Specifically, the study seeks to answer the following questions:
1. What are the leadership skills of the managers of the ABC Corporation in terms of:
1.1 human relations 1.2 technical
1.3 administrative, and 1.4 institutional skills
as perceived by themselves and their subordinates?
2. Is there a significant difference in the perceptions of the two groups of respondents on the leadership skills of managers, in terms of:
2.1 human relations 2.2 technical
2.3 administrative, and 2.4 institutional skills?
TOPICAL FORM
The study seeks to determine the following:
2. The leadership skills of the managers of the ABC Corporation in terms of:
1.4 human relations 1.5 technical
1.6 administrative, and 1.4 institutional skills
as perceived by themselves and their subordinates.
2. The significant difference of the perceptions of the two groups of respondents on the leadership skills of managers, in terms of:
2.5 human relations 2.6 technical
2.7 administrative, and 2.8 institutional skills.
THE NULL HYPOTHESIS
These are statements of “no significant relationship/difference” which is mainly a negation of the second level specific problem statements.
The order or arrangements of the statements follow from the second level specific problem statements.
The hypothesis is directly related to a theory but contains operationally defined variables and is in testable form. Hypotheses allow us to determine, through research, if our theory is correct. For example, “does prior work experience result s in better grades?” When doing research, we are typically looking for some type of difference or change between two or more groups. For example, we wanted to test the difference between “having work experience” and “not having work experience on college grades”. Every study has two hypotheses; one stated as a difference between groups and one stated as no difference between groups.
When stated as a difference between groups, our hypothesis would be, “stud ents with prior work experience earn higher grades than students without prior work experience.”
This is called our research or scientific hypothesis. Because most statistics test for no difference, however, we must also have a null hypothesis. The null hypothesis is always written with the assumption that the groups do not differ. For example, the null hypothesis would state that, “students with work experience will not receive different grades than students with no work experience.”
The null hypothesis is what we test through the use of statistics and is abbreviated Ho.
Since we are testing the null, we can assume then that if the null is not true then some alternative to the null must be true. The research hypothesis stated earlier becomes our alternative, abbreviated H1. In order to make research as specific as possible we typically look for one of two outcomes, either the null or the alternative hypothesis. To conclude that there is no difference between the two groups means we are accepting our null hypothesis. If we, however, show that the null is not true then we must reject it and therefore conclude that the alternative hypothesis must be true. While there may be a lot of gray area in the research itself, the results must always be stated in black and white.
SIGNIFICANCE OF THE STUDY
The following points are important to note in the significance of the study:
This notes the contribution of the proposed study either to a body of scientific knowledge, to practitioners in the area of research, or to any other group who will benefit from the results of the study.
This is started with an introductory statement implying an enumeration of the individuals or groups who served as the beneficiary of the results of the
investigation.
The beneficiaries should be arranged according to importance and the extent of the benefits they will get, that is macro to micro approach, or from general to specific approach is ideal, vis-à-vis the statements specifically indicating the benefits each could be derived.
SCOPE AND LIMITATIONS OF THE STUDY
Some points to remember in this section are the following:
Included in this portion are the boundaries like geographic, population, time, and variables to be discussed (Castillo, 2001).
Limitations of the study emanating from certain weaknesses/shortcomings should be noted in this section. Reasons for excluding them in the investigation should be discussed.
THE DEFINITIONS OF TERMS
Researchers should remember the following points in formulating the section on definition of terms:
This section provides a definition for the terms repeatedly used throughout the discussion which is usually the variables of the study. The definition usually starts with conceptual definition and followed by the operational definition.
Conceptual definition is mostly concerned with attributing authorities like books, magazines, etc., including unpublished materials (Vizcarra, 2003).
Operational definition of terms is done when a researcher defines the terms as he uses them in the study (Vizcarra, 2003). They can be defined according to the measurements of variables.
Technical studies usually define terms as an explanatory device (Vizcarra, 2003).
