IGNOU MSW 006 Solved Assignment 2022-23 , MSW 006 Social Work Research Solved Assignment 2022-23 Download Free : MSW 006 Solved Assignment 2022-23 , IGNOU MSW 006 Assignment 2022-23, MSW 006 Assignment 2022-23 , MSW 005 Assignment , MSW 006 Social Work Research Solved Assignment 2022-23 Download Free IGNOU Assignments 2022-23- MASTER DEGREE IN SOCIAL WORK Assignment 2022-23 Gandhi National Open University had recently uploaded the assignments of the present session for MASTER DEGREE IN SOCIAL WORK Programme for the year 2022-23. MSW course or Master of Social Work is a post-graduation course majoring in the field of social work. MSW course is imparted with a two-year duration, which is typically divided into four semesters. Aspirants can pursue MSW courses after completing a Bachelor degree in the relevant field. A career in social work is all about giving and helping others in need. From various NGOs (non-government organizations) across the nation to social development, a Master of Social Work (MSW) course provides comprehensive knowledge about the work put into the development of humanity and social welfare. Students are recommended to download their Assignments from this webpage itself. IGNOU solved assignment 2022-23 ignou dece solved assignment 2022-23 ignou ma sociology assignment 2022-23 meg 10 solved assignment 2022-23 ts 6 solved assignment 2022-23 , meg solved assignment 2022-23 .
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IGNOU MSW 006 Solved Assignment 2022-23
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Important Note – IGNOU MSW 006 Solved Assignment 2022-23. You may be aware that you need to submit your assignments before you can appear for the Term End Exams. Please remember to keep a copy of your completed assignment, just in case the one you submitted is lost in transit.
Submission Date :
- 31st March 2033 (if enrolled in the July 2033 Session)
- 30th Sept, 2033 (if enrolled in the January 2033 session).
Answer all the five questions.
All questions carry equal marks.
Answers to question no. 1 and 2 should not exceed 600 words each.
Q.1. What is experimental research? Discuss the significance of Control manipulation and observation is an experimental study.
Explain various stages of research process.
Experimental research uses a scientific method for conducting research, employing the most methodical research design. Known as the gold standard, it involves performing experiments to reach conclusions and can be conducted based on some of the findings from previous forms of research.
Logically, it would follow correlational research, which studies the relationships between variables. It can also follow causal research, a kind of experimental research in itself, as it establishes cause and effect relationships between previously studied variables.
Experimental research is typically used in psychology, physical and social sciences, along with education. However, it too can be applied to business.
This article expounds on experimental research, how it is conducted, how it differs from other forms of research, its key aspects and how survey studies can complement it.
Defining Experimental Research
Experimental research is a kind of study that rigidly follows a scientific research design. It involves testing or attempting to prove a hypothesis by way of experimentation. As such, it uses one or more independent variables, manipulating them and then using them on one or more dependent variables.
In this process, the researchers can measure the effect of the independent variable(s) on the dependent variable(s). This kind of study is performed over some time, so that researchers can form a corroborated conclusion about the two variables.
The experimental research design must be carried out in a controlled environment.
Throughout the experiment, the researcher collects data that can support or refute a hypothesis, thus, this research is also referred to as hypothesis testing or a deductive research method.
The Key Aspects of Experimental Research
There are various attributes that are formative of and unique to experimental research in addition to its main purpose. Understanding these is key to understanding this kind of research in-depth and what to expect when performing it.
The following enumerates the defining characteristics of this kind of research:
- It includes a hypothesis, a variable that will be manipulated by the researcher along with the variable that will be measured and compared.
- The data in this research must be able to be quantified.
- The observation of the subjects, however, must be executed qualitatively.
- It can be conducted in a laboratory in field settings, i.e., field research.
- The latter is rarer, as it is difficult to manipulate treatments and to control external occurrences in a live setting.
- It relies on making comparisons between two or more groups (the variables).
- Some variables are given an experimental stimulus called a treatment; this is the treatment group.
- The variables that do not receive a stimulus are known as the control group.
- First, researchers must consider how the variables are related and only afterward can they move on to making predictions that can be tested.
- Time is a crucial component when putting forth a cause-and-effect relationship.
