USE OF COMMUNICATION STRATEGIES BY TOURISM-ORIENTED EFL LEARNERS IN RELATION TO GENDER AND PERCEIVED LANGUAGE ABILITY Tao Zhao & Channarong Intaraprasert (CRITICAL REVIEW)

A. ARTICLE REVIEW

            I would like to review the article under the title “Use of Communication Strategies by Tourism-Oriented EFL Learners in Relation to Gender and Perceived Language Ability”. The researchers of this article are Tao Zhao & Channarong Intaraprasert. They come from School of Foreign Languages, Institute of Social Technology, Suranaree University of Technology, Thailand. This article was published in English Language Teaching; Vol. 6, No. 7; 2013 ISSN 1916-4742 E-ISSN 1916-4750 Published by Canadian Center of Science and Education with 4 H index.

            The introduction is begun with the explanation from the researchers about the English development in China. In China, English has nowadays been increasingly learned and used as a foreign language with its rapid development of economy, which also facilitates the development of the tourism industry because Chinese governments are encouraging their citizens to learn English, parents are persuading, even forcing, their children to speak it and college students are doing English at the expense of their majors (Jiang, 2003). According to China Daily (2004), the World Tourism Organization (WTO) predicted that China would be the world's largest tourist destination by the year 2020 and English would play a more important role in the development of the world's tourism industry.

            The tourism-oriented EFL learners in colleges or universities are expected to have better English language proficiency, especially their oral communication competence for the future career. However, compared with the EFL learners in the developed areas of China, the EFL learners in underdeveloped or developing areas find it difficult to orally communicate due to lack of communicative opportunities and communication strategies while communicating with people (Zhang, 2007).

            The researchers proposed two research problems, they are:

1.      What is the overall frequency of each type of communication strategies employed by Chinese tourism-oriented EFL learners in relation to gender and perceived language ability?

2.      Does the employment of strategies for coping with oral communication problems vary significantly according to the gender of students and their perceived language ability? If it does, what are they?

The research design used was experimental research.  The participants were 814 tourism-oriented EFL learners (261 males and 553 females) were purposively selected from 6 universities (2 universities in Yunnan Province, 2 universities in Guizhou Province and 2 in Guangxi Province),

The instrument used was only communication strategy questionnaire (CSQ), that contained 20 items of strategies for coping with communication problems (CCP), 10 items of strategies for understanding interlocutor’s messages (UIM), and 5 items of strategies for carrying on the conversation as intended (CCI).

 The research was conducted during November and December, 2012, the researcher went to six universities in the Southwest China in person to collect the data responded to the CSQ, to which 814 university tourism-oriented EFL learners gave their responses and in analyzing the data the researchers used  Analysis of Variance (ANOVA),  Post-hoc Scheffe Test, Chi-square Test.

The result of the use of CSs and students’ gender, the other two individual items of UIM category was very strong. It suggested that females were more interested in interaction, and more cooperative to make themselves understood, obviously female students were active in CSs use and showed significantly higher frequency of individual CSs use and language strategy use is a gender-related issue. If females were more active, positive and skillful in using certain strategies to learn a language, then males may need more help in developing such strategies for communication. Meanwhile, in the use of CSs and perceived language ability there is no significant differences between CSs and CCP category and the significant differences of CS employment were found in the UIM, CCI categories and individual items of CS employment in relation to student’s perceived language ability.

The conclusion is in terms of gender, the results revealed that females were more interested in interaction and more cooperative to make them understood, while males had greater confidence, were more risk-taking, and more enjoyed doing the speaking activity than female students. There is no significant difference according to three aspects of the investigation related to gender and perceived language ability: the overall CS use, the CS category, and the individual CSs. However, interestingly, the findings showed that there were significant differences in both the CS category and the individual CSs.

B. CRITICS AND SUGGESTION

            I begin to criticize the introduction, in the introduction the researchers did not explain the reason they chose gender to be searched. The researchers only explained the need and importance of English in China and the development of tourism in China nowadays. The researchers explained the definition of communication strategies much more than the background.

            The researcher did not mention the significant of the study, they only mentioned the research problem and objective of research. The most important part of the research was the significant of the study, it should be clearly explained both theoretically and practically.

            The participants in the research were chosen from six universities in three provinces that were part of developing areas in China. It is representative enough but unfortunately the researchers chose 553 females and 261 males. The ratio of female and male were quite unfair, it will affect the result of statistical calculation that females tend to be more dominant in the communication.

