Designing Experiments In Fisheries

 

Introduction

 

Bangladesh is a developing country. Fisheries sector is playing a very vital role regarding employment generation, animal protein supply, foreign currency earning and poverty alleviation. This sector faces several types of problem such as conservation and management, culture, harvesting and exporting. To solve these problems we need research or experiment or programme or project. To activate this type of work we need to knowledge about designing experiments. Experimental Design is necessary because it facilitates the smooth sailing of the various research operations and makes the experiment or research as efficient as possible. It yields maximal information with minimal expenditure of effort, time and money and stands for advance planning of the methods to be adopted for collecting the relevant data and techniques to be used in their analysis, keeping in view the objective of the research or experiment and the availability of staff, time and money. So, for a better, economical and attractive output or result in advance of data collection and analysis for the experiment, we need a blueprint well thought out and prepared by an expert.

 

Materials & Methods

 

The topic was selected for knowing about designing experiment in fisheries. For this purpose at first information related for my study were collected from my distinguished teacher Professor & Head Dr. Khandaker Anisul Huq, FMRT Discipline of Khulna University, Khulna. Then literatures were collected from the seminar library of FMRT Discipline of Khulna University, Khulna, Bangladesh.

 

Literatures of this assignment were collected from central library of Khulna University, Bangladesh.

 

Information about this assignment was collected from various web sites.

 

Result & Discussion

 

Design

 

Design is used both as a noun and a verb. The term is often tied to the various applied arts and engineering. As a verb, “to design” refers to the process of originating and developing a plan for a product, structure, system, or component with intention. As a noun, “a design” is used for either the final (solution) plan (e.g. proposal, drawing, model, description) or the result of implementing that plan in the form of the final product of a design process.

Experiment

 

An experiment is an attempt to test a hypothesis about nature. In scientific inquiry, an experiment (Latin: ex- periri, “to try out”) is a method of investigating particular types of research questions or solving particular types of problems. The experiment is a cornerstone in the empirical approach to acquiring deeper knowledge about the world and is used in both natural sciences as well as in social sciences. An experiment is defined, in science, as a method of investigating less known fields, solving practical problems and proving theoretical assumptions.

 

Fishery

 

Generally, a fishery is a unit, engaged in raising and/or harvesting fish, which is determined by an authority or other entity to be a fishery. Typically, the unit is defined in terms of the following: people involved species or type of fish, area of water or seabed, method of fishing, class of boats and purpose of the activities.

 

In particular, the term is often applied to a combination of fish and fishers in a region, the latter fishing for similar species with similar gear types.

 

A fishery may involve the capture of wild fish or raising fish through fish farming or aquaculture.

 

Design of experiments

 

Design of experiments, or experimental design, is the design of all information-gathering exercises where variation is present, whether under the full control of the experimenter or not. (The latter situation is usually called an observational study.) Often the experimenter is interested in the effect of some process or intervention (the “treatment”) on some objects (the “experimental units”), which may be people. Design of experiments is thus a discipline that has very broad application across all the natural and social sciences.

 

Experimental design is a term describing the logical structure of an experiment.

 

The design includes an outline of what researcher will do from writing the hypothesis and its operational implications to the implication of data. More explicitly, the daring decisions happen to be in respect of:

 

1) What is the study about?

2) Why is the study being made?

3) Where will the study be carried out?

4) What type of data is required?

5) Where can the required data be found?

6) What periods of time will the study include?

7) What will be the sample design?

What techniques of data collection will be used?

9) How gill the data analyzed?

10) In what style will the report be prepared?

Keeping in view the above stated design decisions; one may split the overall research design or experimental design into the following parts:

 

(a) the sampling design which with the method of selecting items to be observed for the given study;

 

(b) the observational design which relates to the conditions under which the observations are to be made;

 

(c) the statistical design which concerns with the question of how many items are to be observed and how the information and data gathered ors to he analyzed; and

 

(d) the operational design which deals with the techniques by which the procedures specified in the sampling, statistical and observational designs can be carried out.

 

From what has been stated above, we can state the important features of a research design as under:

(i) It is a plan that specifies the sources and types of information relevant to the research problem.

 

(ii) Itisastrategyspecifyingwhichapproachwillbeusedforgathetingandanalysingthcdata.

 

(iii) It also includes the time and cost budgets since most studies ate done under these two constraints.

