9 Important PhD in Tourism Research and Topics related to Research

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The article shows the detail information of how to understand Research while seeking for PhD in Tourism Research and its types and other topics:

Basic Statistics- Central Tendency & Measures of Dispersion- An understanding of PhD in Tourism Research

Statistics:

Statistics can be considered as numerical statements of fact which are highly convenient and meaningful forms of communication. It is derived from Latin word status- political state/government.

a. Central tendency (Mean, Median, Mode):

  • Mean: It is the average result of the population and depicts the overall analysis as a
    whole.

It can also be denoted as:   where  stands for Arithmetic Mean

  • Median: Median is the middle number in a sorted list of numbers. For odd numbers it is calculated as
    n+1/2 and for even numbers, average of n/2 and n+1/2.
  • Mode: The most frequent number—that is, the number that occurs the highest number of times in a given set of data is known as mode of the data.

Relation among Mean, Median and Mode:
Mode = 3Median * 2Mean

b. Measures of Dispersion:

A measure of statistical dispersion is a non-negative real number that is zero if all the data is same and increases as the data become more diverse.

  • Range: It is the interval between group numbers of data
  • Inter-quartile Range: Inter-Quartile Range is based upon middle 50% of the values in a distribution and not affected by extreme values. Half of the Inter-Quartile Range is called Quartile Deviation (Q.D.).

Thus, Q.D.  = (Q3 – Q1)/2 where Q3 is Upper Quartiles and Q1 is Lower Quartiles.

  • Standard Deviation & Variance: The mean deviations are squared. The Mean of these squared
    deviations is called Variance and positive square root of Variance is known as
    Standard Deviation.
  • Coefficient of Variance: It is not an absolute measure but is a relative measure of
    dispersion. It is expressed as percentage.

C.V.  = (Standard Deviation/Mean) *100

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Research and its Theory:

Research is kept on searching for work and explained as “creative and systematic work undertaken to increase the stock of knowledge and the use of this stock of knowledge to devise new applications.”

The relationship between theory and research as a dialectic whereby theory determines what data are to be collected and research findings provide challenges to accepted theories.

Types of Research:

  • Fundamental or basic research: Basic research is an investigation on basic principles and reasons for occurrence of a particular event or process or phenomenon.

  • Applied research: In an applied research one solves certain problems employing well known and accepted theories and principles. Most of the experimental research, case studies and inter-disciplinary research are essentially applied research.

  • Exploratory Research: Exploratory research might involve a literature search or conducting focus group interviews.

  • Descriptive research: The descriptive research is directed toward studying “what” and how many off this “what”. Thus, it is directed toward answering questions such as, “What is this?”.

  • Explanatory research: Its primary goal is to understand or to explain relationships.

  • Longitudinal Research: Research carried out longitudinally involves data collection at multiple points in time. It can be trend, Cohort and Panel study.

  • Cross-sectional Research: One-shot or cross-sectional studies are those in which data is gathered once, during a period of days, weeks or months.

  • Action research: Fact findings to improve the quality of action in the social world

Quantitative and Qualitative research

Quantitative Research:

Quantitative research, is defined as the systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical or computational techniques. Quantitative research gathers information from existing and potential customers using sampling methods and sending out online surveys, online polls, questionnaires etc., the results of which can be depicted in the form of numerical.

  • Survey Research: Survey Research is the most fundamental tool for all quantitative research methodologies and studies. Surveys used to ask questions to a sample of respondents, using various types such as such as online polls, online surveys, paper questionnaires etc.

  • Correlational Research: Correlation research is conducted to establish a relationship between two closely knit entities and how one impacts the other and what are the changes that are eventually observed.

  • Casual Comparative Research: This research method mainly depends on the factor of comparison. Also called the quasi-experimental research, this quantitative research method is used by researchers to draw conclusions about cause-effect equation between two or more variables, where one variable is dependent on the other independent variable.

  • Experimental Research: It is usually based on one or more theories. This theory has not been proved in the past and is merely a supposition. In an experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences.

