Qualitative and Quantitative Data Collection and Analysis Methods

Management Research Methods

This essay discusses the qualitative and quantitative data collection and analysis methods such as semi-structure interviews, questionnaire, ANOVA and content analysis.

Quantitative data collection and Analysis Methods

Data gathering Approaches

Likert-scale questionnaire

According to Brace (2008, p.73), ‘Likert scale’ is a psychometric response scale which is used in questionnaire to obtain participant response, degree or preference of agreement with statement or set of given statements. Moreover, both one-dimensional and non-comparative scaling are key characteristics of Likert scales techniques. The respondents are asked to scale their level of agreement with given statement in ordinal scale.

The commonly used Likert scale is 5-points scale ranging from ‘strongly disagree’ to ‘strongly agree’.  Each scale is assigned coding or numerical value starting from 1 and incremental for subsequent levels.  On the other hand, the weaknesses include acquiescence bias, central tendency bias, lack of reproducibility and social desirability bias (Barua, 2013). The diagram below enclosed sample Likert scale

sample Likert scale

Appropriateness and relevance of Likert scale

An ordinal psychometric scale, ‘Likert scale’ is useful to measure beliefs, attitude and opinions. The appropriateness is that it is universal method for survey collection which is easily understood. The response of participants is subject to computation and easily quantifiable. Likert scale asks the participants to mark degree of relevance to particular topic. The Likert scale is that it allows collecting data from large population sample (multi-dimensional continuum) (Remenyi, 2012, p.115).

The Likert scale is easy to code, inexpensive and efficient method of data collection. The relevance of Likert scale is that they are useful to measure the latent constructs such as a characteristic of people. The latent construct is considered as unobservable individual characteristics and thus, believe to cause variation in behaviours. The summing together of a number of variables allows creating interval-level variables. In addition, In Likert scale, it is unlikely participants response influenced by previous questions and people avoid extreme options on the scale (Farrell, Sherratt and Richardson, 2016).

Example Published article using Likert scale

The article ‘An Empirical Study of Factors Affecting the Internet Banking Adoption among Malaysian Consumers?’ by Suki (2015) used the questionnaire approach to understand the people attitude and belief in terms of adoption of internet banking among the Malaysian consumers (Suki, 2015).

Suki, N.M. (2015) ‘An Empirical Study of Factors Affecting the Internet Banking Adoption among Malaysian Consumers?’, The Journal of Internet Banking and Commerce, vol. 2, pp. 121-145.

The study consists of a sample of 100 respondents and based on seven-scale Likert scale ranging 1 to strongly agree and 7 strongly disagree. The questionnaire was designed based on two sections. First, section of questionnaire enclosed the demographic of respondents and second level to identify the customer satisfaction and perception of internet banking. The data analysis was conducted using the multiple regression analysis to confirm the hypothesis. Likert scale has enabled the researcher to gather data on latent constructs from larger population and confirm the hypothesis to explain the phenomenon (Remenyi, 2012).

Quantitative Data Analysis

ANOVA (Analysis of Variance)

According to Cardinal and Aitken (2013), ‘Analysis of variance (ANOVA)’ is used for testing the hypothesis when there is no difference between the means of two or more population. ANOVA is used for testing the hypothesis when is no difference in terms/number of treatments. In confirming the hypothesis, null hypothesis represents all means are equal whereas alternative hypothesis highlight at least one mean is different ANOVA framework is useful to study the effect of one or more than one qualitative variables on outcome of quantitative variable. ANOVA uses the F-test to study the standard effect, interactions and main effects. Moreover, Abu-Bader (2011) added that the two types of testing include one-way and two-way ANOVA.

The one-way ANOVA is useful when there is only one independent variable and it is useful to measure means of 2 or more groups. On the other hand, two-way ANOVA includes two factors. In two-way ANOVA the two different explanatory variables enclosed the effect of outcome on change in such context that one variable may or may not depend on other variable (additive model) or it may depend on other variable (interaction model).

Appropriateness and relevance ANOVA

Urdan (2011, p.131) explained that the appropriateness of ANOVA is that where t-test can be used to compare the two means, but t-test raises the issue of type-I error. Therefore, ANOVA is used to study the difference among the multiple means without problem of type-I errors. With increase in number of groups, the number pair comparison increases substantially and thus, calculations become complex. In such case, there is need to examine p-values are insignificant.

Therefore, the relevance ANOVA is that it allows overcoming such problem, ANOVA puts data in F number and resulting in P value to test the null hypothesis. The usefulness of ANOVA is that it offers robust design and increases the statistical power. On the other hand, two-way ANOVA examines the interaction between the factors, reduces the random variability and highlight the effect of second variable after controlling the other factors. Nevertheless, the problems associated with ANOVA, when null hypothesis is rejecting, there understands that one population group is different. However, in ANOVA it is difficult to determine which group is different (Rayner, 2017).

Example of Published article using ANOVA

The article ‘The financial characteristics of large and small firms before and after the 2008 stock market crash’ by Folkinshteyn and Meric (2014) uses the ANOVA analysis to stock market crash in the US (Folkinshteyn and Meric, 2014).

 Folkinshteyn, D. and Meric, G. (2014) ‘The financial characteristics of large and small firms before and after the 2008 stock market crash’, The International Journal of Business and Finance Research, vol. 8, no. 1, pp. 1-34.

The article uses data from COMPUSTAT database for number of firm from 2006-2010. The study examines multiple variables and sample of firms includes both small and range firms. Author used the ANOVA statistics to test the differences between the small and large firms and consequently expanding the results using the multivariate ANOVA. The difference between firm sizes enclosed the effect of outcome on change (additive model) (Urdan, 2011).