- There 3 types of experimental research:
- Pre-experimental research design
- True experimental research design
- Quasi-experimental research design
The Three Types of Experimental Research
Experimental research encompasses three subtypes that researchers can implement. They all fall under experimental research, differing in how the subjects are classified. They can be classified based on their conditions or groups.
Pre-experimental research design:
This entails a group or several groups to be observed after factors of cause and effect are implemented.
- Researchers implement this research design when they need to learn whether further investigation is required for these particular groups.
- Pre-experimental research has its own three subtypes:
- One-shot Case Study Research Design
- One-group Pretest-posttest Research Design
- Static-group Comparison
Quasi-experimental Research Design
Representing half or pseudo, the moniker “quasi” is used to allude to resembling true experimental research, but not entirely.
- The participants are not randomly assigned, rather they are used when randomization is impossible or impractical.
- Quasi-experimental research is typically used in the education field.
- Examples include: the time series, no equivalent control group design, and the counterbalanced design.
True Experimental Research Design
This kind of experimental research design studies statistical analysis to confirm or debunk a hypothesis.
- It is regarded as the most accurate form of research.
- True experimental research can produce a cause-effect relationship within a group.
This experiment requires the fulfillment of 3 components:
- A control group (unaltered) and an experimental group (to undergo changes in variables)
- Random distribution
Variables can be manipulated
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2. Define sampling. Highlight the different methods of probability sampling.
Discuss the concept of measurement in social research.
Sampling Methods | Types, Techniques & Examples
When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.
To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:
Probabilitysampling involves random selection, allowing you to make strong statistical inferences about the whole group.
- Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.
You should clearly explain how you selected your sample in the methodology section of your paper or thesis.
Population vs sample
First, you need to understand the difference between a population and a sample, and identify the target population of your research.
- The population is the entire group that you want to draw conclusions about.
- The sample is the specific group of individuals that you will collect data from.
The population can be defined in terms of geographical location, age, income, and many other characteristics.
It can be very broad or quite narrow: maybe you want to make inferences about the whole adult population of your country; maybe your research focuses on customers of a certain company, patients with a specific health condition, or students in a single school.
It is important to carefully define your target population according to the purpose and practicalities of your project.
If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.
The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
Example: Sampling frameYou are doing research on working conditions at Company X. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database which lists the names and contact details of every employee.
The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis.
Probability sampling methods
Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.
There are four main types of probability sample.
1. Simple random sampling
In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.
To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.
Example: Simple random samplingYou want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.
2. Systematic sampling
Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.
Example: Systematic samplingAll employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.
If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.
3. Stratified sampling
Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.
To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role).
Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.
Example: Stratified samplingThe company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.
4. Cluster sampling
Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.
If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling.
This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.
Example: Cluster samplingThe company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.
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Non-probability sampling methods
In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.
This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.
Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.
1. Convenience sampling
A convenience sample simply includes the individuals who happen to be most accessible to the researcher.
This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results.
Example: Convenience samplingYou are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.
2. Voluntary response sampling
Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).
Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.
Example: Voluntary response samplingYou send out the survey to all students at your university and a lot of students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.
3. Purposive sampling
This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.
It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.
Example: Purposive samplingYou want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.
4. Snowball sampling
If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people.
3)Answer any two of the following questions in about 300 words each:
a) Describe briefly the different forms of hypothesis with examples.
b) Explain various types of documents.
c) Briefly discuss the recent trends in social work research.
d) Define evaluation research. Describe the types of evaluation research designs.
4) Answer any four of the following questions in about 150 words each:
a) Describe the different research methodologies used in social work.
b) Explain the measures of difference.
c) Enlist the characteristics of a good questionnaire.
d) Mention various types of observation and the steps involved in them.
e) What is bivariate analysis?
f) Explain stratified sampling.
5) Write short notes on any five of the following questions in about 100 words each:
a) Operationalization of concepts
b) Representative sample
d) Meaning of mode
e) Participatory research
f) Attitude scale
g) Role of NGOs in research
h) Style of referencing
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IGNOU MSW 006 Solved Assignment 2022-23 Download PDF Before attempting the assignment, please read the following instructions carefully.
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