Methods Of Research
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METHODS OF RESEARCH Res. 1E METHODS OF RESEARCH Res. 1E
Ana L. Vargas RESEARCH METHODOLOGY Assistant Professor 2
Prof. Ana L. Vargas
Research Methodology
Sampling
Sampling
Process of selecting a required number of individuals from the study population so as to focus on the sample and not in the whole population.
Principle of sampling
To get the maximum information about the population with minimum effort and with limited resources
Why do we need Sampling
Studying the entire population is difficult.
It will be costly, time consuming and not feasible
Studying the whole population is impossible and unnecessary.
If sampling is done properly: Accurate and reliable estimates can be made More details/characteristics can be collected Can get best results in least possible time Project management is easy
Characteristics of Sampling
Representativeness
Unbiased selection
Adequacy of sample.
Characteristics of Sampling
Representativeness
Sample has all the important characteristics and similar distribution
Unbiased selection
Underrepresented or overrepresented members of the population
Due to faulty selection of sample ot faulty demarcation of sampling units
Adequacy of sample.
Results in reduction cost and saves time
Advantages of Sampling
Provides a more comprehensive information.
Saves time and effort.
Provides a wider scope of study and interests.
Accurate with the data gathered in total population.
Sampling terms
Population (universe)
Group of individuals or units possessing certain predetermined characteristics intended for the study.
Representative sample
It has all the characteristics with similar distribution as that of the population from which it is drawn
Sampling terms
Sampling frame
It is the list of all elements (persons, households, objects, specific events or units) in the population. e.g. voter’s list
Sampling unit
the constituent elements of a population which are to be sampled from the population and cannot be further subdivided for the purpose of sampling at a time. e.g. town, village, district
Sampling terms
Sampling fraction
Sample size
Proportion of population that is included in the sample. e.g. 20% the number of units in a sample
Sampling error
Any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size
Sampling
Applicable to studies with very high population.
Uses the whole population as the source of information.
The study is tedious and impractical for it wastes a lot of time, effort and money if data will be gathered to all population.
Population
Population is a group individuals to which the study is applied.
Population is limited by place or locality
Population describes who your population.
Examples
All students at Partido State University. Government employees in the Bicol Region Teachers teaching science in public high schools in the Division of Camarines Sur
Three Types of Population
Target Population - the actual population to which the researcher is unable to generalize.
Accessible (source)Population is the population in which the researcher can make a generalization.
Sampling (study) Population is the population from which the sample will be actually taken as determined by the sampling frame.
Population Distribution
Illustration #1
Problem: The effects of Computer Aided Instruction to the Academic Performance of Students in Chemistry.
Target Population: All Chemistry students.
Accessible Population: All Chemistry students at PSU.
Sample Population: Ten percent (10%) of the total Chemistry students at PSU.
Illustration #2
The Attitudes and Perceptions of Public School Teachers Towards In Service Trainings and Seminars.
Target Population: All Public School Teachers
Accessible Population : All Public School Teachers in the Division of Camarines Sur
Sample Population: Two hundred fifty school teachers selected from the division of Camarines Sur.
Sample vs. Population
Population
Sample
Two Types of Sampling
Random Sampling (Probability)
Non random sampling-
Random (probability) Sampling
Random Sampling (Probability)
every member of the population are given equal chance of being selected.
Best among all the methods Most powerful statistical analysis on the results can be done subsequentlyCan be done by drawing lots to avoid bias.
Random (probability) Sampling
Include:
Simple random sampling (unrestricted)
Systematic random sampling (quasi-random)
Stratified random sampling
Cluster sampling (area sampling)
Multistage sampling
Important and frequently used methods
RANDOM SAMPLING
Simple Random Sampling
Simple Random Sampling
Define the study population ( N ) Prepare a proper sampling frame (n) Determine the sample size Select the required number of samples Done by :
Lottery method – small population Random number method – by using standard tables ( Tippet’s table, Fisher and Yate’s table and Kendall and Smith’s table ) Computer generated random numbers
Lottery Procedure 1. Write down the name of each member of the population on pieces of paper. 2. Place these papers in a box or a container drum.
3. The box or lottery drum must be shaken thoroughly to prevent some pieces of paper from sinking at the bottom. 4. Picked the required number of sampleunits from the lottery drum.
