APznzaYNPgDHkYWgycSVc3JKNImav9JwiB7pnPlHYG4VP2KL2yipjoITwGy0Bp72UoH2ke0fR8SbiFbmxFGFlbtSjM45f-4rI0p2JjFwhaWOoN4Ewj9-wLdSGAnw6gHowRYfF3BvtJ4pALXHKgn9Mfo_Y8FxvYJVf-1o1oDNxaQ1GL26BegI536JZO_fT9GnaJ9mKfnJnWK2BgrC2hnl4wr.pdf

UNIT 2 OBJECTIVES State the different methods in collecting data Enumerate and discuss the sources of data Differentiate Random from Non- Random Sampling OBJECTIVES Discuss the different features...

UNIT 2 OBJECTIVES State the different methods in collecting data Enumerate and discuss the sources of data Differentiate Random from Non- Random Sampling OBJECTIVES Discuss the different features of a good questionnaire Determine the appropriate sampling technique used in the study to be conducted  COLLECTION OF DATA It refers to the process of obtaining numerical measurements  SOURCES OF DATA 1. Documentary Sources. The information contained in published or unpublished report, statistics, Internet, letters, magazines, newspapers, diaries, and so on. These are taken from primary and secondary data. a. Primary Data – data gathered are original Examples: conducting a survey (This can be done through online surveys, paper surveys, or in-person interviews) a. Secondary Data – data that are previously gathered from an original source, which are computed and compiled Examples: Sales figures or other reports from third-party companies  SOURCES OF DATA 2. Field Sources. This would include individuals who have sufficient knowledge and experience regarding the study under investigation.  SOURCES OF DATA SOURCES OF DATA Documentary Field Sources Primary Secondary  METHODS USED IN THE COLLECTION OF DATA The choice of method is influenced by the data collection strategy, the type of variable, the accuracy required, the collection point and the skill of the enumerator. Links between a variable, its source and practical methods for its collection can help in choosing appropriate methods.  METHODS USED IN THE COLLECTION OF DATA The choice of method is influenced by the data collection strategy, the type of variable, the accuracy required, the collection point and the skill of the enumerator. Links between a variable, its source and practical methods for its collection can help in choosing appropriate methods.  METHODS USED IN THE COLLECTION OF DATA Direct observations: This method is often referred to as interview method. Interviews: forms which are completed through an interview with the respondent. More expensive than questionnaires, but they are better for more complex questions, low literacy or less co-operation. Questionnaires: forms which are completed and returned by respondents. An inexpensive method that is useful where literacy rates are high and respondents are co-operative.  METHODS USED IN THE COLLECTION OF DATA Registration: registers and licences are particularly valuable for complete enumeration, but are limited to variables that change slowly, such as numbers of fishing vessels and their characteristics. Observation Method : This method is used to collect data pertaining attitudes, behavior, values, and cultural patterns of the samples under investigation. Subjects may be taken individually or collectively. It is usually used when the subjects cannot talk. This can be done directly or indirectly.  METHODS USED IN THE COLLECTION OF DATA Experiment method: this method is used if the researcher would like to determine the cause and effect relationship of certain phenomena under investigation. This is used in making scientific inquiry  METHODS USED IN THE COLLECTION OF DATA Methods used in the Collection of Data Direct Observation Observations Questionnaires Experimentation Registration PLANNING THE STUDY 1. Estimate the number of items in the population 2. Asses resources such as time & money factors, which are available to pursue the research 3. Determine the sample size needed in the study  PLANNING THE STUDY General Tips Step 1: Conduct a census if you have a small population. A “small” population will depend on your budget and time constraints. For example, it may take a day to take a census of a student body at a small private university of 1,000 students but you may not have the time to survey 10,000 students at a large state university.  PLANNING THE STUDY General Tips Step 2: Use a sample size from a similar study. Chances are, your type of study has already been undertaken by someone else. You’ll need access to academic databases to search for a study (usually your school or college will have access). A pitfall: you’ll be relying on someone else correctly calculating the sample size. Any errors they have made in their calculations will transfer over to your study.  PLANNING THE STUDY General Tips Step 3: Use a table to find your sample size. If you have a fairly generic study, then there is probably a table for it. For example, if you have a clinical study, you may be able to use a table published in Machin et. al’s Sample Size Tables for Clinical Studies, Third Edition.  PLANNING THE STUDY General Tips Step 4: Use a sample size calculator, like this one https://surveysystem.com/sscalc. htm  PLANNING THE STUDY General Tips Step 4: Use a sample size calculator, like this one https://surveysystem.com/sscalc. htm You can use a sample size calculator to determine how big your sample should be. In general, the larger the sample size, the more accurately and confidently you can make inferences about the whole population.  PLANNING THE STUDY General Tips Step 5: Use a formula. There are many different formulas you can use, depending on what you know (or don’t know) about your population. If you know some parameters about your population (like a known standard deviation), you can use the techniques below. If you don’t know much about your population, use Slovin’s formula.  PLANNING THE STUDY General Tips Step 5: Use a formula. Probability and Statistics > Slovin’s Formula If you take a population sample, you must use a formula to figure out what sample size you need to take. Sometimes you know something about a population, which can help you determine a sample size. For example, it’s well known that IQ scores follow a normal distribution pattern. But what about if you know nothing about your population at all? That’s when you can use Slovin’s formula to figure out what sample size you need to take, which is written as  PLANNING THE STUDY General Tips Step 5: Use a formula. Probability and Statistics > Slovin’s Formula 𝑁 Where: n= n = Number of samples, ( 1+𝑁 𝑒 2 ) N = Total population and e = Error tolerance (level)  PLANNING THE STUDY General Tips Sample question: Use Slovin’s formula to find out what sample of a population of 1,000 people you need to take for a survey on their soda preferences. Step 1: Figure out what you want your confidence level to be. For example, you might want a confidence level of 95 percent (giving you an alpha level of 0.05), or you might need better accuracy at the 98 percent confidence level (alpha level of 0.02).  