GENERAL CHARACTERISTICS CASE STUDY

When to use case study method?
1)    The type of research question: typically to answer questions like “how” or “why”
2)    Extent of control over behavioural events: when investigator has a little/no possibility to control the events
3)    General circumstances of the phenomenon to be studied: contemporary phenomenon in a real-life context

Case study is an empirical inquiry, in which:
-Focus is on a contemporary phenomenon within its real-life context & boundaries between phenomenon and its context are not clearly evident
Suitable for studying complex social phenomena
-Procedural characteristics in the situation include: Many variables of interest; multiple sources of evidence; theoretical propositions to guide the collection and analysis of data
-Types of case studies might be: explanatory; exploratory; descriptive
-Designs can be single- or multiple-case studies
-Used methods can be qualitative, quantitative, or both

Typical criticisms towards case studies & correcting answers:
-Lack of systematic handling of data -> Systematic reporting of all evidence
-No basis for scientific generalization -> Purpose is to generalize to theoretical propositions, not to population as in statistical research
-Take too long, end up with unreadable documents -> Time limits & writing formula depend on the choices of investigators.

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ONE SHOT CASE - STUDY

Campbell and Stanley classified this design as “pre-experimental.”  No variable is manipulated.  The researchers simply find some group of subjects who have experienced event X and then measure them on some criterion variable.  The researcher then tries to related X to O.  My kids were in the public schools here when a terrible tornado ripped through the county just south of our house.  After the tornado left, psycho-researchers descended on the schools, conducting research to determine the effects of the tornado on the children’s mental health.  Of course, they had no pretest data on these children.  Without a comparison group, observations like this are of little value.  One might suppose that there is an implicit comparison group, such as that provided by “norms” on the measuring instrument, but how do we know whether or not our subjects already differed from the “norms” prior to experiencing the X?

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ONE GROUP PRETEST - POSTTEST DESIGN

Campbell and Stanley called this a “pre-experimental” design, but I consider it to be experimental (since the X is experimentally manipulated), but with potentially serious problems which we have already discussed:  History, maturation, testing, instrumentation, and possibly regression.  If we have contrived ways to control these threats (which might be possible under if our subjects are inanimate objects whose environments we control completely, as we might imagine things are in the physics or chemistry laboratory), then this design could be OK.  Statistically, the comparison between means on O1 and O2 could be made with correlated samples t or a nonparametric equivalent.

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STATIC GROUP COMPARISON

We discussed this design earlier.  As noted by Campbell and Stanley, it is “pre-experimental” in that the researcher does not manipulate the X, but rather simply finds one group which has already experienced the X and compares that group to another group that has not experienced the X.   Independent samples t or a nonparametric equivalent could be employed to compare the two groups’ means.

PRETEST - POSTTEST CONTROL GROUP DESIGN

Here we have added a control group to the one-group pretest-posttest design.  If we can assume that both groups experienced the same history between observations (that is, there is no selection by history interaction), then history is controlled in the sense that it should affect the O1 to O2 difference identically in the two groups. Likewise, maturation, testing, instrumentation, and regression are controlled  in the sense of having the same effects in both groups.  Selection and selection by maturation interaction are controlled by assigning subjects to the two groups in a way (such as random assignment) that makes us confident that they were equivalent prior to experimental treatment (and will mature at equivalent rates).  Unless we are foolish enough to employ different measuring instruments for the two groups, selection by instrumentation interaction should not be a problem.  Of course, testing by treatment interaction is a threat to the external validity of this design.