Illustration:
Variable Conceptual
Definition Operational Definition Work
Effort Speed My job requires me to work fast for ___ hours per day (1-2, 3-5, 6+) Hardness My job requires me to work hard
for ___ hours per day (1-2, 3-5, 6+)
Effort My job requires a lot of effort
for ___ hours per day (1-2, 3-5, 6+)
Dexterity My job requires a lot of dexterity for ___ hours per day (1-2, 3-5, 6+)
Repetitiveness My job is doing repetitive things for ___ hours per day (1-2, 3-5, 6+)
RELATED LITERATURE Points to remember:
This is an exhaustive, comprehensive, and selective discussion of the theories not included in the theoretical framework, but which have relation to the proposed study on the problem dimension.
The theories discussed in this section should come from books, documents, articles, etc. which are closely related to the present study (Castillo, 2001).
Literatures cited in this section should be properly documented.
The discussion is organized logically usually making use of the order of the
specific problem statement as basis for organization. At some point, organization maybe is according to the arrangement of the variables.
The discussion should include the links, the similarities, and the dissimilarities among the works cited and the proposed research.
RELATED STUDIES Points to remember:
This is an exhaustive, comprehensive, and selective discussion of the ideas from previous researches which have relation to the proposed study on the problem dimension.
The ideas discussed in this section should come from theses/dissertations, research journals, etc. which are closely related to the present study (Castillo, 2001).
Studies cited in this section should be properly documented.
The discussion is organized logically usually making use of the order of the specific problem statement as basis for organization.
The discussion should include the links, the similarities, and the dissimilarities among the studies cited and the proposed research.
SYNTHESIS
These are concluding statements which provide a synthesis of the results of the review of literature and studies.
THE RESEARCH DESIGN
This deals with the research design and the technique to be used in the study.
It also include an overview of how the respondents/subjects will be chosen, how the rational size will be determined, the instruments to be used and their
validation, and the data analyses scheme which include the application of statistical tools for treatment of data yielded by the study.
There are two types of study designs, experimental and quasi-experimental.
Experimental: The experimental design uses a control group and applies treatment to a second group. It provides the strongest evidence of causation through extensive controls and random assignment to remove other differences between groups. Using the evaluation of a job training program as an example, one could carefully select and randomly assign two groups of unemployed welfare recipients. One group would be provided job training and the other would not. If the two groups are similar in all other relevant characteristics, you could assume any differences between the groups employment one year later was caused by job training.
Whenever you use an experimental design, both the internal and external validity can become very important factors.
Internal validity : The extent to which accurate and unbiased association between the IV and DVs were obtained in the study group.
External validity : The extent to which the association between the IV and DV is accurate and unbiased in populations outside the study group.
(Please see a separate Handout for EXPERIMENTAL RESEARCH DESIGNS – Appendix A)
Quasi-experimental: The quasi-experimental design does not have the controls employed in an experimental design (most social science research). Although internal validity is lower than can be obtained with an experimental design, external validity is generally better and a well designed study should allow for the use of statistical controls to compensate for extraneous variables.
Types of quasi-experimental design:
1. Cross-sectional study : obtained at one point in time (most surveys) 2. Case study : in-depth analysis of one entity, object, or event
3. Panel study : (cohort study) repeated cross-sectional studies over time with the same participants
4. Trend study : tracking indicator variables over a period of time (unemployment, crime, dropout rates)
(Please see a separate Handout for OTHER RESEARCH DESIGNS) Common Types of Research Designs
Three commonly used research types or designs are quantitative, qualitative, and mixed research.
Quantitative research follows a deductive research process and involves the collection and analysis of quantitative (i.e., numerical) data to identify statistical relations of variables. Common quantitative research methods include: content (relational) analysis, experiments, observations (scaled ratings, checklists), and surveys (closed-ended, validated scales)
Qualitative research follows an inductive research process and involves the collection and analysis of qualitative (i.e., non-numerical) data to search for patterns, themes, and holistic features. Common qualitative research methods include: content (conceptual) analysis, focus groups, observations (narrative, comments), interviews, and surveys (open-ended).