 

In brief, research design must, at least, contain-

 

(a) a clear statement of the research problem;

(b) procedures and techniques to be used for gathering information;

(c) the population to be studied; and

(d) methods to be used in processing and analyzing data.

 

Need for research design or experimental design

• Research design is needed because it facilitates the smooth sailing of the various research operations, thereby making research at efficient as possible yielding maximal information with minimal expenditure of effort, time and money.
  
• Just as for better, economical and attractive construction of a house, we need a blueprint or what is commonly called the map of the house) well thought out and prepared by an expert architect, similarly we need a research design or a plan in advance of dart collection and analysis for our research project.
  
• Research design stands for advance planning of the methods to be adopted fur collecting the relevant data and the techniques to be used in their analysis, keeping in view the objective of the research and the availability of sea time and money.
  
• Preparation of the research design should be done with great care as any error in it may upset the entire project.
  
• Research design, in fact, has a great bearing on the reliability of the results arrived at and as such constitutes the firm foundation of the entire edifice of the research work.
  
• Even then the need for a well thought out research design is at times not realized by many.
  
• The importance which this problem deserves is not given to it.
  
• As a result many researches do not serve the purpose for which they arc undertaken.
  
• In fact, they may even give misleading conclusions.
  
• Thoughtlessness in designing the research project may result in rendering the research exercise futile.
  
• It is, therefore, imperative that an efficient and appropriate design must be prepared before starting research operations.
  
• The design helps the researcher to organize his ideas in a form whereby it will be possible for him to look for flaws and inadequacies. Such a design can even be given to ashes for their comments and critical evaluation.
  
• In the absence of such a course of action, it will be difficult for the critic to provide a comprehensive review of the proposed study.
  

Features of a good design

• A good design is often characterized by adjectives like flexible, appropriate, efficient, economical, and so on. Generally, the design which minimizes bias and maximizes the reliability of the data collected and analyzed is considered a good design.
  
• The design which gives the smallest experimental error is supposed to be the best design in many investigations.
  
• Similarly, a design which yields maximal information and provides an opportunity for considering many different aspects of a problem is considered most appropriate and efficient design in respect of many research problems.
  
• Thus, the question of good design is related to the purpose or objective of the research problem and also with the nature of the problem to be studied.
  
• A design may be quite suitable in one case, but may be found wanting in one respect or the other in the context of some other research problem.
  

One single design cannot serve the purpose of all types of research problems.

A research design appropriate for a particular research problem, usually involves the consideration of the following factors:

(i) the means of obtaining information;

(ii) the availability and skills of the researcher and his staff, if any;

(iii) the objective of the problem to be studied;

(iv) the nature of the problem to be studied; and

(v) the availability of lime and money for the research work.

• If the research study happens to be an exploratory or a formulative one, wherein the major emphasis is on discovery of ideas and insights, the research design most appropriate must be flexible enough to permit the consideration of many different aspects of a phenomenon.
  
• But when the purpose of a study is accurate description of a situation or of an association between variables (or in what arc called the descriptive studies), accuracy becomes a major consideration and a research design which minimizes bias and maximizes the reliability of the evidence collected is considered a good design.
  
• Studies involving the testing of a hypothesis of a causal relationship between variables require a design which will permit inferences about causality in addition to the minimization of bias and maximization of reliability.
  
• But in practice it is the most difficult task to put a particular study
  in a particular group, for a given research may have in it elements of two or more of the functions of different studies.
  
• It is only on the basis of its primary function that a study can be categorized either as an exploratory or descriptive or hypothesis-testing study and accordingly the choice of a research design may be made in case of a particular study.
  
• Besides, the availability of time, money, skills of the research staff and the means of obtaining the information must be given due weight age while working out the relevant details of the research design such as experimental design, survey design, sample design and the like.
  

 

 

 

 

 

 

 

 

 

 

 

General Layout for an Experimental Design Diagram

 

TITLE

The Effect of _______________________________________ (Independent Variable)

on _________________________________________________ (Dependent Variables)

HYPOTHESIS

If _______________________________ (planned change in independent variable),

then ____________________________ (predicted change in dependent variables).

INDEPENDENT VARIABLE

___________________________________________________________________

LEVELS OF INDEPENDENT VARIABLE AND NUMBERS OF REPEATED

TRIALS

 

DEPENDENT VARIABLE AND HOW MEASURED

___________________________________________________________________

CONSTANTS

1.

2.