Simple Random Sampling
Example Population
1 6 11 16
2 7 12 17
3 8 13 18
4 9 14 19
Sample
5 10 15 20
3
6
9
12 15 18
Advantages
Personal bias is eleminated Representative of a homogenous population No need for thorough knowledge of the units of population Accuracy of the sample can be tested Used in other methods of sampling
Disadvantages
Cannot be used for large population When there is large difference between units Cost and time of collection of data are more Logistically more difficult in field conditions
Systematic Random Sampling
Systematic Random Sampling
Simple & convenient way of selecting a sample Requires less time and cost Sample is spread evenly over entire reference population Can be used in infinite population requires sampling frame Units are selected at an uniform interval Useful when information is collected from units which are in serial order (i.e.) entries in register, house in blocks etc.
Systematic Random Sampling
Done by:
Identify the sample size (n)
Put the population in sequential order & number them serially – sampling frame
Identify total no.of units in the population (N)
Divide N/n = sampling interval (k)
Identify a random no.which is less than or equal to ‘k’
Select every n’th item starting with a random one
Stratified Random Sampling
Stratified Random Sampling (SRS)
Dividing the population into subgroups or strata - stratification
Units within the stratum are homogenous and between the strata are heterogeneous
From each stratum a simple random sample is selected and combined together to form the required sample from the population
Two types of SRS
Proportional stratified random sampling
Unequal size
Disproportionate stratified random sampling
Equal size
Proportional stratified sampling • cases are drawn from each strata in proportion to their prevalence in the population.
Advantages of SRS
Every unit in the stratum has the same chance of being selected More representative Ensures proportionate representation Greater accuracy Greater geographical concentration
Disadvantages of SRS
Division of population into strata needs more money, time and statistical experience Improper stratification leads to bias – if there is overlapping of strata
Certain Subgroups or strata are selected. Strata
Population
%
Sample Size
Upper Class
200
30%
60
Middle Class
400
40%
160
Lower Class
200
30%
60
Learning check A survey to find out if families living in a certain municipality are in favor of Charter change will be conducted. To ensure that all income groups are represented, respondents will be divided into high income (class A), middle-income (class B) and low income (class C). Strata High- income (Class A) Middle-income Cclass B) Low-income (Class C)
No. of families 1 000 2 500 1 500 N= 5 000
Learning check 1. Using a 5% margin of error, how many families should be include in the sample? 2. Using proportional allocation, how many from each group should be taken as samples? 3. Using equal allocation, how many from each group should be taken as samples?
Learning check Strata
No. of families
Class A
1 000
Class B
2 500
Class C
1 500 N= 5 000
Proportional Equal allocation allocation (%)
Cluster Sampling
The whole population is divided into groups called clusters.
Each cluster is representative of the population
Clusters are selected randomly
A random sample is then is taken from within each cluster
Lot of clusters are sampled so that the results can be generalized for whole population
Cluster Sampling
Clusters should be as small a possible consistent with the time & cost limitations
No. of units in each cluster must be more or less equal
Advantages
Cuts down the cost of preparing sampling frame and cost of travelling between selected units Eliminates the problem of “packing”
Disadvantages
Sampling errors is usually higher than for a simple Random sample of the same size
Cluster Sampling Example WHO 30 clusters for coverage evaluation survey Pulse polio immunization coverage evaluation survey In a PHC estimate the proportion of infants with age 6 months to 1 yr who are fully immunized .
Cluster Sampling Example In a PHC estimate the proportion of infants with age 6 months to 1 yr who are fully immunized . 1. Identification of total population and the geographical area 2. Identification of age group to be included 3. Listing of all villages 4. Tabulation
Cluster Sampling Selection of groups or clusters of subjects rather than individuals A A
B
C C
Population E D
E
F Sample
Cluster sampling
Learning check
You want to determine the average expenses of families living in the province of Camarines Sur. In sum, there are 208 Brgys. In four districts of Camarines Sur. What should be the sample size?