 Qualitative Research:

Qualitative research is defined as a market research method that focuses on obtaining data through open-ended and conversational communication. The various methods are:

  • One-on-One Interview: Conducting in-depth interviews is one of the most common qualitative research methods. It is a personal interview that is carried out with one respondent at a time. This is purely a conversational method and invites opportunities to get details in depth from the respondent.
  • Focus groups: A focus group is also one of the commonly used qualitative research methods, used
    in data collection. A focus group usually includes a limited number of respondents (6-10) from within your target market. The main aim of the focus group is to find answers to the why what and how questions.
  • Ethnographic research: Ethnographic research is the most in-depth observational method that studies people in their naturally occurring environment. This method requires the researchers to adapt to the target audiences’ environments which could be anywhere from an organization to a city or any remote location.
  • Case study research: This type of research method is used within a number of areas like
    education, social sciences and similar. This method may look difficult to
    operate; however, it is one of the simplest ways of conducting research as it
    involves a deep dive and thorough understanding of the data collection methods
    and inferring the data.
  • Record keeping: This method makes use of the already existing reliable documents and similar sources of information as the data source. This data can be used in a new research. This is similar to going to a library. There one can go over books and other reference material to collect relevant data that can likely be used in the research.
  • Process of observation: Qualitative Observation is a process of research that uses subjective methodologies to gather systematic information or data. Since, the focus on qualitative observation is the research process of using subjective methodologies to gather information or data. The qualitative observation is primarily used to equate quality differences. Qualitative observation deals
    with the 5 major sensory organs and their functioning – sight, smell, touch, taste, and hearing.
  • Grounded Theory: It is to describe the essence of an activity of an explanation of phenomological event. Open and axial coding techniques to identify themes and build the theory.
  • Phenomology: When describing an event, activity. A combination of videos, places
  • Narrative: weaves together a sequence of events from one/two individuals’ interviews.

Steps of doing Research

Steps of Research:

Step 1: Identify the Problem:

The first step in the process is to identify a problem or develop a research question.

Step 2: Review the Literature

The researcher must learn more about the topic under investigation. To do this, the researcher must review the literature related to the research problem.

Step 3: Development of Working Hypothesis

In step 3 of the process, the researcher will make certain assumptions as per literature review to find out the scope of the study.

Step 4: Preparing the Research Design

It resembles the preparation of blue print of research.

Step 5: Data Collection

The actual study begins with the collection of data.

Step 6: Analysis of Data

The researcher has data to analyze so that the research question can be answered.

Step 7: Hypothesis Testing

The testing of the expected and observed outcome of hypothesis.

Step 8: Generalization and Interpretation

The researcher will interpret the result and conclude.

Step 9: Preparation of Report/Thesis:

In this, Scholars and researchers will prepare their full thesis/ report according to their need.

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Research Analytical Tools

Analysis tools:

  • Factor Analysis:

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved (underlying) variables.

  • Discriminant Analysis:

Discriminant Analysis is a statistical tool with an objective to assess the adequacy of a classification, given the group memberships; or to assign objects to one group among a number of groups. For any kind of Discriminant Analysis, some group assignments should be known beforehand.

  • Co joint Analysis:

Conjoint analysis is a statistical technique that helps in forming subsets of all the possible combinations of the features present in the target product. These features used determine the purchasing decision of the product. Conjoint analysis works on the belief that the relative values of the attributes when studied together are calculated in a better manner than in segregation.

  • Multiple Regression:

Multiple regression generally explains the relationship between multiple independent or predictor variables and one dependent or criterion variable.  A dependent variable is modeled as a function of several independent variables with corresponding coefficients, along with the constant term.  Multiple regression requires two or more predictor variables, and this is why it is called multiple regression.

Sampling methods-Probability and Non-probability sampling

 Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling.

  • Probability sampling: A theory of probability is used to
    filter individuals from a population and create samples in probability
    sampling. Participants of a sample are chosen random selection processes. Each
    member of the target audience has an equal opportunity to be a selected in the
    sample.

There are four main types of probability sampling-

  • Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.

  • Stratified random sampling: In the stratified random sampling method, a large population is divided into groups (strata) and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.

  • Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic and demographic segmentation parameters.

  • Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly and all the other elements are chosen using a fixed interval. This interval is calculated by dividing population size by the target sample size.
  • Non-probability sampling: Non-probability sampling is where the
    researcher’s knowledge and experience are used to create samples. Because of
    the involvement of the researcher, not all the members of a target population
    have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

  • Convenience Sampling: In convenience sampling, elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.