Statistically, one can compare the two groups’ pretest means (independent t or nonparametric equivalent) to reassure oneself (hopefully) that the assignment technique did produce equivalent groups -- sometimes one gets an unpleasant surprise here.  For example, when I took experimental psychology at Elmira College, our professor divided us (randomly, he thought) by the first letter of our last name, putting those with letters in the first half of the alphabet into one group, the others in the other group.  Each subject was given a pretest of knowledge of ANOVA.  Then all were given a lesson on ANOVA.  Those in the one group were taught with one method, those in the other group by a different method.  Then we were tested again on ANOVA.  The professor was showing us how to analyze these data with a factorial ANOVA when I, to his great dismay, demonstrated to him that the two groups differed significantly on the pretest scores.  Why?  We can only speculate, but during class discussion we discovered that most of those in the one group had taken statistics more recently than those in the other group -- apparently at Elmira course registration requests were processed in alphabetical order, so those with names in the first half of the alphabet got to take the stats course earlier, while those who have suffered alphabetical discrimination all of their lives were closed out of it and had to wait until the next semester to take the stats course -- but having just finished it prior to starting the experimental class (which was taught only once a year), ANOVA was fresh in the minds of those of us at the end of the alphabet.
One can analyze data from this design with a factorial ANOVA (time being a within-subjects factor, group being a between-subjects factor), like my experimental professor did, in which case the primary interest is in the statistical interaction -- did the difference in groups change across time (after the treatment), or, from another perspective, was the change across time different in the two groups.  The interaction analysis is absolutely equivalent to the analysis that would be obtained were one simply to compute a difference score for each subject (posttest score minus pretest score) and then use an independent samples t to compare the two groups’ means on those difference scores.   An alternative analysis is a one-way Analysis of Covariance, employing the pretest scores as a covariate and the posttest scores as the criterion variable -- that is, do the groups differ on the posttest scores after we have removed from them any effect of the pretest scores.  All three of these analyses (factorial ANOVA, t on difference scores, ANCOV) should be more powerful than simply comparing the posttest means with t.

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POSTTEST ONLY CONTROL GROUP DESIGN

Here we simply assign subjects to groups in a way that should assure pretreatment equivalence, don’t bother with a pretest, administer the treatment to the one group, and then measure the criterion variable.  With respect to controlling the previously discussed threats to internal and external validity, this design is the strongest of all I have presented so far.  However, this design usually is less powerful than designs that include a pretest-posttest comparison.  That is, compared to designs that employ within-subjects comparisons, this design has a higher probability of a Type II error, failing to detect the effect of the treatment variable (failing to reject the null hypothesis of no effect) when that variable really does have an effect.  Accordingly, it is appropriate to refer to this threat to internal validity as statistical conclusion validity.  One can increase the statistical power of this design by converting extraneous variables to covariates or additional factors in a factorial ANOVA, as briefly discussed later in this document (and not-so-briefly discussed later in this course).  While it is theoretically possible to make another type of error that would threaten statistical conclusion validity, the Type I error, in which one concludes that the treatment has an effect when in fact it does not (a Type I error), it is my opinion that the Type II error is the error about which we should be more concerned, since it is much more likely to occur than a Type I error, given current conventions associated with conducting statistical analysis.

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THE NORMAL DISTRIBUTION

The most important continuous probability distribution in the field of statistics is the normal distribution :

1. empirical data is often normally, or approximately normally, distributed
2. the assumption of normality allows for the application of powerful statistical analyses
3. the distribution of many sample statistics tends to normality as the sample size increases (>30)
4. many population distributions can be readily transformed to normality

The properties of the normal distribution are :

1. observations tend to cluster at the mean:  mean = mode = median
2. the distribution of observations is symmetrical about the vertical axis through the mean
3. the total area under the curve is equal to unity, i.e. 1.0000
4. the normal curve continues to decrease in height as one proceeds in either direction away from the mean, but never reaches the horizontal axis, i.e. there is a presumption of negative and positive infinity
5. the area under the curve between two ordinates, X = a and X = b where a < b, represents the probability that X lies between a and b and can be expressed by the probability of a < X < b
6. when the variable X is expressed in standard units, z = (X - m)/@

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SAMPLING ERROR

One purpose of using random probability sampling methods is to apply a variety of statistical techniques to estimate the confidence one can have that the characteristics of a sample accurately reflect the study population.   Sampling error is a random product of sampling.   However, when random sampling methods are used it is possible to compute how much the sample-based estimate of the characteristic will vary from the study population by chance because of sampling.   The larger the sample size and the less variability of the characteristic being measured, the more accurate a sample-based estimate will be.   Sampling error can be defined as the variation around the true population value that results from random sample differences drawn from the population.