Multi-Stage sampling
Used for large and diverse populations (eg) nation, region or state
Usually carried out in phases
Involves more than one sampling methods
Example : Estimating the problem of Iodine deficiency disorders in India
Multi-Stage sampling
First stage : few states are randomly selected
Second stage : few districts from above states
Third stage : few blocks from above districts
Fourth stage : few villages from above districts
Fifth stage : few households from each village
Multi-Stage sampling ADVANTAGES : Sample frame for individual units not required Cuts down the cost of preparing sample frame DISADVANTAGES : final sample may not be representative of the total population Sampling error is increased, when compared with simple random sampling
Multi-Stage Random Sampling
A combination of cluster random with simple random sampling. ABC
DEF
GHI
DEF KLM
KLM
NOP
QRS
QRS
ELR
NON-PROBABILITY SAMPLING
Non-Random Sampling
Does not provide equal chance for every member to be selected.
May lead to unrepresentative samples
It lacks accuracy in view of selection bias
May not represent the population well
Used when there is no sample frame for the population
Mostly used in qualitative research like exploratory research, opinion surveys and marketing studies
Non-Random Sampling
Include:
Purposive sampling (judgemental sampling)
Convenience sampling (oppurtunity sampling)
Quota sampling
Expert opinion sampling
Snowball sampling (chain sampling, chain referral
sampling or referral sampling)
Non-Random Sampling
Include:
Purposive sampling (judgemental sampling)
Convenience sampling (oppurtunity sampling)
Quota sampling
Expert opinion sampling
Snowball sampling (chain sampling, chain referral
sampling or referral sampling)
Convenience(Accidental) Sampling
Accidental, opportunity, accessibility or haphazard sampling
Use of readily available persons for the studysample of convenience
Stopping people in a street corner, people select themselves in response to public notices-risk of bias is greater.
Lack of representativeness
Used for making pilot studies
Purposive Sampling
Judgmental sampling
Researchers knowledge about the population can be used to hand-pick sample members, knowledgeable about the study
Used in newly developed instruments can be pretested and evaluated
Quota Sampling
Researcher utilizes knowledge about the population – representativeness into the sampling plan
Population is divided into quotas – age, socioeconomic status, religion etc.
Number of units within each quota –personal judgment of the investigator.
Used by quantitative researchers
Used in public opinion studies
Example
Snowball Sampling
Network or chain/referral sampling
Research population of specific traits-difficult to identify
Early sample members asked to refer other people who meet eligibility criteria
Sampling hidden populations-homeless or IV drug users-respondent driven sampling (rds),variant of snow ball sampling
Choice of Sampling method
Method
Best when
Simple random sampling
Whole population is available
Stratified random sampling
When specific subgroups are to be investigated
Choice of Sampling method
Method
Systematic random sampling
Cluster sampling
Best when
When stream of representative people are available
When population groups are separated & access to all is difficult
SAMPLE SIZE
Sample size
Percentage
Specific Margin of Error
Formula
Use of percentage Population
Percentage
Sample
100
100
100
200
70
140
300
50
150
400
35
150
500
30
150
600
25
150
700-10,000
20
140-2000
11,000-15,000
15
1,650-2,250
16,000 and above
10
1,600-above
Rivera, 1999
Use of Formula
Sloven Formula n = N/1 + Ne2 Where: n = sample size N = population size e2 = margin of error raised to the second power
Percentage Rs = n/N
Where: Rs = representative sample for a group N = Population size n = computed sample size
Example
Use of specific margin of error Population
Margin of Error
500
+5%
1,500
+3%
2,500
+2%
10,000
+1%
Rivera, 1999
Learning check
A group of students want to know the age of the students in College of Engineering and Tecgnology but do not have the resources to survey the entire population of 2 500. if they want to use sample with 5% margin of error, what should their sample size be?
Learning check
What should be the representative sample size if the population from which the sample will be taken is 15 000 and the desired margin of error is 10%?
Learning check
At present Brgy. San Isidro has about 1 800 registered household members and the Brgy. Captain is planning to conduct a survey that would determine the members’ opinion on the issue of Solid Waste Dumping site in their barangay. How many respondents must be considered?
Ethical Issues
Ethical Issues 1. A permit to conduct the study must be properly sought from the authority. Willingness of the prospective respondent must also be considered. 2. The researcher must assure the respondents of confidentiality of the data that will be generated and used in the study. 3. The researcher must be willing to share the results or findings of the study with the institution where the respondents belong.
