Qualitative Methodologies: Qualitative Data Analysis

for Tourism, Hospitality and Events

Qualitative Methodologies: Qualitative Data Analysis

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Date of Submission:

TLH224 Research Methods for Tourism, Hospitality and Events

University of Sunderland

Table of Contents

Introduction 1

1. Data Collection Techniques 1

2. Ethnography 2

3. Data for Qualitative Research 4

4. Data Analysis Process5

5. Building Ideas and Developing Theory 9

6. Narratives, Plots and Characters: Stories We Weave 9

Conclusion 10

References 11

Tourism is the world's largest industry with revenues of over $500 billion

Introduction

Tourism is the world's largest industry with revenues of over $500 billion. This comprises operation of hotels, motels, resorts, guesthouses, rest houses, picnic and recreation spots etc. while industrialists, businessmen, professionals, working people and of course tourists are the principal customers. In fact in many countries, hospitality industry is the principal source of foreign exchange earnings.

The object of this assignment is to understand how to conduct qualitative research method in a research. Qualitative methods are methods that do not involve measurement or statistics in research methodology. Qualitative research is characterized by its aims, which relate to understanding some aspect of social life, and its methods which (in general) generate words, rather than numbers, as data for analysis (Patton and Cochran, 2002).Qualitative research involves the studied use and collection of a variety of empirical materials - case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts - that describe routine and problematic moments and meanings in individuals' lives (Christina, H, 2014).The another objective of the report is that what is happening after data collection and before final writing.

Qualitative Data

Qualitative data are forms of information gathered in a nonnumeric form. Common examples of such data are:

Interview Field notes Video Audio recordings Images Documents (reports, e-mails, etc)

Such data usually involve people and their activities, signs, symbols and other objects they fill with meaning. The most common forms of qualitative data are what people have said or done.

Data Collection:

The process of data collection is to collecting appropriate data about the research from particular population. There are various way of data collections method such as interviews, questionnaires, group interviews or conference and observation. Each of the individual’s methods has its own and sole features; some advantages and disadvantages. The advantage of questionnaire method is cost effectual, simple association and straightforward analysis predominantly in a quantitative research

Ethnography

Ethnography is a social science research method. It relies heavily on up-close, personal experience and possible participation, not just observation, by researchers trained in the art of ethnography. These ethnographers often work in multidisciplinary teams. The ethnographic focal point may include intensive language and culture learning, intensive study of a single field or domain, and a blend of historical, observational, and interview methods. Typical ethnographic research employs three kinds of data collection: interviews, observation, and documents. This in turn produces three kinds of data: quotations, descriptions, and excerpts of documents, resulting in one product: narrative description. This narrative often includes charts, diagrams and additional artifacts that help to tell "the story" (Hammersley, 1990). Ethnographic methods can give shape to new constructs or paradigms, and new variables, for further empirical testing in the field or through traditional, quantitative social science methods.

Ethnography has it roots planted in the fields of anthropology and sociology. Present-day practitioners conduct ethnographies in organizations and communities of all kinds. Ethnographers study schooling, public health, rural and urban development, consumers and consumer goods, any human arena. While particularly suited to exploratory research, ethnography draws on a wide range of both qualitative and quantitative methodologies, moving from "learning" to "testing" (Agar, 1996) while research problems, perspectives, and theories emerge and shift.

Ethnographic methods are a means of tapping local points of view, households and community "funds of knowledge" (Moll & Greenberg, 1990), a means of identifying significant categories of human experience up close and personal. Ethnography enhances and widens top down views and enriches the inquiry process, taps both bottom-up insights and perspectives of powerful policy-makers "at the top," and generates new analytic insights by engaging in interactive, team exploration of often subtle arenas of human difference and similarity. Through such findings ethnographers may inform others of their findings with an attempt to derive, for example, policy decisions or instructional innovations from such an analysis.