3.

4.

 

 

 

 

 

 

 

 

Important concepts relating to research design or experimental design

 

Before describing the different research design, it will be appropriate to explain the various concepts relating to designs so that these may be better and easily understood.

 

1. Dependent and independent variables: A concept which can take on different quantitative values is called a variable. As such the concepts like weight, height, income arc all examples of variables. Qualitative phenomena (to the attributes) arc also quantified on the basis of the presence or absence of the concerning attribute(s).

 

Phenomena which calls take of quantitatively different values even in decimal points are called continuous variables.

 

But all variable are not continuous. If they can only be expressed in integer values, they are non-continuous variables or in statistical language ‘discrete variables’. Age is an example of continuous variable, but the number of children is an example of non-continuous variable.

 

If one variable depends upon or is a consequence of the other variable, it is termed as a dependent variable, and the variable that is antecedent to the dependent variable is termed as an independent variable. For instance, if we say that height depends upon age, then height is a dependent variable and age is an independent variable. Further, if in addition to being dependent upon age, height also depends upon the individual’s sex, then height is a dependent variable and age and sex are independent variables. Similarly, readymade films and lectures are examples of independent variables, whereas behavioral changes, occurring as a result of the environmental manipulation, are examples of dependent variables.

 

2. Extraneous variable: Independent variables that are to related to the purpose of the study, but may affect the dependent variable are termed as extraneous variables. Suppose the researcher wants to test du hypothesis that there is a relationship between children’s gains in social studies achievement and their concepts. In this case self-concept is an independent variable and social studies achievement is a dependent variable. Intelligence may as well affect the social studies achievement, bees since it is not related to the purpose of the study undertaken by the researcher, it will be tented as an extraneous variable. Whatever effect is noticed on dependent variable as a result of extraneous variables) is technically described as an ‘experimental error’. A study must always be so designed that the effect upon the dependent variable is attributed entirely to the independent variable(s), and not to some extraneous variable or variables.

 

3. Control: One important characteristic of a good research design is to minimize the influence or effect of extraneous variable(s). The technical term ‘control’ is used when we design the study minimizing the effects of extraneous independent variables. In experimental researches, the term ‘control’ is used to refer to restrain experimental conditions.

 

4. Confounded relationship: When the dependent variable is not free from the influence of extraneous variable(s), the relationship between the dependent and independent variables is said to be confounded by an extraneous variable(s).

 

5. Research hypothesis: When a prediction a hypothesized relationship is to be tested by scientific methods, it is termed as research hypothesis. The research hypothesis is a predictive statement that relates an independent variable to a dependent variable. Usually a research hypothesis must contain, at least, one independent and one dependent variable. Predictive statements which are not to be objectively verified or the relationships that are assumed but not to be tested are not termed research hypotheses.

 

6. Experimental and non-experimental hypothesis-testing research: When the purpose of research is to test a research hypothesis, it is termed as hypothesis-testing research. It can be of the experimental design or of the non-experimental design. Research in which the independent variable is manipulated is tensed ‘experimental hypothesis-testing research’ and a research in which an independent variable is not manipulated is called non-experimental hypothesis-testing research. For instance, suppose a researcher wants to study whether intelligence affects reading ability for a group of students and for this purpose he randomly selects 50 students and tests their intelligence and reading ability by calculating the coefficient of correlation between the two sets of scores. This is an example of non-experimental hypothesis-testing research because herein the independent variable, intelligence, is not manipulated. But now suppose that our researcher randomly selects 50 students from a group of students who are to take a course in statistics and then divides them into two groups by randomly assigning 25 to Group A, the usual studies programme, and 25 to Group B, the special studies programme.

 

At the end of the course, he administers a test to each group in order to judge the effectiveness of the training programme on the student’s performance-level. This is an example of experimental hypothesis-testing research because in this case the independent variable, viz, the type of training programme, is manipulated.

 

7. Experimental and control groups: In m experimental hypothesis-testing research when a group is exposed to usual conditions, it is termed a ‘control group’, big when the group is exposed to some novel or special condition. it is termed an ‘experimental group’. In the above illustration, the Group A can be called a control group and the Group B an experimental group. If both groups A and B arc exposed to special studies programmes, then both groups would be termed ‘experimental groups.’ It is possible to design studies which include only experimental groups or studies which include both experimental and control groups.