The standard error of mean is the most commonly used statistic to describe sampling error :
SE    =    [s2/n]0.5

Where:    s2 is the variance derived from the sample
        n  is the sample size
        and [sum]0.5 is the square root of the product

Alternatively, the standard error of mean is more easily computed from a proportion statement, since the variance of a proportion is expressed as p[1-p]:   the standard error of mean of a proportion is computed from: [p(1-p)/n]0.5.

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SAMPLE SIZE

How big should my sample be?   It all depends on what you want to do with your findings and the type of relationship you want to establish in your study.   The sample size is crucially important in a correlational research design, i.e. tests of hypotheses and significance, or establishing an association or relationship between two of more variables.   However, there is no relationship between the sample size and the size of the study population, sample size is determined by the variability of the factor, element or characteristic prevalent in the study population.   Other things being equal, precision increases steadily up to sample sizes of 50-200; after that, there is only a modest gain in precision to increasing sample size.   Whilst increasing sample size reduces errors attributable to sampling, managing large amounts of data may increase non-sampling errors (e.g. field-work problems, interviewer-induced bias, clerical errors in transcribing data, etc.).   In determining the size of a sample, consideration should be given to:

1. the degree of accuracy required in the estimation of the variables in the chosen study population
2. the level of confidence demanded from the sample to test the significance of the findings or hypotheses
3. the extent to which the variability of the factor in the chosen study population is known, or can be estimated

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SAMPLE DESIGN

The sample design influences the precision of the subset of measurements obtained from the sample.   Sampling strategies can be categorized as random probability, non-random probability and mixed sampling.   A random probability sample design provides for an equal and independent chance of selection, i.e. each individual has a known probability of selection set by the sampling procedure.   Non-random probability sample designs are used when the population size is either unknown or cannot be discretely identified.   A mixed sampling design has characteristics of both random and non-random probability sampling.   Systematic sampling is the most common strategy used, based on a known population with the sample size determined a priori.   The advantages of systematic sampling over simple random sampling is that it is more efficient, in terms of information per respondent cost, and easier to perform thus reducing sampling error.   Systematic sampling will generally be an adequate form of random sampling to the extent that the placing of any sampling subject or unit is independent of the placing of other sampling individuals or units, i.e. there is no systematic bias introduced into the listing of sampling units.   Should such a risk of sampling bias be known, it may be avoided by choosing a suitable sampling interval; or, after a predetermined number of units have been drawn, a fresh random start can be made.

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SAMPLE FRAME

A sample frame is a complete list of individuals or units in the population to be studied.   An initial step in sampling is to provide a clear and accurate definition of the population exposed to your study.   This study population may comprise of a group of individuals who go somewhere or do something that enables them to be sampled.  Sampling is carried out in two stages; the first involves sampling something other than the individuals to be finally selected, the second entails creating a list of sampling units (individuals) from which a final selection is made.   Whilst the sample frame should be representative of the study population, it may be necessary to control mediating or intervening variables.   For instance, suppose I wish to ascertain the attitude of workers, employed by the Kenkei Electronics Company, toward some object or subject.   A number of workers in the study population will have been recently employed by the company, and may retain beliefs, opinions and attitudes from previous employers.   To eliminate these from the study, the sampling frame may be constructed to include workers with at least one year’s service with the Kenkei Electronics Company.

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SAMPLING THEORY AND SAMPLING

Sampling theory is the study of relationships existing between a population and samples drawn from the population.   It is of value in estimating of unknown population parameters (such as population arithmetic mean, variance, etc.) from knowledge of corresponding sample statistics.   Sampling theory is also useful in determining whether observed differences between two samples are actually due to chance variation or whether they are really significant.   The answers involve use of so-called tests of significance and hypotheses.   In general, a study of inferences made concerning a population by use of samples drawn from it, together with indications of the accuracy of such inferences using probability theory, is called statistical inference.