            The instrument of this research was only questionnaire meanwhile the researchers wanted to measure the effect of communication strategies related to gender and perceived language ability. The instrument was quite not proper because in questionnaire the participants could manipulate the data easily. The researchers should measure the students’ communication strategy by using oral interview related to Faerch and Kasper (1983) and Littlemore’s theories (2003). The aspect that should be measured in the communication strategies are achievement strategies, reduction strategies, substitution strategies and reconceptualization strategies. When the researchers thought that conducting oral interview spent very long time (553 females and 261 males), they could reduce the participants or asking the collaborator to conduct the oral interview. The other instrument that could be used was tape recorder, then the researchers could listen the data more than once to avoid the data bias.

            Faerch and Kasper (1983) divided the communication strategies into achievement strategies and reduction strategies. The achievement strategies applies inter language, cooperative attitudes, and non-verbal languages meanwhile in the reduction applies topic avoidance, message abandonment, and meaning replacement. Littlemore (2003) stated more completely about the aspect of communication strategies, they are substitution and reconceptualization strategies. Substitution strategies apply original analogical/metaphoric comparison, conventional analogical/metaphoric comparison, literal comparison, word transfer with L2 word approximation, super-ordinate, simple word transfer, and substitution plus strategies. Meanwhile reconceptualization strategies apply componential analysis, function, activity, place and emotion. Those strategies were quite suitable and more valid to be used as instrument in this research.

            It is still related with the instrument that the content of the questionnaire were not clear enough, e.g. Strategies to Cope with Communication Difficulties (CCP) no 2: “Using familiar words, phrases or sentences”. The researchers did not state clearly about familiar words, phrases or sentences. The familiar words, phrases and sentences must be based on the student’s vocabulary, background of knowledge, culture etc, therefore it is quite subjective. The researchers should give the example the kinds of familiar words, phrases or sentences to make the same understanding, comprehension between one and other participants.

            Another example of unclear instrument in the questionnaire was Strategies to Cope with Communication Difficulties (CCP) no 5 “Using simple expressions”. The researchers did not give the example of the simple expression. The participants who almost never practice English would regard that most of expression were not simple expression. The researchers should give the guidance the kinds of simple expression.

            The researchers also translated questionnaire into Chinese language to make the participants more understand and easy to fulfill the questionnaire. In my opinion, it is not necessary for the researchers to translate because they wanted to measure the participants’ communication strategy in English. It means that starting from the questionnaire must be written in English only.

            The researchers discussed the result clearly and gave a lot of theories in the discussion. Every result of the research was supported by theories. It makes the readers more understand comprehensively.

            Finally I suggest that it is better for the researchers to conduct the qualitative research to know absolutely about the communication strategies used by the participants, both females and males. That qualitative research will give more information of the kinds and types communication strategy used by participants rather than statistical calculation. Theoretically, the result of the qualitative research can give more information and theories of the way in teaching speaking especially communication strategies. Practically, for the teacher and students, they can also improve the way of teaching and learning English especially speaking and communication strategies.

REFERENCES

China Daily. (2004). China a Top Tourist Destination by 2020. May 13, 2004. Beijing: China Daily.

Faerch, C., & Kasper, G. (1983b). On Identifying Communication Strategies in Interlanguage

Production. in C. Faerch, & G. Kasper (Eds.), Strategies in Interlanguage Communication. New York: Longman.

Jiang, Y. (2003). English as a Chinese language. English Today

Littlemore, J. (2003). The Communicative Effectiveness of Different Types of Communication

Strategy. Retrieved November 25^th, from http://www.sciencedirect.com

Zhang, Y. (2007). Communication Strategies and Foreign Language Learning. US-China Foreign Language

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ETHNOGRAPHY AND GROUNDED THEORY

A. Ethnography Research

Ethnographic designs are qualitative research procedures for describing, analyzing, and interpreting a culture-sharing group’s shared patterns of behavior, beliefs, and language that develop over time. You conduct an ethnography when the study of a group provides understanding of a larger issue. You also conduct an ethnography when you have a culture-sharing group to study—one that has been together for some time and has developed shared values, beliefs, and language. You capture the “rules” of behavior, such as the informal relationships among teachers who congregate at favorite places to socialize (Pajak & Blasé, 1984).

