Author: admin

Types of Study Design

Cross Sectional Studies: The cross sectional studies are simpler and target at discovering the phenomenon, concern, attitude by looking at snap shot view of the cross section of the population. This helps to obtain a more holistic picture which holds relevance with the time in which the study was conducted. To give an example, the … Continue reading “Types of Study Design”

Cross Sectional Studies: The cross sectional studies are simpler and target at discovering the phenomenon, concern, attitude by looking at snap shot view of the cross section of the population. This helps to obtain a more holistic picture which holds relevance with the time in which the study was conducted. To give an example, the use of a cross sectional design would be to have a look at the demographic characteristics of the population. The advantage of these kinds of studies are that they mostly  undertake only one contact with the population under study and the cost involved in undertaking the study is relatively cheaper.

Some studies do pre-test and post-test work towards measuring the change in the situation or the phenomenon that is the focus of the study.  These kinds of studies mostly measure the effectiveness of a programme. These kinds of studies are usually a variation from the conventional cross sectional studies as they take into consideration two sets of data collection which is cross sectional on both sides.  The purpose is to determine if any change has occurred.

 Retrospective Studies: Another kind of study is the retrospective study. The name itself is self-explanatory and as obvious it is used to study a specific phenomenon which has had an occurrence in the past. The probable approach adopted in these studies is the use of secondary data collection which reveals information and data on the previous studies and databases.

Prospective studies: In contrast to retrospective studies, the prospective study design targets to estimate the probability of the occurrence of an event in the coming future. They help to guess what would be the outcome of any event. Usually the general science experiments are of a prospective nature as the experimenter can only know the effect or impact of an experiment only after it has run its course.

Longitudinal Studies: This kind of studies stretch over a longer time period where data is to be collected repeatedly throughout the course of the study. The time span of the longitudinal studies could be from few months to several decades. These kinds of studies help to identify the correlation between the variables and mostly are not able to give out a causal relationship

Categories in Hypotheses

A hypothesis is a tool of quantitative studies. It is a tentative prediction regarding the relationship between the variables which are being studied. The key work that the hypotheses do is that it translates the research question into a prediction of the outcomes that can be expected from it. The entire research is done as … Continue reading “Categories in Hypotheses”

A hypothesis is a tool of quantitative studies. It is a tentative prediction regarding the relationship between the variables which are being studied. The key work that the hypotheses do is that it translates the research question into a prediction of the outcomes that can be expected from it. The entire research is done as an attempt to approve or disapprove the hypotheses.

In order to be complete, it is important that a hypotheses includes these three main components:

  • The variables
  • The Population
  • The relationship

The key features of hypotheses are:

  • Stated clearly  by using  the appropriate terminology
  • Testable
  • It should be a clear about the relationship between the variables
  •  It should be having  definable, limited scope

There is more than one type of hypotheses. They are:

  • Simple Hypotheses: These hypotheses help to predict the relationship between a single independent variable (IV)  on one side and a dependant variable(DV).
  • Complex Hypotheses:  This kind of hypotheses helps to predict the relationship that is there between more than two or two independent variable and likewise two or more than two dependant variable.
  • Directional Hypotheses: These kinds of hypotheses are drawn from theory. These imply that the researcher is committed to a particular kind of outcome. These kind of hypotheses
  • Non-directional Hypotheses: These kinds of hypotheses are used when there is little or no theory or when the findings are contradictory to previous study.  They may have impartial implication and do not stipulate the direction of the relationship.
  • Associative and causal hypotheses:  These kind of hypotheses propose relationships between two variables. In this case when one variable changes the other one also changes.
  • Null Hypotheses: As the name is indicative, they are used when the researcher insists that there is no relationship between the variables or when the empirical data is inadequate to state any kind of hypotheses. Null hypotheses can be simple, complex, causal and associative.
  • Testable Hypotheses: It includes those variables that can be measured or have the capacity to be manipulated. Their task is to predict a relationship on the basis of data.

 

What are Projective Techniques?

Projective techniques are seeked as they help in going to the deep sub consciousness.  The way they work is as follows: All those who are participants in the research that follows this technique, expects them to project their feelings and thoughts onto other things that may not be otherwise apparent.  For instance, if Pepsi was … Continue reading “What are Projective Techniques?”