Mixed research combines or mixes quantitative and qualitative research techniques in a single study. Two sub-types of mixed research includes mixed method research—using qualitative and quantitative approaches for different phases of the study—and mixed model research—using quantitative and qualitative approaches within or across phases of the study.
Research approaches are generally categorized into quantitative and qualitative design.
Each research design may be further classified into any of the four different research purposes: to explore (an attempt to generate ideas about educational phenomenon), describe (an attempt to describe the characteristics of educational phenomenon), predict (an attempt to forecast an educational phenomenon), and explain (an attempt to show why and how an educational phenomenon operates).
Because both quantitative and qualitative approaches have weaknesses that limit the research purposes for which they are appropriate, a mixed research approach may be used that takes advantage of the complementary strengths of the qualitative and quantitative approaches.
Research purpose Preferred approach
Qualitative Quantitative Mixed
Explore X X
Describe X X
Predict X X
Explain X X
TIME AND LOCALE OF THE STUDY
This is a discussion about the time when the study conducted and the setting or place
THE SUBJECTS/RESPONDENTS OF THE STUDY
This section describes the population, why and how the respondents/subjects are to be chosen.
The sampling technique may also be discussed in passing.
SAMPLING TECHNIQUE
It begins with the definition of the population. To define the population is to indicate the characteristics of the elements from which the sample will be taken (Castillo, 2001).
A careful explanation of how to choose the units that will provide the data should follow.
Indicate the sample size and the population frame from which the samples well be taken.
(Please see a separate Handout for SAMPLING)
INSTRUMENTATION
This contains a description of the instrument/s used in the investigation and a discussion of how to use the same instrument. Explanation of the developmental processes involved in the creation of the instrument should form a part of the discussion.
(Please see a separate handout for RESEARCH INSTRUMENTS)
VALIDATION OF THE INSTRUMENT
This includes a discussion on the validation aspects employed when the
instrument was developed, the sample used in the try out, as well as the place where the validation where conducted.
The reliability estimate used should also be discussed and the resulting reliability coefficient should be described.
THE GATA GATHERING PROCEDURE
A discussion on the steps undertaken in gathering the needed data from seeking permission for the fielding of the instrument up to the process of retrieval of the data. This should be explained in detail, step by step that will lead the readers to follow through the process engaged in by the researcher.
Tabulated Example
ENHANCING SCHOOL LEADING PERFORMANCE OF EDUCATIONAL ADMINISTRATORS IN PHILIPPINE
PUBLIC ELEMENTARY SCHOOLS
Summary Table of Data Requirements and Data Gathering Techniques Research
model
Unobtrusive measures are measures that don't require the researcher to intrude in the research context. Direct and participant observation require that the researcher be physically present. This can lead the respondents to alter their behavior in order to look
good in the eyes of the researcher. A questionnaire is an interruption in the natural stream of behavior. respondents can get tired of filling out a survey or resentful of the questions asked.
Unobtrusive measurement presumably reduces the biases that result from the intrusion of the researcher or measurement instrument. However, unobtrusive measures reduce the degree the researcher has over the type of data collected. For some constructs there may simply not be any available unobtrusive measures.
Three types of unobtrusive measurement are discussed here.
1. Indirect Measures
An indirect measure is an unobtrusive measure that occurs naturally in a research context. The researcher is able to collect the data without introducing any formal measurement procedure.
The types of indirect measures that may be available are limited only by the researcher's imagination and inventiveness. For instance, let's say you would like to measure the popularity of various exhibits in a museum. It may be possible to set up some type of mechanical measurement system that is invisible to the museum patrons. In one study, the system was simple. The museum installed new floor tiles in front of each exhibit they wanted a measurement on and, after a period of time, measured the wear-and-tear of the tiles as an indirect measure of patron traffic and interest. We might be able to improve on this approach considerably using electronic measures. We could, for instance, construct an electrical device that senses movement in front of an exhibit. Or we could place hidden cameras and code patron interest based on videotaped evidence.