A sample is a set of individuals or objects selected from a population.   The purpose of sampling is to infer a characteristic or characteristics of a given population from a subset of measurements obtaining from the sample.   In a quantitative research design, the sampling methods and the sample design are crucial to obtain unbiased and consistent estimates of the characteristic population inferred from the sample; and, enable one to statistically validate any conclusions based on the observations and findings.   In order that conclusions of sampling theory and statistical inference be valid, samples must be chosen so as to be representative of the population.   A study of methods of sampling and the related problems, which arise, is called the design of the experiment.   One way in which a representative sample may be obtained is by a process of random sampling, according to which each member of the population has an equal chance of being included in the sample.   One technique for obtaining a random sample is to assign a number to each member of the population, write each number on a separate piece of paper, place them in a container and then draw numbers from the container, being careful to mix thoroughly before each drawing.   Alternatively, this can be replaced by using a table of random numbers specially constructed for such purposes.    

How well a sample represents a given population depends on the sample frame, the sample size and the specific sample design:

1. Sample Frame: the set of subjects who have a chance of being selected from the study population, given the sampling approach chosen
2. Sample Design: the specific procedures to be used for selecting the subjects in the sample
3. Sample Size: the planning of, and reasons for choosing, the number of subjects in the sample.

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CASE STUDIES

The term ‘case study’ is often used quite loosely. They are a way of capturing concrete details of a real or fictional situation, and presenting these details in a structured and compact way. Case studies tell a story, and are often very lively and colourful ways of presenting your research, or to go about conducting research. 

Case studies are used widely in a whole range of disciplines, such as psychology, anthropology, sociology and criminology. Business analysts have used case studies for over 80 years to discuss particular problems with businesses and how they overcame them. (Business case studies were first developed by the Harvard Graduate School of Business Administration in the 1920s.)

Because case studies follow a structured format, different situations can be compared or analysed comparatively. Case studies are typically short (often no more than 5 pages long) and usually only contain the essential information needed to present a situation and, if necessary, to describe and properly analyse a problem. 

Case studies often contain both qualitative and quantitative data, adding to the richness and detail of the situation being described, and the problem being analysed.

There are many different structures to case studies, and you will need to decide on the most appropriate structure for what you are trying to convey. If you are doing more than one case study for the same research topic, it is important to ensure that the structure you use is consistent, so that your results can be compared.

Often case studies will contain:

-    The essential details of the organization or situation under question (such as name of organization, description of core activities, socio-economic background);
-    Some background information that has led up to the situation being presented;
-    A detailed description of the situation being analysed;
-    A description of the problems encountered;
-    An analysis of possible solutions (if a problem is being presented).
Advantages of case studies
-    Specific concrete example;
-    Can help with problem solving;
-    Are often interesting to read.
Disadvantages of case studies
-    Can take time to develop;
-    Depending on format, may need some level of good writing skills;
-    Do not usually give broad overview of issue at hand.
Research tip: Writing case studies
Charles Warner  has some useful tips for writing your own case studies. These can be summarized as follows:

-    Keep your audience in mind (you may be writing for someone who doesn’t know anything about the situation you are describing);
-    Keep jargon to a minimum (or at least explain jargon clearly);
-    Tell a story (make your characters and situations as real as possible);
-    Set the scene (make your opening interesting, set up the confrontations, frustrations and the conflicts that you will describe);
-    Don’t analyse as you tell the story. Simply present the scenes and situations and make sure that your story proceeds in a logical, step-by-step way (save the analysis for your part on problem solving);
-    Provide all the relevant details that are necessary to understand the situation and problem;
-    Use lots of dialogue (your characters need to come alive);
-    Leave the reader with a clear picture of the major problems at the end. The ending should leave you with the question: ’What is to be done now?’

Some of these tips may not be useful for your purposes. Often case studies are simply a way of capturing factual information in a compact and digestible manner. Decide what works for you, and use it.

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QUANTITATIVE RESEARCH

Quantitative research (the word ‘quantitative’ comes from the word ‘quantity’) involves information or data in the form of numbers. This allows us to measure or to quantify a whole range of things. For example: the number of people who live below the poverty line; the number of children between specific ages who attend school; the average spending power in a community; or the number of adults who have access to computers in a village or town.