 

8. Treatments: The different conditions under which experimental and control groups are put are usually referred to as ‘treatments’. In the illustration taken above, the two treatments are the usual studies programme and the special studies programme. Similarly, if we want to determine through an experiment the comparative impact of three varieties of fertilizers on the yield of wheat, is that case the three varieties of fertilizers will be treated as three treatments.

 

9. Experiment: The process of examining the truth of a statistical hypothesis, relating to wine research problem, is known as an experiment- For example, we can conduct m experiment to examine the usefulness d a certain newly developed drug. Experiments can be of two types viz., absolute experiment and comparative experiment. If we want to determine the impact of a fertilizer on the yield of a crop, it h a cam of absolute experiment; but if we want to determine the impact of one fertilizer as compared to the impact of some other fertilizer, our experiment then will be termed as a comparative experiment Often, we undertake comparative experiments when we talker designs of experiments.

 

10. Experimental unit(s): The pre-determined plots or the blocks, where different treatments are used, are known as experimental units. Such experimental units must be selected (defined) very carefully.

 

Different Experimental Designs

 

Different experimental designs can be conveniently described into the following categories.

• Exploratory Research Design
  
• Descriptive and Diagnostic Research Design
  
• Hypothesis-testing Research Design
  

 

Basic principles of experimental designs

 

Professor Fisher has enumerated three principles of experimental designs:

 

(1) the Principle of Replication;

(2) the Principle of Randomization; and

(3) the Principle of Local Control.

 

1) According to the Principle of Replication, the experiment should be repeated more than once. Thus, each treatment is applied in many experimental units instead of one. By doing so the statistical accuracy of the experiments is increased.

For example, suppose we are to examine the effect of two strains of fish. For this purpose we may select the two ponds and grow one strain one pond and the other variety in the other pond. We can then compare the yield of the two ponds and draw conclusion on that basis. But if we are to apply the principle of replication to this experiment, then we first select several ponds, grow one strain in half of these ponds and the other strain in the remaining ponds. We can then collect the data of yield of the two strains and draw conclusion by comparing the same. The result so obtained will be more reliable in comparison to the conclusion we draw without applying the principle of replication. The entire experiment can even be repeated several times for better results. Conceptually replication does not present any difficulty, but computationally it does. For example, if an experiment requiring a two-way analysis of variance is replicated, it will then require a three-way analysis of variance since replication itself may be a source of variation in the data. However, it should be remembered that replication is introduced in order to increase the precision of a study; that is to say, to increase the accuracy with which the main effects and interactions can be estimated

2) The Principle of Randomization provides protection, when we conduct an experiment, against the effect of extraneous factors by randomization. In other words, this principle indicates that we should design or plan the experiment in such a way that the variations caused by extraneous factors can all be combined under the general heading of “chance.”

For instance, if we grow one strains of fish, say, one strain in a ponds and the other strain other pond, then it is just possible that the primary productivity may be different in one pond in comparison to the other pond. If this is so, our results would not be realistic. In such a situation, we may assign the strain of fish to be grown in different ponds on the basis of some random sampling technique i.e., we may apply randomization principle add protect ourselves against the effects of the extraneous factors (primary productivity differences in the given case). As such, through the application of the principle of randomization, we can have a better estimate of the experimental error.

3) The Principle of Local Control is another important principle of experimental designs. Under it the extraneous factor, the known source of variability, is made to vary deliberately over as wide a range as necessary and this need to be done in such a way that the variability it causes can be measured and hence eliminated from the experimental error.

This means that we should plan the experiment in a manner that we can perform a two-way analysis of variance, in which the total variability of the data is divided into three components attributed to treatments (strains of fish in our case), the extraneous factor (primary productivity in our case) and experimental error. In other words, according to the principle of local control, we first divide the field into several homogeneous parts, known as blocks, and then each such block is divided into parts equal to the number of treatments. Then the treatments are randomly-assigned to these parts of a block. Dividing the field into several homogenous parts is known as ‘blocking’. In general, blocks are the levels at which we hold an extraneous factor fixed, so that we can measure its contribution to the total variability of the data by means of a two-way analysis of variance. In brief, through the principle of local control we can eliminate the variability due to extraneous factor(s) from the experimental error.

 

Important Experimental Designs

 

Experimental design refers to the framework or structure of an experiment and as such theft are several experimental designs. We can classify experimental designs into two broad categories, viz., informal experimental designs and formal experimental designs.