Qualitative and Mixed methods analysis

Data gathering

Semi-structured interviews

Interviews are useful to gather information on perceptions, opinions and attitudes of respondent based on present or past experiences or behaviours. Interviews are useful to gather background information as well as tap the expert knowledge. Interview is face-to-face interpersonal situation between the interviewee and interviewer for specific purpose. Interviews are placed on continuum of highly structured to unstructured interviews based on degree of control interviewer will have during discussions.

The objectives of interview include collecting information on existing social problems, create a source of knowledge, gain inner feeling of respondent, observe situation quickly and gain knowledge and understanding on new phenomena (Brinkmann, 2014). The diagram below enclosed the amount of control exercised and kind of interviews

kind of interviews - qualitative and quantitative data collection and analysis methods

Appropriateness and relevance of semi-structured interviews

According to Galletta (2013), the appropriateness of semi-structured interview is that it offers guidelines which topics and questions need to be covered during the interview. The interviewer has discretion on the areas, topics and questions covered during the interviews. The questions are standardised and interviewers used the correct material to understand the scenario. The usefulness of semi-structured interview is that it allows generating a large amount of detail and thoroughly understanding the problem in social context.

The semi-structure approach offers flexibility, the response is reliable and easy to analyse. On the other hand, the problems associated with semi-structured interviews are causes and effect cannot be inferred, biases of respondent, open-end questions are difficult to analyse and it is difficult to compare answers (Blandford, 2013).

Example of Published article using semi-structured interviews

The article ‘A holistic perspective on corporate sustainability drivers’ by Lozano (2015) uses the semi-structure interviews to explore the different CSR drivers.

Lozano, R. (2015) ‘A holistic perspective on corporate sustainability drivers’, Corporate Social Responsibility and Environmental Management, vol. 22, no. 1, pp. 32-44

In the articles, researccher used semi-strcutred interviews to understand the opinions and percption of corpoarte leaders on CSR drivers. There were total of 13 interviews conducted among the corporate top-level executives. The usefulness of interviews for this study is that it creates a source of knowledge, gain inner feeling of leadership, observe CSR situation quickly and gain knowledge and holistic perspective on CSR phenomena.

Data Analysis

Content Analysis

According to Neuendorf (2016), content analysis is useful to understand verbal, written and visual communication messages using systematic and objective mean of quantifying and describing phenomena. Content analysis is widely used data analysis technique in qualitative research. In content analysis, researcher interprets meaning of the content from the textual data (naturalistic paradigm).

Content analysis is used to determine the pattern, presence and relationship between certain concepts and words within text or set of text. The researcher analyse the presence, relationship and meanings of words and consequently make interference to messages enclosed in the text (Riff, Lacy and Fico, 2014).

Appropriateness and relevance – Content Analysis

Krippendorff (2012, pg.85) appraised that the appropriateness of content analysis is that it is useful to conduct document analysis and allows the researcher to test theoretical issues and develop an understanding of data. The content analysis offers the possibility of distilling words into fewer categories. The content analysis allows the researcher to make valid and replicate inferences from data to context with aim of providing knowledge, representation of fact and new insights into practical actions.

The relevance of content analysis is based on three principles which are objectivity, systematic approach and generalizability. The objectivity highlight that analysis is pursued on explicit rules and enable the researcher to obtain same results for different researchers. The generalizability shows result obtained are applicable to other situations (Stemler, 2015).

Example of Published article using Content Analysis

The article ‘Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement’ by Ashley and Tuten (2015) employed content analysis to examine the creative strategies used by two famous brands on social media.

Ashley, C. and Tuten, T. (2015) ‘Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement’, Psychology & Marketing, vol. 32, no. 1, pp. 15-27

The author conducted content analysis to gain information on the branding strategies efforts and outcomes for the company. The analysis is useful to study message strategies using the code sheet to understand the effectiveness of creative strategies and consumer engagement.

Data Access and ethics

In order to identify the factors which have a negative effect on motivation, the researcher is expected to face a number of problems in getting access to data on employee motivation. The two types of difficulties include endogenous and exogenous. The endogenous problems include poor communication structure, lack of comprehensive records and lack of autonomy could result in constraint the data collection. In exogenous context, cultural and environment of the organisation, low standards of social performance and organisation processes restrict the researcher to collect data from employees. The researcher may find it difficult to develop a rapport with employees (Brace, 2013).

In addition, the researcher faces challenges how to locate and chose the participants as well as how to convince the participants to speak on the factors of de-motivation. The demotivation issues are sensitive topic and participants concerns about information would remain confidential and thus, reluctant to discuss the issues. The employee of organisation is less willing to answer the problems; resource availability and confidential data in organisation restrict the data collection (Olsen, 2014).

Mixed Research Methods

According to Creswell (2013), ‘Mixed methods’ research includes characteristics of qualitative and quantitative research. The understanding and collaboration through mixed method unable the researcher to off-set the weakness of each approach and increase reliability and validity of research findings. The benefits of applying mix methods are that it enclosed the strengths of both qualitative and quantitative research and thus, provide comprehensive understanding of the research problem.

On the other hand, the limitations of mixed methods are that design of research is complex and it takes more resources and time to plan and implement mixed research. The article Organizational culture and willingness to share knowledge: A competing values perspective in Australian context by Wiewiora et al (2013) used mixed method approach to examine the employee willingness to share knowledge and culture of organisation.

Wiewiora, A., Trigunarsyah, B., Murphy, G. and Coffey, V. (2013) ‘Organizational culture and willingness to share knowledge: A competing values perspective in Australian context’, International Journal of Project Management , vol. 31, no. 8, pp. 1163-1174


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