Qualitative data analysis

Qualitative data analysis is the range of processes and procedures whereby we move from the qualitative data that have been collected into some form of explanation, understanding or interpretation of the people and situations we are investigating. Qualitative data analysis is usually based on an interpretative philosophy. The idea is to examine the meaningful and symbolic content of qualitative data. For example, by analyzing interview data the researcher may be attempting to identify any or all of:

Someone's interpretation of the world, Why they have that point of view, How they came to that view, What they have been doing, How they conveyed their view of their situation, How they identify or classify themselves and others in what they say,

The process of qualitative data analysis usually involves two things, writing and the identification of themes. Writing of some kind is found in almost all forms of Qualitative Data Analysis. In contrast, some approaches, such as discourse analysis or conversation analysis may not require the identification of themes. Nevertheless finding themes is part of the overwhelming majority of qualitative data analysis carried out today.

4. Data Analysis Process

Data analysis process in qualitative research methods in executed by following several steps. The

effectiveness of the data analysis is important to ensure the findings of the study. The important

steps are explained following.

Step 1: Transcribing

Transliterate (foreign characters) or write or type out (shorthand, notes, or other abbreviated forms) into

ordinary characters or full sentences:

To transcribe means to make a type written copy or to transfer form one storing and recording system to another. You can transcribe an interview or notes from a meeting.

Transcribing is the major step for data analysing and it can help to shift the collected data into

research format. Researcher gathers data from interview and participant observation and

transcribing helps o create complete interpretation for the data. The aim of transcribing is to put

the notes and tapes into a presentable formant in the research paper about tourism and

hospitality. The readable for making of the data is the major purpose of transcribing the data.

Analysing data through transcribing can be very much time consuming because it takes time to

ensure proper data transcribing (Veal, 2011). If the quality of the audio recording and image is

not god then it can create trouble for transcribing the data. Only relevant data needs to be

described in the transcribing step that is relevant to the aim and objectives of the research of

tourism and hospitality industry. The researcher needs to record his own actions and describe

into the research and methodology tasks. The researcher needs to be sincere about how to

produce the data that his collected from his won actions.

Style of Translation: there are some styles of translation of the data leaving plenty of room in the

margins so that the readable format of the data can be clear and transparent for the readers. Using

the dialogues of the respondents is necessary so that the findings of the findings of the study can

be effective (Blumberg et al, 2014). Clear identification of the every passage of the data

transcribing is also useful so that readers can find them easily. Numbering the line is also

important to ensure clear transcribing of the data and put the data in safe place so that it cannot

be lost.

Coding into themesLooking for themes involves coding. This is the identification of passages of text (or other meaningful phenomena, such as parts of images) and applying labels to them that indicate they are examples of some thematic idea. At its simplest, this labelling or coding process enables researchers quickly to retrieve and collect together all the text and other data that they have associated with some thematic idea so that they can be examined together and different cases can be compared in that respect.

CodingCoding is the process of combing the data for themes, ideas and categories and then marking similar passages of text with a code label so that they can easily be retrieved at a later stage for further comparison and analysis. Coding the data makes it easier to search the data, to make comparisons and to identify any patterns that require further investigation.Codes can be based on: Themes, Topics

Ideas, Concepts

Terms, Phrases

Keywords

found in the data. Usually it is passages of text that are coded but it can be sections of an audio or video recording or parts of images. All passages and chunks that are coded the same way – that is given the same label – have been judged (by the researcher) to be about the same topic, theme, concept etc.The codes are given meaningful names that gives an indication of the idea or concept that underpins the theme or category. Any parts of the data that relate to a code topic are coded with the appropriate label. This process of coding (associating labels with the text, images etc) involves close reading of the text (or close inspection of the video or images). If a theme is identified from the data that does not quite fit the codes already existing then a new code is created.As the researcher reads through their data set the number of codes they have will evolve and grow as more topics or themes become apparent. The list of codes thus will help to identify the issues contained in the data set.