1. Educational anthropologists focused on subculture groups such as:

a. Career and life histories or role analyses of individuals

b. Micro ethnographies of small work and leisure groups within classrooms or schools

c. Studies of single classrooms abstracted as small societies

d. Studies of school facilities or school districts that approach these units as discrete communities

2. Types of ethnography design:

a. Realist Ethnographies, is an objective account of the situation, typically written in the third-person point of view, reporting objectively on the information learned from participants at a field site.

b. Case Studies, is an in-depth exploration of a bounded system (e.g., activity, event, process, or individuals) based on extensive data collection.

c. Critical Ethnographies, are a type of ethnographic research in which the author is interested in advocating for the emancipation of groups marginalized in our society.

3. The characteristic of ethnography research:

a. Cultural theme is a general position, declared or implied, that is openly approved or promoted in a society or group.

b. Culture-sharing group in ethnography, is two or more individuals who have shared behaviors, beliefs, and language.

c. A shared pattern, is a common social interaction that stabilizes as tacit rules and expectations of the group.

d. Fieldwork, means that the researcher gathers data in the setting where the participants are located and where their shared patterns can be studied.

e. Description, Themes, and Interpretation.

- A description in ethnography is a detailed rendering of individuals and scenes to depict what is going on in the culture-sharing group.

- Thematic data analysis in ethnography consists of distilling how things work and naming the essential features in themes in the cultural setting.

- interpretation in ethnography, the ethnographer draws inferences and forms conclusions about what was learned.

f. Context or Setting, is the setting, situation, or environment that surrounds the cultural group being studied.

g. Researcher Reflexivity, refers to the researcher being aware of and openly discussing his or her role in the study in a way that honors and respects the site and participants.

B. Grounded Theory

A grounded theory design is a systematic, qualitative procedure used to generate a theory that explains, at a broad conceptual level, a process, an action, or an interaction about a substantive topic. In grounded theory research, this theory is a “process” theory—it explains an educational process of events, activities, actions, and interactions that occur over time. Grounded theory generates a theory when existing theories do not address your problem or the participants that you plan to study.

1. Types of Grounded Theory Design

a. The Systematic Design, emphasizes the use of data analysis steps of open, axial, and selective

coding, and the development of a logic paradigm or a visual picture of the theory generated.

In this definition, three phases of coding exist.

b. The Emerging Design

The more flexible, less prescribed form of grounded theory research as advanced by Glaser (1992) consists of several major ideas:

-          Grounded theory exists at the most abstract conceptual level rather than the least abstract level as found in visual data presentations such as a coding paradigm.

-     A theory is grounded in the data and it is not forced into categories.

-     A good grounded theory must meet four central criteria: fi t, work, relevance, and modify ability. By carefully inducing the theory from a substantive area, it will fit the realities in the eyes of participants, practitioners, and researchers. If a grounded theory works, it will explain the variations in behavior of participants. If it works, it has relevance. The theory should not be “written in stone” ( Glaser, 1992 , p. 15) and should be modified when new data are present.

c. The Constructivist Design

2. The Characteristics of Grounded Theory Research

a. Process Approach, is a sequence of actions and interactions among people and events pertaining to a topic.

b. Theoretical sampling, means that the researcher chooses forms of data collection that will yield text and images useful in generating a theory.

c. Constant comparison is an inductive (from specific to broad) data analysis procedure in grounded theory research of generating and connecting categories by comparing incidents in the data to other incidents, incidents to categories, and categories to other categories.

d. Theory Generation is an abstract explanation or understanding of a process about a substantive

topic grounded in the data.

e. Memos are notes the researcher writes throughout the research process to elaborate on ideas about the data and the coded categories. In memos, the researcher explores hunches, ideas, and thoughts, and then takes them apart, always searching for the broader explanations at work in the process.

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OTHER MULTIVARIATE DATA ANALYSIS: DISCRIMINANT ANALYSIS, CLUSTER ANALYSIS, OTHERS

Multivariate Data Analysis refers to any statistical technique used to analyze data that arises from more than one variable.

Canonical Correlation/Regression

It is also known as multiple multiple regression or multivariate multiple regression. All other multivariate techniques may be viewed as simplifications or special cases of this “fully multivariate general linear model.”