One of my favorite indirect measures occurred in a study of radio station listening preferences. Rather than conducting an obtrusive survey or interview about favorite radio stations, the researchers went to local auto dealers and garages and checked all cars that were being serviced to see what station the radio was currently tuned to. In a similar manner, if you want to know magazine preferences, you might rummage through the trash of your sample or even stage a door-to-door magazine recycling effort.
These examples illustrate one of the most important points about indirect measures --you have to be very careful about the ethics of this type of measurement. In an indirect measure you are, by definition, collecting information without the respondent's knowledge. In doing so, you may be violating their right to privacy and you are certainly not using informed consent. Of course, some types of information may be public and therefore not involve an invasion of privacy.
There may be times when an indirect measure is appropriate, readily available and ethical. Just as with all measurement, however, you should be sure to attempt to estimate the reliability and validity of the measures. For instance, collecting radio station preferences at two different time periods and correlating the results might be useful for
assessing test-retest reliability. Or, you can include the indirect measure along with other direct measures of the same construct (perhaps in a pilot study) to help establish construct validity.
2. Content Analysis
Content analysis is the analysis of text documents. The analysis can be quantitative, qualitative or both. Typically, the major purpose of content analysis is to identify patterns in text. Content analysis is an extremely broad area of research. It includes:
Thematic analysis of text
The identification of themes or major ideas in a document or set of documents. The documents can be any kind of text including field notes, newspaper articles, technical papers or organizational memos.
Indexing
There are a wide variety of automated methods for rapidly indexing text documents. For instance, Key Words in Context (KWIC) analysis is a computer analysis of text data. A computer program scans the text and indexes all key words. A key word is any term in the text that is not included in an exception dictionary. Typically you would set up an exception dictionary that includes all non-essential words like "is", "and", and "of". All key words are alphabetized and are listed with the text that precedes and follows it so the researcher can see the word in the context in which it occurred in the text. In an analysis of interview text, for instance, one could easily identify all uses of the term "abuse" and the context in which they were used.
Quantitative descriptive analysis
Here the purpose is to describe features of the text quantitatively. For instance, you might want to find out which words or phrases were used most frequently in the text. Again, this type of analysis is most often done directly with computer programs.
Content analysis has several problems you should keep in mind. First, you are limited to the types of information available in text form. If you are studying the way a news story is being handled by the news media, you probably would have a ready population of news stories from which you could sample. However, if you are interested in studying people's views on capital punishment, you are less likely to find an archive of text documents that would be appropriate. Second, you have to be especially careful with sampling in order to avoid bias. For instance, a study of current research on methods of treatment for cancer might use the published literature as the population. This would leave out both the writing on cancer that did not get published for one reason or another as well as the most recent work that has not yet been published. Finally, you have to be careful about interpreting results of automated context analyses. A computer program cannot
determine what someone meant by a term or phrase. It is relatively easy in a large analysis to misinterpret a result because you did not take into account the subtleties of meaning.
However, content analysis has the advantage of being unobtrusive and, depending on whether automated methods exist, can be a relatively rapid method for analyzing large amounts of text.
3. Secondary Analysis of Data
Secondary analysis, like content analysis, makes use of already existing sources of data. However, secondary analysis typically refers to the re-analysis of quantitative data rather than text.
In our modern world there is an unbelievable mass of data that is routinely collected by governments, businesses, schools, and other organizations. Much of this information is stored in electronic databases that can be accessed and analyzed. In addition, many research projects store their raw data in electronic form in computer archives so that
In our modern world there is an unbelievable mass of data that is routinely collected by governments, businesses, schools, and other organizations. Much of this information is stored in electronic databases that can be accessed and analyzed. In addition, many research projects store their raw data in electronic form in computer archives so that