Sampling Techniques and Data Analysis Essay Example

Sampling Techniques and Data Analysis Essay Example Sampling Techniques and Data Analysis Essay Sampling Techniques and Data Analysis Essay Polytechnic University of the Philippines College of Economics, Finance and Politics DEPARTMENT OF BANKING AND FINANCE Sta. Mesa, Manila SAMPLING TEACHNIQUES and DATA ANALYSIS Submitted by: Jayson A. Enabia Rechelle Ann V. Elon Lobelyne Elago Monica Mae R. Flores April Mariz Francisco BBF 4-10n TABLE OF CONTENTS Introduction1 Methods of Collecting Data Interview method1 Questionnaire Method2 Empirical Observation Method4 Test Method5 Registration Method5 Mechanical Devices5 Sampling Techniques6 Random Sampling6 Systematic Sampling7 Purposive Sampling7 Quota Sampling8 Convenience Sampling8 Organization and Presentation of Data8 Data Analysis12 Introduction There is no formula for selecting the best method to be used when generating data. It depends on the researcher’s design of the study, the type of data,the time allotment to complete the study and the researcher’s financial capacity. Data Collection is an important aspect of any type of research study. Inaccurate data collection can impact the results of a study and ultimately lead to invalid results. METHODS OF COLLECTING DATA INTERVIEW METHOD This method of collecting data involves presentation or oral-verbal stimuli and reply in terms of oral-verbal responses. There are different types of interview methods namely: a. PERSONAL INTERVIEW The interviewer asks questions generally in a face to face contact to the other person. b. TELEPHONIC INTERVIEW It is use when it is not possible to contact the respondent directly c. STRUCTURED INTERVIEW It follows a set of questions to be asked to the interviewer. d. UNSTRUCTURED INTERVIEW o predetermined questions are asked, in order to remain as open and adaptable as possible to the interviewee’s nature and priorities; during the interview the interviewer â€Å"goes with the flow†. e. FOCUSED INTERVIEW Attention is focused on the given experience of the respondent and its possible effects f. CLINICAL INTERVIEW It is concerned with broad underlying feelings or motivations with the course of individual’s life experience, ra ther than with the effects of the specific experience. g. GROUP INTERVIEW A group of individuals are being interviewed. h. INDIVIDUAL INTERVIEW An interviewer meets a single person and interviews him. i. SELECTION INTERVIEW It is done for the selection of people for certain jobs. QUESTIONNAIRE METHOD A questionnaire is a list of well: 1. Have you been convicted of any crime? Yes______ No______. If your answer is yes, go to the next question and if no proceed to question number 3. b. RECALL TYPE Example: a. age b. sex c. civil status d. length of stay in a community c. RECOGNITION TYPE Example: Which of the following figures is a square? d. DICHOTOMOUS TYPE Fixed: Are you in favor of REPRODUCTIVE HEALTH BILL? YES______ NO______ e. MULTIPLE: What causes night and day? A. The earth spins on its axis. B. The earth moves around the sun. C. Clouds block out the suns light. D. The earth moves into and out of the suns shadow. E. The sun goes around the earth. f. MULTIPLE: What computer games do you prefer to play? Encircle the numbers. 1. DOTA6. FarmVille 2. Special Force7. Black Point 3. Angry Birds8. Ragnarok 4. Plant vs. Zombie9. Flyff 5. Rotate and Roll10. Red Alert g. FREE: 1. How serious is the drug problem in your barangay? ____ very serious ____ serious ____ fairly serious ____not serious ____ not a problem 2. Attitude towards Mathematics In answering the question below, please refer to the verbal equivalent of the numbers used in the table. 1 = disagree2= slighty agree3= moderately agree 4=strongly agree I love mathematics |1 |2 |3 |4 | |I always like the subject since my elementary years |1 |2 |3 |4 | |I am always excited to attend my mathematics class |1 |2 |3 |4 | EMPIRICAL OBSERVATION METHOD The observation method is commonly used in psychological and anthropological studies. It is a method of obtaining data by seeing, hearing, testing, touching, and smelling. Through observation, additional information, which cannot be obtained using the other methods like the questionnaire, may be gathered. The observer may participate in the activities of the group being studied (participant observation) or he may just be a bystander (nonparticipant observation). When an observation is done in a laboratory, as in the case of experimental studies, the type of observation is called controlled observation. TEST METHOD ? Widely used in psychological research and psychiatry ? Used because of: Validity Reliability Usability Example: 1. Aptitude tests 2. IQ tests 3. Achievement tests REGISTRATION METHOD ? Commonly enforced by certain laws, ordinances or standard practices ? Very practical and inexpensive method of gathering data ? In this method, information are kept systematized and available to all because of the law’s requirement Example: 1. Data obtained from NSO: A researcher wants to know the average income of the families living in Barangay A which has 2,500 residents. Calculate the sample size the researcher will need if a 5% margin of error is allowed. Given:N= 2500 e=0. 05 Solution:2500/[1+2,500(0. 05^2)]= 345 families Random Sampling In this method, all members of the population have equal chances of being included in the study. This is applicable if the target population is not classified into different clusters, sections, levels, or classes. The method is easy to use, but not when the population is very large, say a thousand or more. a. Lottery Method It is the most common and the easiest method of random sampling. The names of the respondents will be written on small pieces of paper which will be rolled and placed in a jar. The respondents who will be included in the study will be those names are written on the pieces of paper that are picked at random from the jar. Systematic Sampling a. Stratified Random Sampling This method is applied when the population is divided into different classes wherein each class must be represented in the study. . Cluster Sampling When the geographical area where the study will be done is too big and the target population is too large, the cluster sampling technique may be appropriate. In this technique, the selection of sample units is not individuals but by groups of clusters. The area will be divided in clusters, then a desired number of clusters will be selected at random. Example: A d octor wants to make a nationwide study on the correlation between smoking and death rate. He decided to focus on the 13 regions of the country which can be considered as the clusters. If 3 of the 13 clusters or regions are the desired sample units, the names of the 13 clusters will be written on small pieces of paper, then three will be picked at random using the lottery method. All the residents of the selected three clusters will