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This document discusses research design and different types of research methods. It begins by defining a research design as a systematic plan for studying a scientific problem that defines key aspects of a study such as the type of design, research questions, variables, and statistical analysis plan. It then describes different types of non-experimental designs including relational, comparative, and longitudinal designs. Within non-experimental designs, it distinguishes between exploratory and descriptive research. It also discusses experimental designs including causal and quasi-experimental designs. Finally, it contrasts cross-sectional and longitudinal study designs. In summary, the document provides an overview of key research design concepts and differentiates between experimental and non-experimental designs as well as specific types of designs within those two
![probability of rejecting a real effect or relation, this probability is sometimes referred to
as β and the associated error is of type II. In other words, if you want to see whether some
of your measured variables could be related, you would want to increase your chances of
finding a significant result by lowering the threshold of what you deem to be significant.
b) Descriptive research design
Descriptive research design is used to describe characteristics of a population or
phenomenon being studied. It does not answer questions about how/when/why the
characteristics occurred. Rather it addresses the "what" question (What are the
characteristics of the population or situation being studied?) [1]
The characteristics used to
describe the situation or population are usually some kind of categorical scheme also
known as descriptive categories. For example, the periodic table categorizes the elements.
Scientists use knowledge about the nature of electrons, protons and neutrons to devise
this categorical scheme. We now take for granted the periodic table, yet it took
descriptive research to devise it. Descriptive research generally precedes explanatory
research. For example, over time the periodic table’s description of the elements allowed
scientists to explain chemical reaction and make sound prediction when elements were
combined. Hence, research cannot describe what caused a situation. Thus, Descriptive
research cannot be used to as the basis of a causal relationship, where one variable affects
another. In other words, descriptive research can be said to have a low requirement for
internal validity. The description is used for frequencies, averages and other statistical
calculations. Often the best approach, prior to writing descriptive research, is to conduct a
survey investigation. Qualitative research often has the aim of description and researchers
may follow-up with examinations of why the observations exist and what the
implications of the findings are.](https://image.slidesharecdn.com/researchdesign-140618130510-phpapp02/85/pengajian-malaysia-2-320.jpg)
![b. Quasi-experimental research design
A quasi-experiment is an empirical study used to estimate the causal impact of an
intervention on its target population. Quasi-experimental research shares
similarities with the traditional experimental design or randomized controlled
trial, but they specifically lack the element of random assignment to treatment or
control. Instead, quasi-experimental designs typically allow the researcher to
control the assignment to the treatment condition, but using some criterion other
than random assignment (e.g., an eligibility cutoff mark).[1]
In some cases, the
researcher may have control over assignment to treatment condition. Quasi-
experiments are subject to concerns regarding internal validity, because the
treatment and control groups may not be comparable at baseline. With random
assignment, study participants have the same chance of being assigned to the
intervention group or the comparison group. As a result, differences between
groups on both observed and unobserved characteristics would be due to chance,
rather than to a systematic factor related to treatment (e.g., illness severity).
Randomization itself does not guarantee that groups will be equivalent at baseline.
Any change in characteristics post-intervention is likely attributable to the
intervention. With quasi-experimental studies, it may not be possible to
convincingly demonstrate a causal link between the treatment condition and
observed outcomes. This is particularly true if there are confounding variables
that cannot be controlled or accounted for.
3. Cross sectional design versus longitudinal design
a) Cross-sectional research
Cross- sectional research is one type of observational study that involves data
collection from a population, or a representative subset, at one specific point in
time. They differ from case-control studies in that they aim to provide data on the
entire population under study, whereas case-control studies typically include only
individuals with a specific characteristic, with a sample, often a tiny minority, of](https://image.slidesharecdn.com/researchdesign-140618130510-phpapp02/85/pengajian-malaysia-4-320.jpg)
![the rest of the population. Cross-sectional studies are descriptive studies (neither
longitudinal nor experimental). Unlike case-control studies, they can be used to
describe, not only the Odds ratio, but also absolute risks and relative risks from
prevalences. They may be used to describe some feature of the population, such
as prevalence of an illness, or they may support inferences of cause and effect.