We have two sets of variables (set X and set Y). We wish to create a linear combination of the X variables (b1X1 + b2X2 + .... + bpXp), called a canonical variate, that is maximally correlated with a linear combination of the Y variables (a1Y1 + a2Y2 + .... + aqYq). The coefficients used to weight the X’s and the Y’s are chosen with one criterion, maximize the correlation between the two linear combinations.

Logistic Regression

Logistic regression is used to predict a categorical (usually dichotomous) variable from a set of predictor variables. With a categorical dependent variable, discriminant function analysis is usually employed if all of the predictors are continuous and nicely distributed; logit analysis is usually employed if all of the predictors are categorical; and logistic regression is often chosen if the predictor variables are a mix of continuous and categorical variables and/or if they are not nicely distributed (logistic regression makes no assumptions about the distributions of the predictor variables).

Principal Components and Factor Analysis

Here we start out with one set of variables. The variables are generally correlated with one another. We wish to reduce the (large) number of variables to a smaller number of components or capture most of the variance in the observed variables. Each factor (or component) is estimated as being a linear (weighted) combination of the observed variables. We could extract as many factors as there are variables, but generally most of them would contribute little, so we try to get a few factors that capture most of the variance. Our initial extraction generally includes the restriction that the factors be orthogonal, independent of one another.

Discriminant Function Analysis

It is to predict group membership from a set of two or more continuous variables. The analysis creates a set of discriminant functions (weighted combinations of the predictors) that will enable us to predict into which group a case falls, based on scores on the predictor variables (usually continuous, but could include dichotomous variables and dummy coded categorical predictors). The total possible number of discriminant functions is one less than the number of groups, or the number of predictor variables, whichever is less.

Multiple Analysis Of Variance, MANOVA

In MANOVA the Y’s are weighted to maximize the correlation between their linear combination and the X’s. A different linear combination (canonical variate) is formed for each effect (main effect or interaction—in fact, a different linear combination is formed for each treatment df—thus, if an independent variable consists of four groups, three df, there are three different linear combinations constructed to represent that effect, each orthogonal to the others). Standardized discriminant function coefficients (weights for predicting X from the Y’s) and loadings (for each linear combination of Y’s, the correlations between the linear combination and the Y’s themselves) may be used better to define the effects of the factors and their interactions. One may also do a “step down analysis” where one enters the Y’s in an a priori order of importance (or based solely on statistical criteria, as in stepwise multiple regression). At each step one evaluates the contribution of the newly added Y, above and beyond that of the Y’s already entered.

Cluster Analysis

In a cluster analysis the goal is to cluster cases (research units) into groups that share similar characteristics. Contrast this goal with the goal of principal components and factor analysis, where one groups variables into components or factors based on their having similar relationships with  latent variables.

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ONE-WAY ANOVA

Analysis of Variance (ANOVA) is a hypothesis-testing technique used to test the equality of two or more population (or treatment) means by examining the variances of samples that are taken. ANOVA allows one to determine whether the differences between the samples are simply due to random error (sampling errors) or whether there are systematic treatment effects that causes the mean in one group to differ from the mean in another.

Most of the time ANOVA is used to compare the equality of three or more means, however when the means from two samples are compared using ANOVA it is equivalent to using a t-test to compare the means of independent samples.

ANOVA is based on comparing the variance (or variation) between the data samples to variation within each particular sample. If the between variation is much larger than the within variation, the means of different samples will not be equal. If the between and within variations are approximately the same size, then there will be no significant difference between sample means.

Assumptions of ANOVA:

(i) All populations involved follow a normal distribution.

(ii) All populations have the same variance (or standard deviation).

(iii) The samples are randomly selected and independent of one another.

Since ANOVA assumes the populations involved follow a normal distribution, ANOVA falls into a category of hypothesis tests known as parametric tests. If the populations involved did not follow a normal distribution, an ANOVA test could not be used to examine the equality of the sample means. Instead, one would have to use a non-parametric test (or distribution-free test), which is a more general form of hypothesis testing that does not rely on distributional assumptions.

Whereas the t test is an appropriate test of the difference between the means of two groups at a time (e.g., boys and girls), ANOVA is the test for multiple group comparisons. Variance is an important statistical measure and is described as the mean of the squares of deviations taken from the mean of the given series of data. It is a frequently used measure of variation.  Its square root is known as standard deviation. Standard deviation = √ Variance.