 

Informal experimental designs are draw designs that normally use a less sophisticated form of analysis based on differences in magnitudes, whereas formal experimental designs offer relatively more control and use precise statistical procedures for analysis. Important experiment designs are as follows:

(a) Informal experimental designs:

(i) Before-and-after without control design.

(ii) After-only with control design.

(iii) Before-and-after with control design.

 

(b) Formal experimental designs

(i) Completely randomized design (C.R.Design).

(ii) Randomized block-design (R.B. Design).

(iii) Latin square designs (L.S. Design).

(iv) Factorial designs.

We may briefly deal with each of the above stated informal as well as formal experimental designs.

 

i) Before-and-after without control design

 

In such a design a single test group or area is alerted and (he dependent variable is measured before the introduction of the treatment. The treatment is then introduced and the dependent variable is measured again after the treatment has been introduced. The effect of the treatment would be equal to the level of the phenomenon after the treatment minus the level of the phenomenon before the treatment. The design can be represented thus:

 

Test area: Level of phenomena Treatment Level of phenomenon

Before treatment (X) introduced after treatment (Y)

 

Treatment effect = (Y) – (X)

 

Fig. Before-and-after without control design

 

The main difficulty of such a design is that with the passage of time considerable extraneous variations may be there in its treatment effect.

 

ii) After-only with control design

 

In this design two groups or areas (test area and control area) arc selected and the treatment is introduced into the test area only. The dependent variable is then measured in both the areas at the same time. Treatment impact is assessed by subtracting the value of the dependent variable in the control area from its value in the test area. This can be exhibited in the following form:

 

Test area: Treatment introduced Level of phenomenon

after treatment (Y)

Control area: Level of phenomenon without

treatment (Z)

 

Treatment effect = (Y) – (Z)

 

Fig. After-only with control design

The basic assumption in such a design is that the two areas are identical with respect to their behavior towards the phenomenon considered. If this assumption is not true, there is the possibility of extraneous variation entering into the treatment effect. However, data can be collected in such a design without the introduction of problems with the passage of time. In this respect the design is superior to before-and-after without control design.    

 

iii) Before-and-after wish control design

 

In this design two areas are selected and the dependent variable is measured in both the areas for an identical time-period before the treatment. The treatment is then introduced into the test area only, and the dependent variable is measured in both form identical time-period after the introduction of the treatment The treatment effect is determined by subtracting the change in the dependent variable in the control area from the change in the dependent variable in test area. This design can be shown in this way:

 

Test area: Level of phenomena Treatment Level of phenomenon

Before treatment (X) introduced after treatment (Y)

Control area: Level of phenomena Level of phenomenon

without treatment (A) without treatment (Z)

 

Treatment effect = (Y – X)- (Z-A)

 

Fig. Before-and-after wish control design

 

This design is superior to the above two designs for the simple dawn that it avoids extraneous variation resulting both from the passage of time and from non-comparability of the test and control areas. But at times, due to lack of historical data, time or a comparable control area, we should prefer to select one of the first two informal designs slated above.

 

i) Completely randomized block design (C. R. design)

 

The C. R. design involves only two principles viz., the principle of replication and the principle of randomization of experimental designs. It is the simplest possible design and its procedure of analysis it also easier.

The essential characteristic of the design is that subjects are randomly assigned to experimental treatments (or vice-versa).

For instance, if we have 10 subjects and if we wish to test S under treatment A and 5 artier treatment B, the randomization process gives every possible group of 5 subjects selected from a set of 10 in equal opportunity of being assigned to treatment A and under B.

 

Major Characteristics

• It is the simplest possible design and its procedure of analysis is also easier.
  
• This simple design has the convenience of complete flexibility.
  
• Practically any number of treatments and replications can be used.
  
• Even unequal replications can also work in this design.
  
• One-Way analysis of variance (One-Way ANOVA) is used to analyze such a design.
  
• It provides maximum number of degrees of freedom to the error.
  
• The statistical analysis of the experimental results is easy in this design.
  
• This design is subjected to criticism on the ground of accuracy.
  
• The experimental error is likely to be high in CRD.
  
• CRD is found to be suitable in cases where experimental material is homogeneous, where significant quantities of experimental units are likely to be destroyed and when the experiment is small.
  