A common way of conducting quantitative research is using a survey. Surveys usually involve filling in a questionnaire. The usefulness of a survey is that the information you get is standardized because each respondent – the person who fills out the questionnaire – is answering the exact same questions. Once you have enough responses to your questionnaire, you can then put the data together and analyse it in a way that answers your research question – or what it is you want to know.

It is important to realize that quantitative research does not necessarily mean that respondents will give numbers for their answers to your questions. Sometimes they may answer a ‘yes’ or ‘no’ question, as in: ’Do you have a computer?’ Sometimes they might write down an answer, a word, a sentence, or a paragraph to describe something, as in answers to: ’What is the brand or make of your computer?’ and ’Please describe in detail what you use your computer for.’ Other answers may involve numbers, as in: ’How many computers do you have in your business or organization?’

How these varied responses become numbers is in the way they are analysed. From the example questions above, one might be able to say: 20 out of the 30 (66%) respondents use a particular brand of computer, while 5 (16%) use another. The remaining five respondents all used different brands of computers which you would list. You might then want to provide some examples of how the computers are used.

There are, of course, many different kinds of quantitative research besides the survey. Observational research involves watching or observing various behaviours and patterns.  Perhaps you want to find out how many cars of a particular make use a specific intersection.To do this you might stand at the intersection at a particular time of day, and record the makes of cars. Perhaps you want to monitor the number of people entering a particular shop at specific times of the day, recording their behaviours, and whether or not they buy anything or are just browsing.

More complicated forms of quantitative research are experimental research or mathematical modelling research.  (See the glossary for their definitions.)

Media research may use a form of quantitative research to understand the number of articles published in a range of newspapers on a particular topic. These articles are then analysed according to various monitoring criteria, such as the specific focus of the article, the author, the date of publication, page number, the column length and even the headline. From this, you can make analyses such as: ’Of all the commercial newspapers in Nigeria, 25% of them carried stories on HIV/AIDS during January and February 2004.’ You may want to add that most of these were written by five journalists, or that none of them appeared on the front page of the newspaper during this period.

With all kinds of research, it is important to be as specific as possible, and to explain your assumptions. Remember, your research results might not tell you everything but they will be valuable for what they do reveal. In the example of the media research, we might be able to conclude that HIV/AIDS didn’t feature prominently in the commercial media during the monitored period. We might want to find out the reasons for this and decide to interview the newspaper editors. By doing this, we would be doing some qualitative research.

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CROSSTAB

Characteristic use of the crosstab are the data input or the nominal scale ordinal, such as tabulation between gender a person with a level of education person, a person's employment with the attitudes of those with a particular product, and others. Actually, the data metric (interval or ratio) in principle can also be done crosstab. Only on data metric, there is the possibility of data has a decimal, such as the length of 1.25 meters, 1.26 meters long, 1.27 meters long, and so on. all have different values ​​that have made ​​a lot of columns; it can be place the number of rows or columns to be effective and not so much to describe the data. For that manufacturing metrics data crosstab
usually seen 'content' data first. In practice, making crosstab can also be accompanied by counting the closeness of the relationship (association) antarisi crosstab. Statistical tools often used to measure the association on a crosstab chisquare. This tool can be applied in statistical practice to test any the relationship between the rows and columns of a crosstab. In addition to chi square, several other test tools are popular Kendall, Kappa, and so.

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DESCRIPTIVE TYPES OF RESEARCH

Many types of research that includes a descriptive study. Every expert in the study often provide information about the grouping of types of descriptive research, tend to vary slightly. The difference is usually influenced by the views and knowledge into the background of these experts. Differences of opinion, one of them when viewed from the musty how the process of collecting data in a descriptive study conducted by researchers.

From the aspect of how the process of data collection is done, all kinds of research can be minimal deskrptif dbedakan into three kinds, namely the report of, or self-report, development studies, advanced studies, (follow-up study), and studies sosiometrik.