Ethical Issues 4. The researcher must maintain integrity in the publication of the findings of the study. 5. The researcher must not inflict any harm to the respondents especially during experimental research. 6. The researcher must consider the potential benefits that the respondents may get from the study. 7. The research must observe intellectual honesty in undertaking such research
Ana L. Vargas RESEARCH METHODOLOGY Assistant Professor 2
Prof. Ana L. Vargas
Research Methodology
Sampling
Sampling
Process of selecting a required number of individuals from the study population so as to focus on the sample and not in the whole population.
Principle of sampling
To get the maximum information about the population with minimum effort and with limited resources
Why do we need Sampling
Studying the entire population is difficult.
It will be costly, time consuming and not feasible
Studying the whole population is impossible and unnecessary.
If sampling is done properly: Accurate and reliable estimates can be made More details/characteristics can be collected Can get best results in least possible time Project management is easy
Characteristics of Sampling
Representativeness
Unbiased selection
Adequacy of sample.
Characteristics of Sampling
Representativeness
Sample has all the important characteristics and similar distribution
Unbiased selection
Underrepresented or overrepresented members of the population
Due to faulty selection of sample ot faulty demarcation of sampling units
Adequacy of sample.
Results in reduction cost and saves time
Advantages of Sampling
Provides a more comprehensive information.
Saves time and effort.
Provides a wider scope of study and interests.
Accurate with the data gathered in total population.
Sampling terms
Population (universe)
Group of individuals or units possessing certain predetermined characteristics intended for the study.
Representative sample
It has all the characteristics with similar distribution as that of the population from which it is drawn
Sampling terms
Sampling frame
It is the list of all elements (persons, households, objects, specific events or units) in the population. e.g. voter’s list
Sampling unit
the constituent elements of a population which are to be sampled from the population and cannot be further subdivided for the purpose of sampling at a time. e.g. town, village, district
Sampling terms
Sampling fraction
Sample size
Proportion of population that is included in the sample. e.g. 20% the number of units in a sample
Sampling error
Any type of bias that is attributable to mistakes in either drawing a sample or determining the sample size
Sampling
Applicable to studies with very high population.
Uses the whole population as the source of information.
The study is tedious and impractical for it wastes a lot of time, effort and money if data will be gathered to all population.
Population
Population is a group individuals to which the study is applied.
Population is limited by place or locality
Population describes who your population.
Examples
All students at Partido State University. Government employees in the Bicol Region Teachers teaching science in public high schools in the Division of Camarines Sur
Three Types of Population
Target Population - the actual population to which the researcher is unable to generalize.
Accessible (source)Population is the population in which the researcher can make a generalization.
Sampling (study) Population is the population from which the sample will be actually taken as determined by the sampling frame.
Population Distribution
Illustration #1
Problem: The effects of Computer Aided Instruction to the Academic Performance of Students in Chemistry.
Target Population: All Chemistry students.
Accessible Population: All Chemistry students at PSU.
Sample Population: Ten percent (10%) of the total Chemistry students at PSU.
Illustration #2
The Attitudes and Perceptions of Public School Teachers Towards In Service Trainings and Seminars.
Target Population: All Public School Teachers
Accessible Population : All Public School Teachers in the Division of Camarines Sur
Sample Population: Two hundred fifty school teachers selected from the division of Camarines Sur.
Sample vs. Population
Population
Sample
Two Types of Sampling
Random Sampling (Probability)
Non random sampling-
Random (probability) Sampling
Random Sampling (Probability)
every member of the population are given equal chance of being selected.
Best among all the methods Most powerful statistical analysis on the results can be done subsequentlyCan be done by drawing lots to avoid bias.
Random (probability) Sampling
Include:
Simple random sampling (unrestricted)
Systematic random sampling (quasi-random)
Stratified random sampling
Cluster sampling (area sampling)
Multistage sampling
Important and frequently used methods
RANDOM SAMPLING
Simple Random Sampling
Simple Random Sampling
Define the study population ( N ) Prepare a proper sampling frame (n) Determine the sample size Select the required number of samples Done by :
Lottery method – small population Random number method – by using standard tables ( Tippet’s table, Fisher and Yate’s table and Kendall and Smith’s table ) Computer generated random numbers
Lottery Procedure 1. Write down the name of each member of the population on pieces of paper. 2. Place these papers in a box or a container drum.
3. The box or lottery drum must be shaken thoroughly to prevent some pieces of paper from sinking at the bottom. 4. Picked the required number of sampleunits from the lottery drum.