Projective techniques are seeked as they help in going to the deep sub consciousness.  The way they work is as follows:

All those who are participants in the research that follows this technique, expects them to project their feelings and thoughts onto other things that may not be otherwise apparent.  For instance, if Pepsi was a bird then which bird would it become?  The most common kind of projective techniques used are:

  • Completion of sentences
  • Completion of cartoons
  • Stereotyping
  • Personification of brands

After completing the first step, the participants are expected to explain their answers. This stage in the technique is important and the participant plays a crucial role in fairly answering the “Why” in this question. It is important because the projective techniques work towards releasing the sub conscious and attempting to reveal the real explanation by the means of probing. For example here, if Pepsi is seen as a pigeon by the participant then the explanation in the second stage may be that it is dull, silly and a trend follower rather than a trend creator in the market.

There is a lot of fun element that is involved in the projective techniques.  It is found to be breaking the mundane monotony of the research and researchers, academicians, clients, respondents often look forward to the use of this technique. At the same time, it is very important to take care while using these techniques, particularly with the measurement of abstract things. It may create confusion between the truth and the error. The sub conscious is something that is more deep and profound than the abstract and often researchers are found struggling with new words and people, more than often do not even think about the sub conscious.

The development of projective techniques happened in psychology. The reliability of these techniques has been a concern for the alternate disciplines of research. Another area where projective techniques pick the mainstream is market research. This is largely because the other techniques are not able to generate an insight into the factors that lead to brand preference and buying.

The ambiguity in the projective technique responses can be minimized using these techniques:

  •  The probing technique is very useful
  • Use of multiple techniques to draw out the most common conclusion
  • Combining data of multiple people with same responses.

 

Sampling Vs Non sampling Error

There are two types of error that we may find occurring when the effort is to try and estimate the parameters of the population from the sample. These errors can be classified as sampling and non-sampling errors. Sampling error: This kind of error is often seen arising when the sample of the study does not … Continue reading “Sampling Vs Non sampling Error”

There are two types of error that we may find occurring when the effort is to try and estimate the parameters of the population from the sample. These errors can be classified as sampling and non-sampling errors.

Sampling error: This kind of error is often seen arising when the sample of the study does not represent the population that has to be studied.  To understand better with an example, if the entire population comprises 200 MBA students of a business school and the research focus is to estimate the average height of these 200 students. The sample chosen is, let’s say, 10 students. In this case if we assume that the true mean of the population is known and the analysis show us that there is a wide difference between the sample mean and population mean. This kind of an error falls in the category of a sampling error. The reason for this kind of an error is the chosen sample size. In the above case, a sample of 10 is not a representative of the entire population. If the sample size is increased to 15 the error reduces. A significant increase in the sample size on one side significantly reduces the error on the other side.

Non Sampling error: This error arises because of various reasons. Some of the reasons are:

a)     False or incorrect information given by the respondents may lead to a non-sampling error.  For example, sometimes the respondent may not disclose his correct age and this may bring up a non-sampling kind of error.

b)    Sometimes error arises when the transfer of data is being done onto a spreadsheet, from a manual sheet which is the questionnaire.

c)     There are some errors that may happen at the time of coding or tabulation.

d)    At times, it so happens, that the population of the study is not defined in the correct manner. It leads to errors.

e)      The respondent that the researcher chooses for study, at times refuses to become a part of the study. This also becomes a kind of non-sampling error.

f)      Another type of non-sampling error is the error of the sampling frame. Sometimes, the researcher decides to ignore a certain category of respondents and that may lead to the development of a non-sampling error.

Classifying Experimental Designs

Experimental designs should be categorized with many a variations. They can be classified and organised by understanding the application of the fundamental signal to noise ratio metaphor. This metaphor elucidates that what we see or observe can be split into two basic components. These two components are the signal and the noise. In most of … Continue reading “Classifying Experimental Designs”

Experimental designs should be categorized with many a variations. They can be classified and organised by understanding the application of the fundamental signal to noise ratio metaphor. This metaphor elucidates that what we see or observe can be split into two basic components. These two components are the signal and the noise.

In most of the researches, the signal has its link with the key variable of interest. The noise here comprises the random factors in the situation which make the visibility of the signal in the room relatively poorer. A ratio construct can be created when the signal is divided by the noise.  When one talks of research, the signal should have high relativity to noise. For instance, if the treatment or programme and the measurement is also very good they can be termed as strong signal and low noise. In light of this concept, the experimental designs can also be classified into two categories. They can be termed as signal enhancers or noise reducers. Both these categories work towards enhancing the quality of the research. The first kind which is the signal enhancing experimental design is technically called the factorial designs. In this type of design, the entire focus is on the set up of the programme. It would help to examine and understand the different variations of a treatment.

In the other category, there are two major types of noise reducing experimental designs. They are called the covariance designs and blocking designs. The basic purpose of this kind of a design is to put the sample information and pre programme variables so that some noise from the study is taken out and more precise and worthy analysis can be done