b) Longitudinal design
A longitudinal study is a correlational research study that involves repeated
observations of the same variables over long periods of time — often many
decades. It is a type of observational study. Longitudinal studies are often used in
psychology to study developmental trends across the life span, and in sociology to
study life events throughout lifetimes or generations. The reason for this is that
unlike cross-sectional studies, in which different individuals with same
characteristics are compared,[1]
longitudinal studies track the same people, and
therefore the differences observed in those people are less likely to be the result of
cultural differences across generations. Because of this benefit, longitudinal
studies make observing changes more accurate, and they are applied in various
other fields. In medicine, the design is used to uncover predictors of certain
diseases. In advertising, the design is used to identify the changes that advertising
has produced in the attitudes and behaviors of those within the target audience
who have seen the advertising campaign. Because most longitudinal studies are
observational, in the sense that they observe the state of the world without
manipulating it, it has been argued that they may have less power to detect causal
relationships than do experiments. But because of the repeated observation at the
individual level, they have more power than cross-sectional observational studies,
by virtue of being able to exclude time-invariant unobserved individual
differences, and by virtue of observing the temporal order of events. Some of the
disadvantages of longitudinal study include the fact that it takes a lot of time and
is very expensive. Therefore, it is not very convenient. Longitudinal studies allow](https://image.slidesharecdn.com/researchdesign-140618130510-phpapp02/85/pengajian-malaysia-5-320.jpg)
pengajian malaysia
- 1. RESEARCH DESIGN A research design is a systematic plan to study a scientific problem. The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta- analytic) and sub-type (e.g., descriptive-longitudinal case study), research question, hypotheses, independent and dependent variables, experimental design, and, if applicable, data collection methods and a statistical analysis plan. 1. Non-experimental research designs Non-experimental research designs do not involve a manipulation of the situation, circumstances or experience of the participants. Non-experimental research designs can be broadly classified into three categories. First, relational designs, in which a range of variables is measured. These designs are also called correlational studies, because correlational data are most often used in analysis. It is important to clarify here that correlation does not imply causation, and rather identifies dependence of one variable on another. Correlational designs are helpful in identifying the relation of one variable to another, and seeing the frequency of co-occurrence in two natural groups. The second type is comparative research. These designs compare two or more groups on one or more variable, such as the effect of gender on grades. The third type of non-experimental research is a longitudinal design. A longitudinal design examines variables such as performance exhibited by a group or groups over time. There are two types of non-experimental research design which is: a) Exploratory research design Exploratory research seeks to generate a posteriori hypotheses by examining a data-set and looking for potential relations between variables. It is also possible to have an idea about a relation between variables but to lack knowledge of the direction and strength of the relation. If the researcher does not have any specific hypotheses beforehand, the study is exploratory with respect to the variables in question (although it might be confirmatory for others). The advantage of exploratory research is that it is easier make new discoveries due to the less stringent methodological restrictions. Here, the researcher does not want to miss a potentially interesting relation and therefore aims to minimize the
- 2. probability of rejecting a real effect or relation, this probability is sometimes referred to as β and the associated error is of type II. In other words, if you want to see whether some of your measured variables could be related, you would want to increase your chances of finding a significant result by lowering the threshold of what you deem to be significant. b) Descriptive research design Descriptive research design is used to describe characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred. Rather it addresses the "what" question (What are the characteristics of the population or situation being studied?) [1] The characteristics used to describe the situation or population are usually some kind of categorical scheme also known as descriptive categories. For example, the periodic table categorizes the elements. Scientists use knowledge about the nature of electrons, protons and neutrons to devise this categorical scheme. We now take for granted the periodic table, yet it took descriptive research to devise it. Descriptive research generally precedes explanatory research. For example, over time the periodic table’s description of the elements allowed scientists to explain chemical reaction and make sound prediction when elements were combined. Hence, research cannot describe what caused a situation. Thus, Descriptive research cannot be used to as the basis of a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity. The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are.