Research Reports Of (Self-Report research)
From the data collected related to the descriptive study has several types including self-report using the observations. In a self-study report, the information collected by the person who also serves as a researcher.
In this study self-report research is recommended using direct observation techniques, namely individuals who researched visited and viewed their activities in a natural situation. Direct observation goal is to obtain information according to the problem and research objectives. In a self-report studies, researchers are also encouraged to use other tools to obtain data, including for example by using other equipment such as records, cameras, and recording. Such tools are used primarily to maximize when they have to get the data from the field.

That need to be considered by the researchers that the model of self-report is that in using the method of observation in an interview, the researcher should be able to use the data simultaneously to obtain the maximum. One example of research using self-report can be viewed in a report on the study of Institutional and System Financing Small and Medium Enterprises.

Examples of Descriptive study using self-report
Institutional Studies and System Financing Small and Medium Enterprises

Comparative study of institutional and small business financing system has a 5 important objectives, namely:
• Identify the factors of development of small micro and medium enterprises through the institutional system.
• Obtain information about the factors of institutional development for small and medium-sized cooperatives.
• Improve cooperation agencies in order to have a comprehensive financing system that is relevant to the needs of employers.
• Formulate policies, implementation, and monitoring systems relevant to the institutional and financing systems of small and medium businesses.
• Obtain a model of best practice on institutional and financing system in the State of the Philippines which may be applied in accordance with Indonesian culture.
The study was a comparative study using the method dekriptif with self-report approach. Institution where the research is high-Department of Trade and Industry and other agencies and other institutions that handle the growth and development of small and medium enterprises. Other institutions include the Institute for Development Bureau Office of Small and Medium Enterprises (BSMD), Office of Technology Livelihood Resource Center (TLRC). Colombo Plan Staff CALLEGE (CPSC), and Technology Universisty of Philippines (TUP). Research subject is the resource person who has the necessary information and they are eager and willing to cooperate in providing information.

This comparative study has results that can be grouped into two parts, the institute management and financing systems of small and medium businesses. Relating to the management body of them are SMEs include:
Development of small and medium enterprises in the Philippines under the Department Of Trade and Industry (DTI), involving several agencies that have national and regional level.

Which include small and medium entrepreneurs in the Philippines, is the businessman or entrepreneur, either indifidual Filipino citizens or groups who have the following characteristics: micro entrepreneur has assets <P1, 500.001; small business asset mempuyai-P 1,500,001 P 15,000,000; and medium entrepreneurs mempuyai P15 ,000,001-P60, 000,000

There are six higher institutions of State and several offices that are relevant to a variety of business activities as a place of registration and which will assist the development and growth of new businesses. Government programs related to small and medium enterprises in lakanakan by all relevant agencies including the office is under the responsibility of the department of trade industry, the Department of finance, budget and management. Agriculture, agrarian reform, environment and natural resources, labor and employment, transport and communications, employment and pubic roads, and government and tourism, science and technology, national economic and development authority of all the Philippine central bank at the national, regional, and provinces. At each office of the institution have procedures, authority, and the number of registrations of financing listed prominently. Authority, procedures and a clear total cost is, in principle, is to make it easier for entrepreneurs, we they do their business registration to the office of the agency.

Development Studies (Developmental Study)
Devlopmental development studies or studies conducted by many researchers in the field of educational psychology or related field behavior, development of research goals are generally related to behavioral variables individually and in groups. In that study the researchers interested in the development of the preferred variable to distinguish between the age, growth or maturity of the subject under study.

Development studies are usually done within a certain time period with a longitudinal, aims to discover the dimensions that occur in the development of a respondents. Dimensions that are often a concern of this research, for example: intellectual, physical, emotional, reaction to certain terhadapan, and development of children sosoial. Development studies are usually done either by cross-sectional or logiotudinal.

If the research is done by cross-sectional models, researchers at the same time and disimultan use shared variables to be investigated levels. Data obtained from each level can be described and then look at a comparison or association level. In the longitudinal study development of a model, researchers used a sample of respondents, for example: one class at school, then intensive scrutiny to continue their development within a specified period such as three months, six months, one year. All the phenomena that appear to be used as documented in analyzing information in order to achieve results.