be included in the study. Purposive Sampling The respondents of the study will be chosen based on their knowledge of the information required by the researcher. Example: Suppose a researcher wants to make a historical study about Town A. the target population will be the senior citizens of the town since they are the most reliable persons who know the history of the town. If there are 2,000 senior citizens and a 3% margin of error is allowed the sample size will be 714. They will be chosen using any of the methods discussed previously Quota Sampling This technique is commonly used in opinion polls. Suppose a salesman is required to gather information as to the most common hair shampoo used by female Filipino clients. If he wants 2,000 sample units and he needs to do the survey within a short timetable, he can station himself at a public place, such as a park or a mall, then ask the females what shampoo they usually use. After meeting the required number of sample points, the researcher is through with his collection of data Convenience Sampling This technique is resorted to by researchers who need the information the fastest way possible. The telephone can be used to interview the respondents about their opinions on a certain issue. This method may be fast but it is also biased because those who have no telephones do not have a chance to be included in the study. Another example is the case of a teacher who makes a research which requires the inclusion of the students as respondents. Conveniently, the teacher may use his own students as respondents. PRESENTATION OF DATA Generally data are presented in the form of tables, graphs or charts. Tables and graphs (pictorial presentation of data) may simplify and clarify the research data. Tabular and graphic representation of data may take a number of forms, ranging from computer printouts to elaborate pictographs. The purpose of each table or graph, however, is to facilitate the summarization and communication of the meaning of the data. a. Table A table is a systematic arrangement of related statistical data in columns and rows with some predetermined aim or purpose. Example: There are 50 science and arts students in a college. The number of students from poor families is the same for each course and the total is 30. Whereas science and commerce courses are equally popular in rich familis, yet the number of rich art students is twicw as much. In all 40 students are from the rich families studying in the college. The majority of students are from middle class families and their number is 80. Types of tables Reference or general purpose tables- tables that are in a way a store of information with an aim of presenting detailed statistical materials. Generally we can derive smaller tables from it. Special Purpose or text tables- smaller tables that can be obtained from reference tables. It aimed to analyse a particular aspect to bring out a specific point. b. Graphs and Charts There are many types of graphs and charts that are commonly used for showing business reports. These are listed as follows. 1. Line graphs:  A line graph is a way of representing two pieces of information, which is usually related and vary with respect to each other. This is useful when comparisons are needed. e. g. [pic] 2. Pie Charts:  A pie chart is a type of a circle graph normally used in showcasing a wholesome quantity; we have to show that how this whole quantity is broken into parts. The whole quantity depicts entire sample space  and the pieces of pie in the circle graph are called sectors. [pic] 3. Bar Charts:  This is a type of chart, which contains labeled horizontal or vertical bars showing a piece of information and an axis. The numbers along the side of bar graph compose the axis. This is also called as a histogram; Bar Graph is useful when there is a numerical comparison. [pic] 4. Area Graphs:  These graphs are used to show how something changes with respect to time. An area graph shows the contribution of each data series in the form of a picture. [pic] 5. Waterfall Chart:  This is a type of chart, which shows an increase or decrease in a initial value. This contains floating vertical columns that shows the increase or decrease in a initial value through a series of intermediate steps leading to a final value. An invisible column keeps the increase or decrease related to the heights of the previous columns. [pic] 6. Polar Chart:  A Polar Chart is a circular chart in which data is displayed in terms of values and angles. This provides a mechanism to compare various qualitative and quantitative aspects of a situation graphically. o By using Polar Charts we can plot multiple data sets each with a single line with as many points as needed. o These are normally used in Engineering and modeling Industries. o A Polar Chart has two variables X and Y where X is plotted as an angle and Y is the radius. o In a Polar Chart the points are plotted in Polar coordinates rather than Cartesian coordinates. o In a Polar Chart the dataset having the maximum values covers the maximum area in the whole graph. The X and Y-axes can be used to demonstrate real world quantities. [pic] Analysis of data Analysis of data  is a process of inspecting, cleaning, transforming, and modeling  data  with the goal of highlighting useful  information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Data mining  is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes. Data integration  is a precursor to data analysis, and data analysis is closely linked to  data visualization  and data dissemination. The term data analysis  is sometimes used as a synonym for  data modeling. Data cleaning is an important procedure during which the data are inspected, and erroneous data are if necessary, preferable, and possible corrected. Data cleaning can be done during the stage of data entry. If this is done, it is important that no subjective decisions are made. The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that are aimed at answering the original research question. The initial data analysis phase is guided by the following four questions: Quality of data Quality of measurements Initial transformations Final stage of the initial data analysis Considerations/issues in data analysis There are a number of issues that researchers should be cognizant of with respect to data analysis. These include: Having the necessary skills to analyze Concurrently selecting data collection methods and appropriate analysis Drawing unbiased inference Inappropriate subgroup analysis Following acceptable norms for disciplines Determining statistical significance Lack of clearly defined and objective outcome measurements Providing honest and accurate analysis Manner of presenting data Environmental/contextual issues Data recording method Partitioning ‘text’ when analyzing qualitative data Training of staff conducting analyses Reliability and Validity Extent of analysis