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# Quantitative And Qualitative Data

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Quantitative data are measures of values or counts and are expressed as numbers (www.abs.gov.au). In other words, quantitative data are data about numeric variables (www.abs.gov.au). Four types of quantitative data are interval, nominal, ordinal and ratio. Firstly, interval scales are numeric scales in which we know not only the order, but also the exact differences between the values (www.mymarketresearchmethods.com). Other than that, interval data also sometimes called integer is measured along a scale in which each position is equidistant from one another (www.changingminds.org). For example agree, neutral and disagree. Next, nominal data is categories of an object that the researchers are measuring (Hazman, n.d.). The scale categories are merely labels and can only give the frequency. Nominal data are items which are differentiated by a simple naming system and the only thing a nominal scale does is to say that items being measured have something in common, although this may not be described (www.changingminds.org). Nominal items also may have numbers assigned to them. For example, male/female labels are categories to assign people (Hazman, n.d.).

After that, ordinal data not only categorizes variables in such a way as to denote differences among various categories, it also rank-orders categories in some meaningful way (Yarina, n.d.). It ranks order the categories from highest to lowest (Hazman, n.d.). For example, in the case of educational qualification, we can arrange from hight to low which is from Ph.D, masters, bachelors, diploma, and certificate (Hazman, n.d.). Lastly, ratio is the interval between values is not interpretable in an ordinal measure. In interval measurement the distance between attributes does have meaning (www.socialresearchmethods.net). It is overcome the disadvantages of the arbitrary origin point of the interval scale, in that, it has absolute zero point which is a meaningful measurement point (Yarina, n.d.). For example, when we measure temperature (in Celcius), the distance from 0-10 is same as distance from 10-20 (www.socialresearchmethods.net).

In data analysis, qualitative techniques are measures of ‘types’ and may be represented by a name, symbol, or a number code and in other words, it is qualitative data are data about categorical variables (www.abs.gov.au).Â There are four types of qualitative techniques which are interview, content analysis, observation and transcription. First of all, interview involves asking questions and getting answers from participants in a study (www.qualres.org). There are two types of interview which are structured and unstructured interview. Unstructured interviews is where the interviewer does not enter the interview setting with a planned sequence of questions to be asked of the respondent while structured interviews are conducted when it is known at the outset what information is needed (Yarina, n.d.). The interviewer also has a list of predetermined questions to be asked of the respondents either personally, through the telephone, or via the computer (Yarina, n.d). Personal interview is a communication between a face to face two way interviewer and the respondents (Kumar, 2014).

The purpose of conducting a personal interview survey is to explore the responses of the people to gather more and deeper information (www.explorable.com). Telephone interview is when the information is collected from the respondent by asking him questions on the phone (Kumar,2014). The advantages of this type of interview are discomfort of face to face is avoided, faster or Number of calls per day could be high and lower cost while the disadvantages are interview length must be limited, low response rate and no facial expressions (Yarina, n.d.) Next, observation is involves going into âthe fieldâ, watching what the people do and interpreting what one has seen (Yarina, n.d.) It requires thr researcher to be very observant and he can observe without getting involved by directly participating in an event (Hazman, n.d.). Observation is the act of recognizing and noting facts or occurrences and no questions are asked in data collection (Kumar, 2014). For example, observing in-store shopping behaviour of consumers via a camera. Participant of observation may in aspect complete participation, moderate participation and active participation (Yarina, n.d.).

Next is content analysis is the procedure for the categorization of verbal or behavioural data for the purpose of classification, summarization and tabulation and the content can be analyzed on two levels descriptive and interpretative (Nigata,2012). Content analysis involves coding and classifying data, also referred to as categorising and indexing and the aim of context analysis is to make sense of the data collected and to highlightÂ the important messages, features or findings (www.libweb.surrey.ac.uk). The purpose of all research is to discover some truth about the problem or phenomena under investigation and to arrive at this, the data must be categorized and compared to establish some characteristic of importance as stated in the research objectives (Hazman, n.d.). The first step in content analysis is to plan on how the content will be analysed to arrive at conclusions.

The second step is to examine the validity of the measure used and the third step is to state what specific technique will be used to test hypothesis and why (Hazman, n.d.) Lastly, transcription is part of the qualitative research activities designed to capture and unpack the complicatedness and meaning of naturally occurring phenomena and this information enables researchers to easily retrieve the data and allows for tidily organized data management (Widodo, n.d.). It was brought on by the advancement of audio recording technology and recording and transcription of interviews became a staple of qualitative research (Peter, 2011). Transcription of recorded talk is done to facilitate analysis, where the choice of one system over another, or of a level of detail within a system, depends on the researcher’s analytic perspective (Derek, 2006).

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