- 3. 2. The experimental research design experimental design is the design of any information-gathering exercises where variation is present, whether under the full control of the experimenter or not. However, in statistics, these terms are usually used for controlled experiments. Formal planned experimentation is often used in evaluating physical objects, chemical formulations, structures, components, and materials. Other types of study, and their design, are discussed in the articles on computer experiments, opinion polls and statistical surveys (which are types of observational study), natural experiments and quasi-experiments (for example, quasi-experimental design). See Experiment for the distinction between these types of experiments or studies. In the design of experiments, the experimenter is often interested in the effect of some process or intervention (the "treatment") on some objects (the "experimental units"), which may be people, parts of people, groups of people, plants, animals, etc. Design of experiments is thus a discipline that has very broad application across all the natural and social sciences and engineering. There are two types of experimental research design, which is: a) Causal research design The Casual research design in marketing research identifies the cause and effect between people. This study is primarily focuses on the relationship between several people and how it affects the person contract and association. Causal comparative research can be on people relationship with things that are personal to them such as losing weight and the ups and downs of eating habits. It finds the cause and effect relationship between variables. It seeks to determine how the dependent variable changes with variations in the independent variable. For example, a marketer may want to determine the cause of dip in sales. He would test the sales against various parameters like selling price, competition, geography etc. The results obtained may not be very straight forward because, more often than not, the variability will be a factor of more than one variable. Therefore, while varying one variable, the other variables need to be held constant
- 4. b. Quasi-experimental research design A quasi-experiment is an empirical study used to estimate the causal impact of an intervention on its target population. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but they specifically lack the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment (e.g., an eligibility cutoff mark).[1] In some cases, the researcher may have control over assignment to treatment condition. Quasi- experiments are subject to concerns regarding internal validity, because the treatment and control groups may not be comparable at baseline. With random assignment, study participants have the same chance of being assigned to the intervention group or the comparison group. As a result, differences between groups on both observed and unobserved characteristics would be due to chance, rather than to a systematic factor related to treatment (e.g., illness severity). Randomization itself does not guarantee that groups will be equivalent at baseline. Any change in characteristics post-intervention is likely attributable to the intervention. With quasi-experimental studies, it may not be possible to convincingly demonstrate a causal link between the treatment condition and observed outcomes. This is particularly true if there are confounding variables that cannot be controlled or accounted for. 3. Cross sectional design versus longitudinal design a) Cross-sectional research Cross- sectional research is one type of observational study that involves data collection from a population, or a representative subset, at one specific point in time. They differ from case-control studies in that they aim to provide data on the entire population under study, whereas case-control studies typically include only individuals with a specific characteristic, with a sample, often a tiny minority, of
- 5. the rest of the population. Cross-sectional studies are descriptive studies (neither longitudinal nor experimental). Unlike case-control studies, they can be used to describe, not only the Odds ratio, but also absolute risks and relative risks from prevalences. They may be used to describe some feature of the population, such as prevalence of an illness, or they may support inferences of cause and effect. b) Longitudinal design A longitudinal study is a correlational research study that involves repeated observations of the same variables over long periods of time — often many decades. It is a type of observational study. Longitudinal studies are often used in psychology to study developmental trends across the life span, and in sociology to study life events throughout lifetimes or generations. The reason for this is that unlike cross-sectional studies, in which different individuals with same characteristics are compared,[1] longitudinal studies track the same people, and therefore the differences observed in those people are less likely to be the result of cultural differences across generations. Because of this benefit, longitudinal studies make observing changes more accurate, and they are applied in various other fields. In medicine, the design is used to uncover predictors of certain diseases. In advertising, the design is used to identify the changes that advertising has produced in the attitudes and behaviors of those within the target audience who have seen the advertising campaign. Because most longitudinal studies are observational, in the sense that they observe the state of the world without manipulating it, it has been argued that they may have less power to detect causal relationships than do experiments. But because of the repeated observation at the individual level, they have more power than cross-sectional observational studies, by virtue of being able to exclude time-invariant unobserved individual differences, and by virtue of observing the temporal order of events. Some of the disadvantages of longitudinal study include the fact that it takes a lot of time and is very expensive. Therefore, it is not very convenient. Longitudinal studies allow
- 6. social scientists to distinguish short from long-term phenomena, such as poverty. If the poverty rate is 10% at a point in time, this may mean that 10% of the population are always poor, or that the whole population experiences poverty for 10% of the time. It is impossible to conclude which of these possibilities is the case using one-off cross-sectional studies. Types of longitudinal studies include cohort studies and panel studies. Cohort studies sample a cohort, defined as a group experiencing some event (typically birth) in a selected time period, and studying them at intervals through time. Panel studies sample a cross-section, and survey it at (usually regular) intervals. A retrospective study is a longitudinal study that looks back in time. For instance, a researcher may look up the medical records of previous years to look for a trend. In this case the research design used is exploratory research design. This is because of the three objectives mentioned which is: 1. To develop an integrated corporate entrepreneurship model that incorporate the individual dimension, organizational dimension and external environmental dimension. 2. To determine the relationship between corporate entrepreneurship dimensions and organizational performance with moderating demographic characteristics 3. To compare the effect of corporate entrepreneurship dimensions towards the financial and non-financial performance of the organization. According to exploratory research design, it is generally used to clarify the thoughts and opinions about the research problem or to provide insight on how to do more conclusive research. Often the researcher is new to problem either because the product is new or the researcher is studying it for the first time. In such cases, the first few studies tend to be exploratory in nature. One of the objective states that have to develop an integrated corporate entrepreneurship model which means we have to identify the new model whereby the suggested model has not been explored yet.
- 7. QUESTIONNAIRE: A Study on Motivation and Job Performance among Employees of Maya Food Industries SDN.BHD