Simple Random Sampling
Example Population
1 6 11 16
2 7 12 17
3 8 13 18
4 9 14 19
Sample
5 10 15 20
3
6
9
12 15 18
Advantages
Personal bias is eleminated Representative of a homogenous population No need for thorough knowledge of the units of population Accuracy of the sample can be tested Used in other methods of sampling
Disadvantages
Cannot be used for large population When there is large difference between units Cost and time of collection of data are more Logistically more difficult in field conditions
Systematic Random Sampling
Systematic Random Sampling
Simple & convenient way of selecting a sample Requires less time and cost Sample is spread evenly over entire reference population Can be used in infinite population requires sampling frame Units are selected at an uniform interval Useful when information is collected from units which are in serial order (i.e.) entries in register, house in blocks etc.
Systematic Random Sampling
Done by:
Identify the sample size (n)
Put the population in sequential order & number them serially – sampling frame
Identify total no.of units in the population (N)
Divide N/n = sampling interval (k)
Identify a random no.which is less than or equal to ‘k’
Select every n’th item starting with a random one
Stratified Random Sampling
Stratified Random Sampling (SRS)
Dividing the population into subgroups or strata - stratification
Units within the stratum are homogenous and between the strata are heterogeneous
From each stratum a simple random sample is selected and combined together to form the required sample from the population
Two types of SRS
Proportional stratified random sampling
Unequal size
Disproportionate stratified random sampling
Equal size
Proportional stratified sampling • cases are drawn from each strata in proportion to their prevalence in the population.
Advantages of SRS
Every unit in the stratum has the same chance of being selected More representative Ensures proportionate representation Greater accuracy Greater geographical concentration
Disadvantages of SRS
Division of population into strata needs more money, time and statistical experience Improper stratification leads to bias – if there is overlapping of strata
Certain Subgroups or strata are selected. Strata
Population
%
Sample Size
Upper Class
200
30%
60
Middle Class
400
40%
160
Lower Class
200
30%
60
Learning check A survey to find out if families living in a certain municipality are in favor of Charter change will be conducted. To ensure that all income groups are represented, respondents will be divided into high income (class A), middle-income (class B) and low income (class C). Strata High- income (Class A) Middle-income Cclass B) Low-income (Class C)
No. of families 1 000 2 500 1 500 N= 5 000
Learning check 1. Using a 5% margin of error, how many families should be include in the sample? 2. Using proportional allocation, how many from each group should be taken as samples? 3. Using equal allocation, how many from each group should be taken as samples?
Learning check Strata
No. of families
Class A
1 000
Class B
2 500
Class C
1 500 N= 5 000
Proportional Equal allocation allocation (%)
Cluster Sampling
The whole population is divided into groups called clusters.
Each cluster is representative of the population
Clusters are selected randomly
A random sample is then is taken from within each cluster
Lot of clusters are sampled so that the results can be generalized for whole population
Cluster Sampling
Clusters should be as small a possible consistent with the time & cost limitations
No. of units in each cluster must be more or less equal
Advantages
Cuts down the cost of preparing sampling frame and cost of travelling between selected units Eliminates the problem of “packing”
Disadvantages
Sampling errors is usually higher than for a simple Random sample of the same size
Cluster Sampling Example WHO 30 clusters for coverage evaluation survey Pulse polio immunization coverage evaluation survey In a PHC estimate the proportion of infants with age 6 months to 1 yr who are fully immunized .
Cluster Sampling Example In a PHC estimate the proportion of infants with age 6 months to 1 yr who are fully immunized . 1. Identification of total population and the geographical area 2. Identification of age group to be included 3. Listing of all villages 4. Tabulation
Cluster Sampling Selection of groups or clusters of subjects rather than individuals A A
B
C C
Population E D
E
F Sample
Cluster sampling
Learning check
You want to determine the average expenses of families living in the province of Camarines Sur. In sum, there are 208 Brgys. In four districts of Camarines Sur. What should be the sample size?
