K. (2011). Narrative inquiry. New York: Oxford University Press. Ltd.

Yanow, D. (2000). Conducting interpretive policy analysis. Thousand Oaks, Calif: Sage Publications.

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