 

 

We can present a brief description of the two forms of such a design as given below

 

(a) Two-group simple randomized design: In a two-group simple randomized design, first of all the population is defined and then from the population a sample is selected randomly. Further, requirement of this design is that items after being selected randomly from the population, be randomly assigned to the experimental and control groups (Such random assignment of items to two groups is technically described as principle of randomization). Thus, this design yields two groups as representatives of the population. In a diagram form this design can be shown in this way:

 

 

 

 

 

Fig. Two-group simple randomized experimental design (in diagram form)

 

 

(b) Random replication design: The limitation of the two-group random design is usually eliminated within the random replications design. In the illustration just cited below, the teacher differences on the dependent variable were ignored, i.e. the extraneous variable was not controlled- But in a random replications design, the effect of such differences are minimized (or reduced) by providing a number of repetitions for each tream6t. Each repetition is technically called a “replication”. Random replication design serves two purposes viz., it provides controls for the differential effects of the extraneous independent variables and secondly, it randomizes any individual differences among those conducting the treatments. Diagrammatically we can illustrate the random replications design thus: (Fig. 3.5)

 

 

 

 

 

 

 

Random selection Random selection

 

 

 

 

Random Random

Assignment Assignment

Group 1 E

Group 2 E

Group 3 E

Group 4 E

 

Group 5 C E= Experimental group

Group 6 C C= Control group

Group 7 C

Group 8 C

Treatment A Treatment B

 

 

 

 

Fig. Random replication design in diagram (in diagram form)

 

Application

This design of experiment is quite common in research studies concerning behavioral sciences.

 

 

Merits

• The merit of such a design is that it is simple and randomizes the differences among the sample items.
  
• The designs are very flexible and can be used for any number of treatments, and may have any numbers (not necessarily all the same) of observations in each treatment group.
  
• The statistical analysis is comparatively easy and straightforward. It is moreover unaffected if some or all of the observations for any treatment are lost or missing for some purely random accidental reason, i.e., if the accident is not more likely to happen to one treatment rather than another. We merely carry out the standard analysis on the observations that are available.
  

 

Demerits

• However, in certain circumstances the design suffers from the disadvantage of being inherently less informative than other more sophisticated layouts. If there are large differences between blocks, due say to fluctuations in fertility, the whole of this variation is included in the residual variance, making the usual significance tests less sensitive. It is then better to use the randomized block design described in the next section. With entirely homogeneous material, on the other hand, the completely randomized layout is the most accurate.
  

 

 

Limitations

• The limitation of it is that the individual differences among those conducting the treatments are not eliminated, i.e., it does not control the extraneous variable and as such the result of the experiment may not depict a correct picture.
  

 

Example

For example, if a particular treatment is to be applied in three units the process of randomization gives every combination of three units of experiments an equal probability of receiving a treatment. Further experiment is that in the subsequent processing of the experiment at different stages, randomization process must be followed.

For instances, if we have 20 subjects and if we wish to test 10 under treatment A and 10 under treatment B, the randomization process gives every possible group of 10 subjects selected from a set of 20 an equal opportunity of being assigned to treatment A and treatment B.

 

Illustration

Suppose that a fish nutritionist has the following 3 types of fish feed

1. Feed consisting of protein level 25% (Feed A),
   
2. Feed consisting of protein level 23% (Feed B) &
   
3. Feed consisting of protein level 21% (Feed C)
   

    

and he has to test which type of feed can give better production of fish.

He plans to take 5 replicates of Feed A, 4 replicates of Feed B & 3 replicates of Feed C. For this, he needs 12 experimental units (ponds) to apply these treatments.

 

 

The layout plan of the experiment

 

 

 

 

 

 

ii) Randomized block design (R.B. design)

 

R. B. design is an improvement over the C.R. design. In the R.B. design the principle of local control can be applied along with else other two principles of experimental designs.

In the R.B. design, subjects are first divided into groups, known as books, such that within each group the subjects are relatively homogeneous in respect to some selected variable.

 

Major Characteristics

 

• The variable selected for grouping the subjects is one that is believed to be related to the measures to be obtained in respect of the dependent variable.
  
• The number of subjects in a given block would be equal to the number of treatments and one subject in each block would be randomly assigned to each treatment.
  
• In general, blocks are the levels at which we hold the extraneous factor fixed, so that its contribution to the total variability of data can be measured.
  
• The main feature of the R.B. design is that in this each treatment appears the same number of times in each block.
  
• The R.B. design is analyzed by the two-way analysis of variance (two-way ANDVA)’technique.
  