  • Consecutive Sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can chose a single element or a group of samples and conduct research consecutively over a significant time period and then perform the same process with other samples.

  • Quota Sampling: Using quota sampling, researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.

  • Snowball Sampling: Snowball sampling is conducted with target audiences which are difficult to contact and get information. It is popular in cases where the target audience for research is rare to put together.

  • Judgmental Sampling: Judgmental sampling is a non-probability sampling method where samples are created only on the basis of the researcher’s experience and skill.

Hypothesis testing- Parametric & Non-parametric tests

Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter.

-Null Hypothesis: A null hypothesis is a type of hypothesis used in statistics that proposes that no statistical significance exists in a set of given observations.

-Type 1 error: It refers to rejecting a valid null hypothesis.

-Type 2 error: It is accepting an invalid null hypothesis

-Four Steps of Hypothesis Testing

  • The first step is for the analyst to state the two hypotheses so that only one can be right.
  • The next step is to formulate an analysis plan, which outlines how the data will be evaluated.
  • The third step is to carry out the plan and physically analyze the sample data.
  • The fourth and final step is to analyze the results and either accept or reject the null hypothesis.

–The p-value is the probability that a given result (or a more significant result) would occur under the null hypothesis.

– If the p-value is less than the chosen significance then we say the null hypothesis is rejected at the chosen level of significance.

– If the p-value is not less than the chosen significance threshold then the evidence is insufficient to support a conclusion.

A parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one’s data are drawn, while a non-parametric test is one that makes no such assumptions.

Types of parametric tests

t-tests:

A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. It is of 3 types:

  • One sample t-test
  • Independent t-test
  • Paired t-test

ANOVA:

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the “variation” among and between groups) used to analyze, differences among group means in a sample. ANOVA was developed by statistician and evolutionary biologist Ronald Fisher. To conduct a test with three or more variables, one must use an analysis of variance.

Types of Non-parametric tests

Chi-square tests:

A chi-squared test, also written as χ2 test, is any statistical hypothesis test where the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true. Without other qualification, ‘chi-squared test’ often is used as short for Pearson’s chi-squared test.

Run Test:

Runs Test for Detecting Non-randomness. The runs test (Bradley, 1968) can be used to decide if a data set is from a random process.

Sign Test:

The sign test is a statistical method to test for consistent differences between pairs of observations, such as the weight of subjects before and after treatment.

Wald- Walfowitz Test:

The Wald–Wolfowitz runs test (or simply runs test), named after statisticians Abraham Wald and Jacob Wolfowitz is a non-parametric statistical test that checks a randomness hypothesis for a two-valued data sequence. More precisely, it can be used to test the hypothesis that the elements of the sequence are mutually independent.

Kursal Walis Test:

The Kruskal–Wallis test by ranks, Kruskal–Wallis H test or one-way ANOVA on ranks is a non-parametric method for testing whether samples originate from the same distribution. It is used for comparing two or more independent samples of equal or different sample sizes. It extends the Mann–Whitney U test, which is used for comparing only two groups.

Komogrov- Smirnov Test.

The Kolmogorov–Smirnov test (K–S test or KS test) is a nonparametric test of the equality of continuous (or discontinuous, see Section 2.2), one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two samples (two-sample K–S test). It is named after Andrey Kolmogorov and Nikolai Smirnov.

Discreet, Continuous, Normal and Sampling Distributions

Distributions- Discrete and Continuous:

  • Discreet: A discreet random variable takes on discrete values that can be counted and
    assume values from a distinct predetermined set. For ex: Binomial distribution,
    Poisson distribution
  • Continuous: The continuous distribution is useful because it represents variables that are
    evenly distributed over a given interval. For ex: Normal Distribution

Normal distribution, Sampling distribution.

  • The Normal Distribution has many uses in practical world as many experimental results often follow normal distribution. It can be represented as Bell-Shaped curve. The normal curve is symmetrical and defined by its mean and standard deviation. The number of standard deviations Z for an observation, which is distance between value x and mean is defined by:

Z = (x- μ )/ σ

Where x = value of the observation

            μ = the mean of the distribution

            σ = standard deviation of the distribution

  • The Sampling Distribution: In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic.
    In here, we take sample size n of the population P. So calculating Sample distribution would be:

Z = (x- μ )/ σ x where σ x = σ/(n)1/2

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