ANOVA is essentially a procedure for testing the difference among different groups of data for homogeneity. The essence of ANOVA is that the total amount of variation in a set of data is broken down into two types:

-          The amount which can be attributed to chance.

-          The amount which can be attributed to specified causes.

ANOVA consists in splitting the variance for analytical purposes. Hence, it is a method of analyzing the variance to which a response is subject into its various components corresponding to various sources of variation. Through ANOVA technique one can, in general, investigate any number of factors which are hypothesized or said to influence the dependent variable.  One may as well investigate the differences among various categories within each of these factors which may have a large number of possible values.

In terms of variation within the given population, it is assumed that the values of (Xij) differ from the mean of this population only because of random effects i.e., there are influences on (Xij) which are unexplainable, whereas in examining differences between populations we assume that the difference between the mean of the jth population and the grand mean is attributable to what is called a ‘specific factor’ or what is technically described as treatment effect. In short, we have to make two estimates of population variance:

-          based on between samples variance

-          based on within samples variance.

The two estimates of population variance are compared with F-test,

This value of F is to be compared to the F-limit for given degrees of freedom.  If the F value we work out is equal or exceeds* the F-limit value, we may say that there are significant differences between the sample means.

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DESCRIPTIVE STATISTICS

Descriptive statistics, such as measures of central tendency and variability, help us to understand typical cases in a sample and the distribution of a variable more clearly. Measures of central tendency include the mode, the median, and the mean and these provide us with idea of what may be the typical/average data value in the data set. The mode should be used only for categorical data as it basically counts the frequencies. The median should be reported when an unusual data value is present in the data set. Otherwise, the mean should be reported as it possesses statistically preferable characteristics.

            Measures of variability include the range, the interquartile range, the variance, and the standard deviation and they provide us an idea of the accuracy of the measures of central tendency. The range should be used as a crude measure of variability as it is extremely sensitive to the presence of unusual data values. The interquartile range should be reported when unusual or outlying data value is present in the data set. Otherwise, the standard deviation should be reported as it possesses statistically preferable characteristics.

            A normal distribution is a very important probability distribution, which can represent many human characteristics, such as height, weight, and blood pressure. Skewness and kurtosis can be used to assess whether a variable is normally distributed; values should be between -1 and +1 standard deviations to be normal. It is important that variables of interest be normally distributed as most statistical analyses assume a normal distribution.

            When a variable is normally distributed, 68% of observations will fall within one standard deviation from the mean, 95% of observations will fall within two standard deviations from the mean, and 99.7% of observations will fall within three standard deviations from the mean. Any value that falls outside of the three standard deviation range can be treated as an unusual value for the data set.

            Z-score are a good example of how we can compute standardized scores to determine where any given score(s) fall in a normal distribution. We can use standardized scores to make comparisons between a single score, such as on a standardized test, with all scores.

            Instead of estimating an unknown population parameter with a single number or poit estimate, one can create an interval, called a confidence interval, as a different way of answering to the question, “How well does the sample statistic represent an unknown population parameter?” Confidence intervals are interpreted as the interval that will include the true parameter with a given confidence level, either 90%, 95%, or 99%. As the percentage of the confidence interval goes up (increased confidence that the mean falls within that range) the likelihood of confidence interval including a true population parameter increases.

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SURVEY STUDIES

Survey research designs are procedures in quantitative research in which investigators administer a survey to a sample or to the entire population of people to describe the attitudes, opinions, behaviors, or characteristics of the population. In this procedure, survey researchers collect quantitative, numbered data using questionnaires (e.g., mailed questionnaires) or interviews (e.g., one-on-one interviews) and statistically analyze the data to describe trends about responses to questions and to test research questions or hypotheses. They also interpret the meaning of the data by relating results of the statistical test back to past research studies.

Survey designs differ from experimental research in that they do not involve a treatment

given to participants by the researcher. Because survey researchers do not experimentally manipulate the conditions, they cannot explain cause and effect as well as experimental researchers can. Instead, survey studies describe trends in the data rather than offer rigorous explanations. Survey research has much in common with correlational designs. Survey researchers often correlate variables, but their focus is directed more toward learning about a population and less on relating variables or predicting outcomes, as is the focus in correlational research.

Types of Survey Design

A longitudinal survey design involves the survey procedure of collecting data about trends with the same population, changes in a cohort group or subpopulation, or changes in a panel group of the same individuals over time. Thus, in longitudinal designs, participants may be different or the same people. In a cross-sectional survey design, the researcher collects data at one point  in time.

