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Business Enterprise Simulation-Mod5 - Copy.pptx
- 1. SURVEYS ✗In survey research,the researcher selects a sample of respondents from a population and administers a standardized questionnaire to them. 1
- 2. Experiments ✗This is anexperiment where the researchers manipulate one variable, and control/randomizes the rest of the variables. 2
- 3. Observational research ✗Observational research (or fieldresearch) is a type of correlational (i.e., non- experimental) research in which a researcher observes ongoing behavior. 3
- 4. Random Sampling Methods The random samplingis also called as a probability sampling since the sample selection is done randomly so the laws of probability can be applied. 1
- 5. Non-Random Sampling Methods In thecase of non-random sampling, the selection is done on the basis other than the probability considerations, such as judgment, convenience, etc. 2
- 6. Non-Random Sampling Methods The non-randomsampling is subject to sampling variability, but however there is no certain pattern of variability in the process. 2.0
- 7.
- 8. Entrepreneur Asia Pacific(n.d.) states that market research provides relevant data to help solve marketing challenges that a business will most likely face--an integral part of the business planning process. 8
- 9. primary sources Gathering datafrom primary sources includes observation networking, interviewing and experimentation. It means that the person who needs the data does the gathering himself or herself while gathering data from secondary sources means that somebody else has gathered the data and you are a secondary user of said data. 1
- 10. secondary sources Secondary sources ofdata are those that have already been compiled and are available like those from business directories, demographic data from government or private agencies, existing market research, and those from the internet. 2
- 11. Secondary data includes: opublic documents o books o journals and magazines o internet o internal data bases 11
- 12.
- 13. British Library (n.d.)cited sampling as an effective way of obtaining opinions from a wide range of people, selected from a specific group, in a bid to find out more about a whole group in general. 13
- 14. Simple Random Sampling — themost commonly used sampling technique, and truly random, this method randomly selects individuals from a list of the population, with every individual having an equal chance at being selected. 14
- 15. Stratified Sampling — this methodis a conflation of Simple Random and Systematic Sampling and is often used when there are a multitude of unique subgroups that require full, randomized representation across the sampling population. 15
- 16. Systematic Sampling — rather thanrandomly selecting individuals from a population, this method is based on a system of selecting participants. 16
- 17. Multistage Sampling – it isthe probability sampling technique wherein the sampling is carried out in several stages such that the sample size gets reduced at each stage. 17
- 18. Judgement Sampling – it isthe non-random sampling technique wherein the choice of sample items depends exclusively on the investigator’s knowledge and professional judgment. 18
- 19. Convenience Sampling – it isthe non-probability sampling technique wherein a proportion of the population is selected on the basis of its convenient availability. 19
- 20. Quota Sampling – itis yet another non-probability sampling method wherein the population is divided into a mutually exclusive, sub-groups from which the sample items are selected on the basis of a given proportion. 20
- 21. Snowball Sampling – it isa non-random sampling technique wherein the initial informants are approached who through their social network nominate or refer the participants that meet the eligibility criteria of the research under study. 21
- 22. Snowball Sampling Thus, this methodis also called as the referral sampling method or chain sampling method. 22
- 23.
- 24. Lumen (n.d.) describedanalysis of data as a process of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. 24
- 25. 1. Input –The first part of the data processing cycle involves collecting data as well as entering it and then preparing it for the next part of the cycle. Data Processing Cycle 25
- 26. 2. Processing –During the second part of the cycle, data is manipulated according to instructions and parameters programmed into the processing application. Data Processing Cycle 26
- 27. 3. Output –The form of outputs includes common variations such as results that are printed or displayed on a computer monitor. Data Processing Cycle 27
- 28. 4. Interpretation – Assessingand analyzing results: What does the data mean? Data Processing Cycle 28
- 29. 5. Feedback – Comparingoutput with desired results: How can data be processed better? Data Processing Cycle 29
- 30. 6. Storage –Archiving the data (either physically or electronically) for future use. Data Processing Cycle 30
- 31.
- 32. 1. Editing –What data do you really need? Extracting and editing relevant data is the critical first step on your way to useful results. Steps in Business Data Processing 32
- 33. 2. Coding –This step is also known as bucketing or netting and aligns the data in a systematic arrangement that can be understood by computer systems. Steps in Business Data Processing 33
- 34. 3. Data Entry– Entering the data into software is a step that can be performed efficiently by data entry professionals. Steps in Business Data Processing 34
- 35. 4. Validation –After a “cleansing” phase, validating the data involves checking (and preferably double-checking) for desired quality levels. Steps in Business Data Processing 35
- 36. 5. Tabulation – Arrangingdata in a form that facilitates further use and analysis. Steps in Business Data Processing 36
- 37. Drawing Conclusions and FormulatingRecommendations Thus, you have to generate your conclusions and recommendations based on the data you have generated, processed and analyzed. 37
Editor's Notes
- #16 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #17 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #18 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #19 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #20 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #21 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #22 For example, a market researcher may select from a list of the population every 20th person. While this allows for a controlled way to select from a target population, it may be skewed depending on how the original list is structured or organized.
- #24 Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names in different business, science, and social science domains (Lumen, n.d.).