 

Illustration

Suppose that a fish farmer has the following 4 types of fish seed:

1. Rohu
   
2. Catla
   
3. Mrigal
   
4. Prawn
   

    

and he has to test which stocking density/decimal of fish seed can give better production of fish. He plans to take 3 treatments.

 

The layout plan of the experiment

 

 

 

 

 

 

 

 

iii) Latin squire design (LS. design)

 

L. S. design is an experimental design very frequently used in agricultural research. The conditions under which agricultural investigations are carried out are different from those in other studies for nature plays an important role in agriculture.

 

The treatments in a L.S. design are so allocated among the plots that no treatment occurs more than once in any one row or any one column. The two blocking factors may be represented through rows and columns (one through rows and the other through columns).

 

Major Characteristics

• With the Latin Square design you are able to control variation in two directions.
  
• Treatments are arranged in rows and columns.
  
• Each row contains every treatment.
  
• Each column contains every treatment.
  
• The most common sizes of LS are 5×5 to 8×8
  

 

 

Merits

• You can control variation in two directions.
  
• Hopefully you increase efficiency as compared to the RCBD.
  

 

Demerits

• The number of treatments must equal the number of replicates.
  
• The experimental error is likely to increase with the size of the square.
  
• Small squares have very few degrees of freedom for experimental error.
  
• You can’t evaluate interactions between:
  

1. Rows and columns
   
2. Rows and treatments
   
3. Columns and treatments.
   

 

Illustration

 

The following is a diagrammatic form of such a design in respect of, say, five types of stocking density, viz., A. B, C, D and E and the two blocking factor viz., the varying primary productivity and the species:

 

The layout plan of the experiment

 

 

 

 

 

iv) Factorial design

 

Factorial designs are used in experiments where the effects of varying more than one factor are to be determined. They are specially important in several economic and social phenomena where usually a large number of factors affect a particular problem.

 

Factorial designs can be of two types:

 

(i) simple factorial designs and

(ii) complex factorial designs.

 

(i) Simple factorial design: In case of simple factorial designs, we consider the effects of varying two factors on the dependent variable, but when an experiment is done with more than two factors, we use complex factorial designs. Simple factorial design is also termed as a ‘two-factor-factorial design’, whereas complex factorial design is known as ‘multi-factor-factorial design.’ Simple factorial design may either be a 2 x 2 simple factorial design, or it may be, say. 3 x 4 a 5 x 3 or the like type of simple factorial design. We illustrate some simple factorial designs as under:

 

 

Illustration 1: (2 x 2 simple factorial design)

 

 

Illustration 2: (4 x 3 simple factorial design)

 

 

ii) Complex factorial designs

Experiments with man than two factors at a time involve the use of complex factorial designs. A design which considers three or mare independent variables simultaneously is called a complex factorial design. In eau of three factors with one experimental variable having two treatments and two control variables, each one of which having two levels, the design used will 6e termed 2 x 2 x 2 complex factorial design which will contain a total of eight cells as shown below:

 

Illustration 2: (2x 2 x 2 complex factorial designs)

		Experimental Variable
		Treatment A		Treatment B
		Control Variable 2 Level i	Control Variable 2 Level ii	Control Variable 2 Level i	Control Variable 2 Level ii
Control variable	Level i	Cell 1	Cell 3	Cell 5	Cell 7
	Level ii	Cell 2	Cell 4	Cell 6	Cell 8

 

Merits

• Appropriate for explanatory type of research work.

• Useful for studying the interaction among the effect of several factors.
  

 

Demerits

• If the factors are numerous then the single factorial experiment has the disadvantages of size and complexity.
  
• Difficulty may arise in the interpretation of the result of this design when the large number of treatment is involved.
  

 

Conclusion

 

Experimental design possesses a great bearing on the reliability of the results arrived at the experiment and constitutes the firm foundation of the entire edifice of the research work. It helps the researcher to organize his ideas in a form whereby it will be possible for him to look for flaws and in adequacies. It helps the critic to provide a comprehensive review of the proposed study. A careless preparation of the experimental design may cause a great harm to the research as any error in it may upset the entire project. Thoughtlessness in designing the research project may result in rendering the research exercise futile and may even give misleading conclusions.

 

Reference

 

Khothari, C. R., 1990. Research Methodology. New age international publishers. New Delhi. pp. 401

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