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PREVIOUS LECTURES DELIVERED.1

EXPLORATORY

LITERATURE SURVEY EXPERT SURVEY CASE STUDY

DISCRIPTIVEAND

DIAGNOSTIC

EXPERIMENTALDESIGN

PRE EXPERIMENTAL TRUE EXPERIMENTAL QUASI EXPERIMENTAL STATISTICAL

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QUASI-EXPERIMENTS: TIME SERIESDESIGNS

O1 O2 O3 O4 T O5 O6 O7 O8

Pre-observations to establish a baseline A treatment intervention

Post-observations to establish new baseline

EX

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MULTIPLE TIME SERIES3

The basic differencen between this design and earlier is that with anexperimental group which is exposed to treatment, a control isintroduced.

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STATISTICAL DESIGN

Statistical design permit the researcher to measureand eliminate the effect of extraneous variables. Instatistical design BLOCKING FACTOR is introduced.It is the extraneous variable which researcher is ableto isolate and eliminate its effect.

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STATISTICAL DESIGN TYPES

1) Completely randomized design( C. R Design)

- Involves two principles:- principle of replication andprinciple of randomization.

-Subjects are randomly assigned to experimental treatments

- Simplest possible formal design

- One way ANOVA-

- Applied for uncontrolled extraneous factors.

- Eg: 10 test units and two treatment A and B. we want togive treatment to 5.Every possible group of 5 subjects areselected.

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TYPES OF CRD

I Two group simple randomized design

- Selection of experimental and control group randomly.

- Two groups are then subject to different treatments.

- It does not control the extraneous variable

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DIAG. FORM7

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Conti.

II Random Replication Design.- Extension to the two group simple randomized design, incorporation of

repetition.

- Extraneous variable controlling.

- Each group is assigned equal no of items.- Test unit and administer of treatment is also randomly selected.

- EX

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DIAG. FORM9

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STATISTICAL DESIGN

2) Randomized Block design ( R. B. design)

- Improvement over C. R. design.

- Principle of Local control can be applied with the other two

principles of experimental design.- Subjects are first divided into groups or blocks.

- No of subjects in blocks = No of treatments

- Each treatment appears same no of time in each block.

- One subject in each block is randomly assigned totreatment.

- Use of ONE WAY ANOVA and F test

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EXAMPLE

Eg: Suppose four different forms of a standardised test in statistics weregiven to each of five students (selected one from each of the five I.Q.blocks) and following are the scores which they obtained.

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LATIN SQUARE DESIGN

Very frequently used in agricultural research.

LS is used when there are two major extraneousfactors.

For example if we want to judge the effect of fivedifferent varieties of fertilizers on the yield of wheat.

The two extraneous factors here are fertility of landand varying seeds.

Field is divided into 5*5 parts and each extraneousfactor is taken at one axis

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LATIN SQUARE DESIGN14

The following is a diagrammatic form of such a design in respect of, say, five

variety of cosmetic creams and the

The above diagram clearly shows that in a L.S. design the field is divided into as

many blocks as there are varieties of fertilizers and then each block is again divided

into as many parts as there are varieties of fertilizers in such a way that each of the

fertilizer variety is used in each of the block (whether column-wise or row-wise) only

once. The analysis of the L.S. design is very similar to the two-way ANOVA technique

liking/variety A B C D E

X1 O P B C FX2 P B O F C

X3 B C F P O

X4 C F P O B

X5 F O C B P

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EVALUATION15

The merit of this experimental design is that itenables differences in fertility gradients in the fieldto be eliminated in comparison to the effects ofdifferent varieties of fertilizers on the yield of thecrop

But this design suffers from one limitation, and it isthat although each row and each column represents

equally all fertilizer varieties, there may beconsiderable difference in the row and columnmeans both up and across the field.

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FACTORIAL DESIGN

This method is used in experiments where the effectsof varying more than one factor are to bedetermined

This is specially useful in several economic and socialphenomena where there are large number of factorsaffect a particular problem.

Two types

I simple factorial design - Effect of varying twofactors on the dependent variable

II Complex factorial design more than two factors.

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SIMPLE FACTORIAL DESIGN

Two variables control variable and experimental variable. Extraneous variable to be controlled by homogeneity is called control

variable and the independent variable ,which is manipulated is calledexperimental group

Four cells in which sample is divided.Experimental variable

treat A Treat BControl

Variable level1 I IIlevel 2 III IV

- Randomly assigned and means are obtained for control variable andExperimental variables.- One can examine the interaction between treatment and

level. This enables researcher to evaluate the combined effect or theinteraction effect of two or more variables simultaneously

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EXAMPLE

Training

Treat A Treat B

Control level I (Low) 15 23 19

(Intelligence) level II (High) 35 30 32.525 26.5

Control

level(Intelligence)

Treatments

Treatment and level are

dependent on each other

from graph

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Control(intelligence)

treatment

No relationship betweentreatment and

intelligence

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II COMPLEX FACTORIAL DESIGN

Experiments with more than two factors at a timeinvolves the use of complex factorial design.

Treatment and control variable both have differentlevels.

Experimental variableTreat A Treat B

level 1 level2 level 1 level 2

Control level1 I III IV VII

Variable level2 II IV VI VIII

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To determine the main effects of the experimentalvariable, the researcher must necessarily combinedmean of Cell I, II, III, IV

Advantages of factorial design- They provide equivalent accuracy

- Economic

- The determination of interaction effects is possible incase of factorial design.

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