Research Methodology for Dissertation Writing

You can read the following text online by clicking on the link given below and then you will be taken to a page where you can scroll down to see different modules. Click on each individual module and you will see a lot on organized text to help you in understanding what is Health research, how you do it, and How to write your Dissertation.

The link is given below

http://www.idrc.ca/en/ev-33011-201-1-DO_TOPIC.html

Dear Sir Nayer Sahib,

Thanks for uploading this nice book.

Regards.

Ameen

Dear Sir Nayer Sahib,

Thanks for uploading this

nice book.

Regards,

ameen

Thank You Sir,
It is really a Helpful book.

Thanks for uploading very nice Book

Dear Ameen!
Tell me truly,wether the avatar was yours selection or Dr Nayyar Gifted it to You.
Normally such mischief is committed by intellegent people, you got my point?

Rab Ka Shukur Ada Ker Bhai
Jis Nay Hamari Gaiye Banaee


It is beautiful

dear and sweet abuzar sb.
lagi dhaasy che khaa prepare ye exam la paaraa.
dear maa laa kho sa smaj naa razii . bas allah dhe kher okiii..
umeed kaum che zamund khoog and sweet class CR dr abuzar bhaa zmaa help kwee paaa research k. kaaani ka paa khplaaa me research oko no wlaaah che nayyar sb baa warlaaa john hopkin university na research scholar rawali da pohedalo la para.... allah dhe kher okiiii.
kho haga zmaa nor wrouna noroz khan, zia urehman , adnan na khkaaarii paa forum. and dr bangash hum silent dheee..... research laaa para chartaaa abroad kho na dhee thalli ... best of luck for all

Dear Abu zar,

This nice avatar is

gifted by Dear Nayer Sahib.

Truly very nice

Ameen Khana Wrora!

sta da avatar Qeemat pa eidul Azha ke der ziyat wo, no ma bia mekha wakhesta, mekha arzana wa


Latifa! tasu der khe topic khwakh keray day,che class ta rashe no baqi ba ye darla refine kem.
Te bilkul gham me kawa sta capacty kafi kha da, insha Allah te ba ye khe teek tak tar sara ke.
sta sms ma sara day, ao kar ba pe shorokome.

da Allah pe Aman
Sta wror Dr Ab Zar

thanks dear and the great dr abu zar sb..
worora dera dera manana.....
best wishes

Dear Friends!
l am sure you all would be busy in research/ thesis writing.
One of the problems you woud be facing is how much should be the sample size for our study, here are some hints, I have coppied from the net to help my colleagues.
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Sample Size Calculator


This Sample Size Calculator is presented as a public service of Creative Research Systems. You can use it to determine how many people you need to interview in order to get results that reflect the target population as precisely as needed. You can also find the level of precision you have in an existing sample.
Before using the sample size calculator, there are two terms that you need to know. These are: confidence interval and confidence level. If you are not familiar with these terms, click here. To learn more about the factors that affect the size of confidence intervals, click here.
Enter your choices in a calculator below to find the sample size you need or the confidence interval you have. Leave the Population box blank if the population is very large or unknown.

Dear Sir!
Thank You for Rectifying the Post Format.

Survey Research Methods

Survey Research Methods

What makes a survey a survey?

-

Scientific methodology

-

Data collection from an individual

-

Usually samples from a large population

-

Conducted for the purpose of . . .

Description

Exploration

Explanation

Characteristics of Good Survey Research





Self-monitoring

Contemporary

Replicable

Systematic

Impartial

Representative

Theory-based


Types of Survey





Cross

Longitudinal

Trend

Time cohort

Panel




General Sampling Issues





Basic rule—all individuals must have equal chance of being selected

May be more accurate data than a census

If all members of a population were identical, sampling

Not be necessary

Aim for a sample that is generalizable to total population of

Interest


Some Sampling Definitions



Element—unit from which data are collected

Universe—aggregation of all elements

Survey population—population from which you are drawing

sample

Sampling frame—actual list or “hat” from which you select

elements


Probability Sampling Approaches





Simple random sample

-

Random number generator

-

Draw number out of a “hat”



Systematic sample

-

Every element of list

-

Select every “kth”

element

-

Make sure list isn’t “periodic”

Stratified sample

-

Same as for simple random sample

-

However, select from within specific pre-determined

Groupings

-

Insures heterogeneity



Multi-stage sample

-

Select cluster of elements first, for example, physicians

-

Then select elements, for example, patients


Non-Probability Sampling Approaches





Purposive or judgmental

-

Educated guess of representative unit

Purposive or judgmental

-

Educated guess of representative unit

Quota sampling

-

Select any way you want following a pre-set quota

pattern

Available subject sampling

-

Use whomever is available



















search: Choice of Instrument, Sample

Survey Research: Choice of Instrument, Sample

Key Concepts in Determing Sample Size





From a sampling point of view, the key concern is having a

large enough number of cases to minimize the variable

sampling (standard) error in the estimates

Sampling error can be reduced by increasing the sample size

(denominator) or minimizing the random errors in the data

collection process






What Difference Do You Expect?





In experimental designs, there is a particular interest in

determining the effect (or difference) between experimental

and control groups

The effect size, which essentially reflects the hypothesized

difference between groups, provides a basis for calculating

the sample size for these types of designs



Type I and Type II Errors



A Type I error results from falsely rejecting the null hypothesis

when the hypothesis is actually true (alpha)

A Type II error refers to the reverse error—failing to reject the

null hypothesis when it is actually false

The probability of Type I and Type II errors decreases as the

sample size increases, primarily because the estimates

obtained from larger samples are more reliable (have less

random sampling variation)



Criteria for Estimating Sample Size Based on The Study Design



Objective—to test a hypothesis

Framework—power analysis

Steps

1.

Identify the major study hypotheses

2.

Determine the statistical tests for the study hypotheses,

such as a t-test, F-test, or chi-square



Steps

3.

Select the population or subgroups of interest (based on

study hypotheses and design)

4.

Step 4

a.

Indicate what you expect the hypothesized

difference to be

b.

Estimate the standard deviation of the difference

Steps

4.

Step 4

c.

Compute the effect size

5.

Decide on a tolerable level of error in rejecting the null

hypothesis when it is true (alpha) (this is usually set at

.05.)

Steps

6.

Decide on a desired level of power for rejecting the null

hypothesis when it is false (power) (this is usually set at

.80.)

7. Compute sample size, based on study assumption


Examples of Major Study Hypothesis





Proportion of patients (improving in health [by a global

health index measure] and who received the treatment) will

not differ from the proportion who improve and did not

receive the treatment (null hypothesis)

-

Ho P1 -

P2 = 0

Alternative hypothesis

-

Ha P1 -

ne 0

Determing the Statistical Test



Calculation of sample size depends on the study design and

what statistical test you will be using to test the hypotheses

It is possible that you will have several outcomes, each of

which will be determined using a different statistical test

Different sample sizes will be required, depending on the

outcome

Weighting the sample Data to Reflect The Population



You may want to sample some sub groups in the population

at different rates to ensure that there will be enough of these

individuals without having to increase the overall size of the

sample

The sample can be weighted so that it resembles the

population from which it was drawn

Weighting literally involves a process of statistically assigning

more, or less, weight to some groups than others so that their

distribution in the sample corresponds more closely to their

actual distributions in the population as a whole