Multi-Stage sampling
Used for large and diverse populations (eg) nation, region or state
Usually carried out in phases
Involves more than one sampling methods
Example : Estimating the problem of Iodine deficiency disorders in India
Multi-Stage sampling
First stage : few states are randomly selected
Second stage : few districts from above states
Third stage : few blocks from above districts
Fourth stage : few villages from above districts
Fifth stage : few households from each village
Multi-Stage sampling ADVANTAGES : Sample frame for individual units not required Cuts down the cost of preparing sample frame DISADVANTAGES : final sample may not be representative of the total population Sampling error is increased, when compared with simple random sampling
Multi-Stage Random Sampling
A combination of cluster random with simple random sampling. ABC
DEF
GHI
DEF KLM
KLM
NOP
QRS
QRS
ELR
NON-PROBABILITY SAMPLING
Non-Random Sampling
Does not provide equal chance for every member to be selected.
May lead to unrepresentative samples
It lacks accuracy in view of selection bias
May not represent the population well
Used when there is no sample frame for the population
Mostly used in qualitative research like exploratory research, opinion surveys and marketing studies
Non-Random Sampling
Include:
Purposive sampling (judgemental sampling)
Convenience sampling (oppurtunity sampling)
Quota sampling
Expert opinion sampling
Snowball sampling (chain sampling, chain referral
sampling or referral sampling)
Non-Random Sampling
Include:
Purposive sampling (judgemental sampling)
Convenience sampling (oppurtunity sampling)
Quota sampling
Expert opinion sampling
Snowball sampling (chain sampling, chain referral
sampling or referral sampling)
Convenience(Accidental) Sampling
Accidental, opportunity, accessibility or haphazard sampling
Use of readily available persons for the studysample of convenience
Stopping people in a street corner, people select themselves in response to public notices-risk of bias is greater.
Lack of representativeness
Used for making pilot studies
Purposive Sampling
Judgmental sampling
Researchers knowledge about the population can be used to hand-pick sample members, knowledgeable about the study
Used in newly developed instruments can be pretested and evaluated
Quota Sampling
Researcher utilizes knowledge about the population – representativeness into the sampling plan
Population is divided into quotas – age, socioeconomic status, religion etc.
Number of units within each quota –personal judgment of the investigator.
Used by quantitative researchers
Used in public opinion studies
Example
Snowball Sampling
Network or chain/referral sampling
Research population of specific traits-difficult to identify
Early sample members asked to refer other people who meet eligibility criteria
Sampling hidden populations-homeless or IV drug users-respondent driven sampling (rds),variant of snow ball sampling
Choice of Sampling method
Method
Best when
Simple random sampling
Whole population is available
Stratified random sampling
When specific subgroups are to be investigated
Choice of Sampling method
Method
Systematic random sampling
Cluster sampling
Best when
When stream of representative people are available
When population groups are separated & access to all is difficult
SAMPLE SIZE
Sample size
Percentage
Specific Margin of Error
Formula
Use of percentage Population
Percentage
Sample
100
100
100
200
70
140
300
50
150
400
35
150
500
30
150
600
25
150
700-10,000
20
140-2000
11,000-15,000
15
1,650-2,250
16,000 and above
10
1,600-above
Rivera, 1999
Use of Formula
Sloven Formula n = N/1 + Ne2 Where: n = sample size N = population size e2 = margin of error raised to the second power
Percentage Rs = n/N
Where: Rs = representative sample for a group N = Population size n = computed sample size
Example
Use of specific margin of error Population
Margin of Error
500
+5%
1,500
+3%
2,500
+2%
10,000
+1%
Rivera, 1999
Learning check
A group of students want to know the age of the students in College of Engineering and Tecgnology but do not have the resources to survey the entire population of 2 500. if they want to use sample with 5% margin of error, what should their sample size be?
Learning check
What should be the representative sample size if the population from which the sample will be taken is 15 000 and the desired margin of error is 10%?
Learning check
At present Brgy. San Isidro has about 1 800 registered household members and the Brgy. Captain is planning to conduct a survey that would determine the members’ opinion on the issue of Solid Waste Dumping site in their barangay. How many respondents must be considered?
Ethical Issues
Ethical Issues 1. A permit to conduct the study must be properly sought from the authority. Willingness of the prospective respondent must also be considered. 2. The researcher must assure the respondents of confidentiality of the data that will be generated and used in the study. 3. The researcher must be willing to share the results or findings of the study with the institution where the respondents belong.
Ethical Issues 4. The researcher must maintain integrity in the publication of the findings of the study. 5. The researcher must not inflict any harm to the respondents especially during experimental research. 6. The researcher must consider the potential benefits that the respondents may get from the study. 7. The research must observe intellectual honesty in undertaking such research
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