Continuing Studies (Follow-up study)
Continuation study conducted by researchers to determine the status of the respondent after a certain period of time the treatment fare, such as education rogram. Continuation of the study is done for internal evaluation and the evaluation eksteral, after the subject or respondent received the program in an educational institution. For example, the National Accreditation Board recommends the level of information uptake graduates in entering the workforce after they complete their education program. In a continuation of research studies investigators are usually familiar with the term between output and outcome. Out (keluran) information relating to the final result after a program is given to the subject of targets in the finish. While the definition of data taken from the outcome (result) usually involves the effect of a treatment, such as educational programs to the subject which is examined after they return home is the community.

Sosiometrik studies (Sociometric study)
What is meant by sosiometrik is the analysis of interpersonal relationships within a group of individuals. Through the analysis of individual choice on the basis of an idol or someone rejection of others in a group can be set.
Prinsif theory is basically the study sosiometrik penanyakan on each member of the group under study to determine the premises whom he most liked to work together in group activities. In this case, he can choose one or three in the group. Of each member, researchers will gain a variety of positions. By using images sociogram, a person's position would be explained by its position within the organization.

The sociogram is generally used some terms that may indicate limitations of individual positions within the group. Some terms such as for example:
• "Star" is given to those most widely chosen by its members,
• "Isolated" is given to those who are not chosen by the members of the group,
• "Click" is given to a small group of members who each choose one person in the group.

Education, sosiometrik has been widely used to determine a person's relationship status variables such as formal leader, a leader in educational institution or an individual's position in the group with a variable in educational activities. The research is descriptive research methods that seek menggambarlkan object or subject under study in accordance with what it is, with the aim of describing systematically facts and objects in meticulous karakeristik appropriately.

Has a unique descriptive study of them, as follows.
• Using questionnaires or interviews are often just get a few respondents who can menakibatkan usually conclusion;
• Descriptive research that uses observation, sometimes in the data collection did not obtain sufficient data;
• Requires ssecara formulate the problem in clear, so that at the time of capture data in the field, researchers have no trouble.
An examination of the field data collection, descriptive research can be distinguished, among others, self study, study of the development, continuation of studies, and studies sosiometrik.
• The study has a descriptive methods such as the following steps.
• Identify the significant issues to be resolved through descriptive methods.
• Limit and clearly define problems.
• Determine the purpose and benefits of research.
• Conduct literature related to research problems.
• Establish a framework of thinking, and research questions or hypotheses and research.
• Design research methods yamg about the use, including in this case to determine the population, sample, sampling techniques, determine the data collection instruments, and analyzing data.
• Collect and organize and analyze data using statistical techniques that are relevant.
• Create a research report

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OTHER TYPES OF RESEARCH METHODS

Other types of research methods can be classified based, objective, and the level of naturalness object under study. Based on the objectives, research methods can be classified as basic research, applied research, and research development. further based on the level of naturalness, research methods can be grouped into experimental research methods, surveys, and naturalistic.

Research and development is the bridge between basic research with applied research, where basic research aimed to discover new knowledge about fundamental phenomena and applied research aimed at finding practical knowledge can be applied. although there are also times when the applied research to develop products. research and development aims to discover, develop and validate a product.
Experimental research methods, surveys and naturalistic / qualitative also be placed in a continuum line. experimental research methods are research methods used to search for a specific treatment effect. for example the influence of AC on the productivity of office work. survey methods used to obtain data from a particular place is natural (not artificial), but the researchers performed the treatment in the collection of data, for example by distributing questionnaires, tests, structured interviews and so on (not as in the experimental treatment). naturalistic research methods / qualitative, used to examine the natural place, and the study did not make the treatment, because the researchers in collecting data is emic, that is based on the views of the data source, not the views of researchers.
Based on the types of research as mentioned above, it can be noted here that, which is included in the quantitative method is experimental and survey research methods, while qualitative methods are included in the naturalistic method. research for basic research in general, using experimental and qualitative methods, applied research using experimental and survey, R & D can use the survey, qualitative and experimental.

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