Longitudinal

1.Trends Studies : Longitudinal survey designs that involve identifying a population and examining changes within that population overtime.

2.Cohort Studies : Longitudinal survey design in which a researcher identifies a subpopulation based on the specific characteristic and then studies that subpopulation over time.

3.Panel Studies : Longitudinal survey design in which the researcher examines the same people over time.

Sampling from a Population

1.      The Population is the group of individuals having one characteristic that distinguishes them from other groups.

2.      The Target Population or Sampling Frame is the actual list of sampling units from which the sample is selected.

3.      The Sample is the group of participants in a study selected from the target population from which the researcher generalizes to the target population.

Questionnaires and Interviews

A questionnaire is a form used in a survey design that participants in a study complete and return to the researcher. The participant chooses answers to questions and supplies basic personal or demographic information. An interview survey, is a form on which the researcher records answers supplied by the participant in the study.

In quantitative survey interviews, the investigator uses a structured or semi structured interview consisting of mostly closed-ended questions, provides response options to interviewees, and records their responses. In qualitative survey interviews, an interviewer asks open-ended questions without response options and listens to and records the comments of the interviewee.

Forms of Data Collection to Survey Research

1.      A mailed questionnaire is a form of data collection in survey research in which the investigator mails a questionnaire to members of the sample.

2.      A Web-based questionnaire is a survey instrument for collecting data that is available on the computer.

3.      One-on-one interviewing in survey research is a form of data collection in survey research in which the investigators conduct an interview with an individual in the sample and record responses to closed-ended questions.

4.      Focus group interviews in survey research is a form of data collection in survey research in which the researcher locates or develops a survey instrument, convenes a small group of people (typically a group of 4 to 6) who can answer the questions, and records their comments on the instrument.

5.      Telephone interview surveys is a form of data collection in survey research in which the researcher records the participants’ comments to questions on instruments over the telephone.

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SCOPE OF RESEARCH THEMES OF QUALITATIVE STUDIES IN ELT

1.      Qualitative research is a process of enquiry aimed at understanding human behavior by building complex, holistic pictures of the social and cultural settings in which such behavior occurs. It does so by analyzing words rather than numbers, and by reporting the detailed views of the people who have been studied.

2.      Qualitative research seeks to understand the what, how , when, and where of an event or an action in order to establish its meaning, concepts, and definitions, characteristics, metaphors, symbols and descriptions.

3.      The goal of qualitative research is the researchers try to understand a research object without making any theoretical prediction.

4.      The focus is not the condition or the results of a process, but the process itself, like how effective teachers behave differently from ineffective teachers, or how a writer become a skill writer, how students fail in their final exam.

5.      The research problems are usually made after the research has been started, when some collected data have been analyzed. The research problems in qualitative research are developing into more focused during the process of research.

6.      The data is collected by observing people when they are interacting in their natural setting.

7.      In qualitative research the data analysis does not use statistics that requires numerical data. Therefore the data are collected and recorded in description, not symbols or numbers.

8.      Qualitative data are analyzed through logical-inductive analysis, a process of grouping, regrouping, and matching data with research questions. The results are expressed as verbal statements.

9.      The sources of data are assumed to be homogeneous, having no variation. Therefore the trustworthy source of data does not  come from representation of different groups of the source, but selected based on certain criteria to find the most authoritative one. The source in qualitative research is usually called informants (of course when the source is human being)

10.  The source of data can be many different kinds used. When personality is involved as one of the variables, the data on personality are assessed from as many different sources as many different sources as possible; from their parents, from their neighborhood, from their diaries, etc.

11.  The researcher collects and analyzes data simultaneously to draw a temporary conclusion and repeats the cycles several times, deciding what data needs to be collected again to verify their temporary conclusion.

12.  The conclusions are inductively based on what people see as meaningful patterns, concepts, trends or categories in the course of their everyday life experience.

13.  Methods in Qualitative Research:

1.      Case study : one of the qualitative research methods used to study in-depth a unit of a person, a family, a social group, a social institution, or a community for the purpose of understanding the life cycle or an important part of the life cycle of the unit.

2.      Ethnographic

3.      Phenomenological

4.      Constructivists

5.      Participants observational

6.      Interpretive

7.      Naturalistic Enquiry

8.      Exploratory Descriptive

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