PLANNING THE STUDY General Tips Step 2. Plug your data into the formula. In this example, we will use a 95 percent confidence level with a population size of 1,000. n = N / (1 + N e2) 1000 = 2 1+(1000 ∗ 0.05 = 285.714286 Step 3: Round your answer to a whole number (because you can’t sample a fraction of a person or thing!) 285.714286 = 286 PLANNING THE STUDY 4. Pick the sample by using the appropriate sampling technique 5. Prepare the questions to be asked in the interview or in the questionnaire  TYPES OF QUESTIONS 1. Structured Question This is a type of question that leaves only one way or few alternative ways of answering it. The questions asked are clear, simple, and objective. Structured questions are easy to answer and tabulate  TYPES OF QUESTIONS 1. Structured Question This is a type of question that leaves only one way or few alternative ways of answering it. The questions asked are clear, simple, and objective. Structured questions are easy to answer and tabulate A typical example of a structured questionnaire is the Census questionnaire, which collects demographic information from individuals.  TYPES OF QUESTIONS 1. Structured Question A typical example of a structured questionnaire is the Census questionnaire, which collects demographic information from individuals.  TYPES OF QUESTIONS 2. Unstructured or Open-ended Questions These are questions that can be answered in many ways- probing questions or questions that want to elicit reasons TYPES OF QUESTIONS Types of Questions Structured Unstructured Questions Questions  FEATURES OF A GOOG QUESTIONNAIRE 1. Make the question short and clear 2. Avoid leading questions 3. Always state the precise units in which you require them to answer in order to facilitate tabulation later on 4. As much as possible ask questions which can only be answered by just checking slots or stating simple names or brands 5. Arrangement of questions should be carefully planned 6. Limit questions to essential information  PROBABILITY SAMPLING TECHNIQUES Probability sampling involves random selection, allowing you to make statistical inferences about the whole group. 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, you need to use a probability sampling technique. PROBABILITY SAMPLING TECHNIQUES 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. PROBABILITY SAMPLING TECHNIQUES Example : You 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. PROBABILITY SAMPLING TECHNIQUES 1. Simple random sampling random number generators or other techniques that are based entirely on chance.  PROBABILITY SAMPLING TECHNIQUES 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.  PROBABILITY SAMPLING TECHNIQUES Example All 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.  PROBABILITY SAMPLING TECHNIQUES Example 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. PROBABILITY SAMPLING TECHNIQUES 3. Stratified sampling This sampling method is appropriate when the population has mixed characteristics, and you want to ensure that every characteristic is proportionally represented in the sample. You divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role). PROBABILITY SAMPLING TECHNIQUES From 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. PROBABILITY SAMPLING TECHNIQUES Example The 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 PROBABILITY SAMPLING TECHNIQUES 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. PROBABILITY SAMPLING TECHNIQUES 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. PROBABILITY SAMPLING TECHNIQUES 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. PROBABILITY SAMPLING TECHNIQUES Example The 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.  PROBABILITY SAMPLING TECHNIQUES Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data  PROBABILITY SAMPLING TECHNIQUES 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. https://www.slideshare.net/danilojrolaer99/nonprobability-sampling  PROBABILITY SAMPLING TECHNIQUES Non-probability sampling methods This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias, and you can’t use it to make valid statistical inferences about the whole population. Non-probability sampling techniques are often appropriate for 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.  PROBABILITY SAMPLING TECHNIQUES 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.  PROBABILITY SAMPLING TECHNIQUES Example : Convenience sampling You 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. PROBABILITY SAMPLING TECHNIQUES 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). PROBABILITY SAMPLING TECHNIQUES 2. Voluntary response sampling Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.  PROBABILITY SAMPLING TECHNIQUES Example : Voluntary response sampling You 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.  PROBABILITY SAMPLING TECHNIQUES 3. Purposive sampling This type of sampling involves the researcher using their judgement to select a sample that is most useful to the purposes of the research.  PROBABILITY SAMPLING TECHNIQUES 3. Purposive sampling It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences. An effective purposive sample must have clear criteria and rationale for inclusion.  PROBABILITY SAMPLING TECHNIQUES Example : Purposive sampling You 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. PROBABILITY SAMPLING TECHNIQUES 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.  PROBABILITY SAMPLING TECHNIQUES Example : Snowball sampling You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area  PROBABILITY SAMPLING TECHNIQUES Probability Non Probability Sampling Sampling Voluntary Simple Systematic Convenience Response Random Sampling Sampling Sampling Sampling Stratified Cluster Purposive Snowball Sampling Sampling Sampling Sampling QUESTIONS WHAT HAVE I LEARNED TODAY?