Survey Notes PDF

ENGLISH FOR ACADEMIC AND PROFESSIONAL PURPOSES SURVEY NOTES 1 Questionnaire – a written document containing questions and other types of items designed to solicit first-hand information appropriate to analysis. A...

ENGLISH FOR ACADEMIC AND PROFESSIONAL PURPOSES SURVEY NOTES 1 Questionnaire – a written document containing questions and other types of items designed to solicit first-hand information appropriate to analysis. A well-designed questionnaire motivates respondents to provide accurate and complete information which is very helpful in attaining the survey’s objective. Survey Research - the collection of information from a sample of individuals through their responses to questions or statements Respondent – a person who provides data in a survey research. Pre-Activity Directions: Write T if the statement is True and F if it is False. 1. A questionnaire is the same as a survey. 2. When a staff handed you a piece of paper that asks you to choose a smiley to express how you feel about their service is an example of a survey question. 3. A questionnaire should be anchored on the research problem. 4. In designing a questionnaire, a researcher can always include all the questions that come into his/her mind. 5. Likert-Scale questionnaire items are best used to measure the feeling or opinion. 6. It is always good to appeal to the emotions of the respondents in creating questions. 7. Arrangement of questions does not matter as long as it will provide the information needed from the respondents. 8. Always use multiple choice type of questions or close-ended questions to be able to gather data easily. 9. A questionnaire can be compared to a newly sewn dress that needs to be fitted before finally giving to a customer. 10. Testing and revising a questionnaire can be ignored as long as the design of the questionnaire hits the targeted research problem and the required data. DESIGNING A QUESTIONNAIRE Here are the suggested steps on how to develop a questionnaire: QUESTION WORDING BASIC (Adapted from Filiberto, D. (2013) with some notes from Baxter, L. and Babbie, E. (2003)) 1. Write short and simple questions Respondents are often unwilling to study an item in order to understand it. Assume that respondents will answer the questionnaire quickly. Therefore, provide clear, short items that will not be misinterpreted. Example: Given the current trend of more hits, more home runs, longer games in general, and more injuries in baseball today, do you think that steroid use should continue to be banned even though it is not enforced? *Problem: Long questions can be confusing Better question: Steroid use has both positive and negative effects on baseball. Do you think that steroid use should be banned?" 2. Avoid leading questions, wording that influences respondents to consider a subject in a weighted manner, or injects a preference or opinion. Example: Do you hate the president of the Philippines?” Why is this leading? Because the question itself includes an opinion word. Who do you think of when you hear COVID -19? a. China b. Pres. Duterte c. Chinese d. Lockdown Why is this leading? Because it forces the respondent to answer one of these choices, even if none of them comes to mind. 3. Appropriately Open-Ended and Closed-Ended Questions Use open-ended questions when responses need to be elaborated by the respondents for exhaustive and comprehensive data gathering. They’re more suited to exploratory research that looks to describe a subject based on trends and patterns. Closed-Ended questions are popular because they provide greater uniformity or responses and are easily processed compared to open-ended questions. However, closed-ended questions the response categories should be exhaustive and mutually exclusive. In other words, all possible options should be provided. Example: Why do you play sports? 1. Enjoyment 2. Health 3. Friends 4. Other----- 4. Questions must be non-threatening and attempt to evoke the truth. Example: Who do you think consume more cigarettes: you or your friends? When a respondent is concerned about the consequences of answering a question in a particular manner, there is a good possibility that the answer will not be truthful. 5. Question Clarity Avoid ambiguities and vague words (e.g. usual, regular, normal) Example: What is your number of serving of eggs in a typical day? Problem: How many eggs constitute a serving? What does ‘a typical day’ mean? Better question: On days you eat eggs, how many eggs do you usually consume? Do you watch television regularly? *Vague questions are difficult to answer (what is the meaning of "regularly"?) Better question: How often do you watch Television?" Note: Questions should mean the same thing to all respondents. All the terms should be understandable or defined, time periods specified, complex questions asked in multiple stages. 6. Don’t use double-barreled questions Ask one question at a time. Avoid asking 2 questions, imposing unwarranted assumptions, or hidden contingencies. Whenever you use ‘and’ on a question or a statement, check if it is double-barreled. Example: Do you find the classes you took during your first semester in SHS more demanding and interesting than your JHS classes? Yes No * How would someone respond if they felt their SHS classes were more demanding but also more boring than their JHS classes? Or less demanding but more interesting? Because the question combines “demanding” and “interesting,” there is no way to respond yes to one criterion but no to the other. Do you find the classes you took during your first semester in SHS more demanding than your JHS classes? 7. Clearly define the response scale dimension or continuum. When using a response scale, clearly define the dimension or continuum respondents are to use in their rating task Example: Response categories - Make them logical and meaningful: NOT: Many......Some.......A Few......Very Few.....None DO a Bipolar or Unipolar rating scale: Bipolar measures both direction and intensity of an attitude: Unipolar scale measures one concept with varying degrees of intensity. 8. Minimize presuppositions – an assumption about the world whose truth is taken for granted. Answering a question implies accepting its presuppositions, a respondent may be led to provide an answer even if its presuppositions are false. Example: Are you a DDS or a Dilawan? Problem: presupposes that one of the alternatives is true. What are your usual hours of work?” Problem: Does respondent have usual hours of work? Better Question: What are your usual hours of work, or do you not have usual hours? Practice Task 1 Directions: Here are some survey questions from a questionnaire. Examine them closely and identify whether they are acceptable or not. Write A for acceptable. For any non-acceptable question, revise the question to make it acceptable. Write your answers on a separate sheet of paper or in your notebook. Example: (for non-acceptable) Question: Was the school facility not unclean? Revision: How would you rate the cleanliness of the school facility? 1. How awesome is the service provided? 2. Where do you enjoy drinking milk tea? 3. How would you rate the preparedness and rescue mission? 4. What device do you usually use to check your email? A. Computer B. Mobile Phone C. Tablet D. iPad 5. How was our service today? Okay Good Fantastic Unforgettable Mind-blowing 6. 7. What Senior High School Track are you currently enrolled in? a. Academic b. TVL c. Arts and Design d. Sports 8. Which of the following options best describes your employment status? o Employed (Full-time) o Employed (Part-time) o Homemaker o Retired o Not currently employed 9. Who did you purchase the product for? 1. Self 2. Family member 3. Friend 4. Colleague 5. Others, please specify ______________________ 10. Does Research contribute to your stress/anxiety level? TYPE OF QUESTIONS AND ITS USAGE 1. Open-Ended Best Used for:  Breaking the ice in an interview  When respondent’s own words are important  When the researcher does not know all the possible answers Example: What changes do you recommend for the school to do in order to help students perform better? _________________________________________________________________________ _________________________________________________________________________ 2. Closed-Ended Best Used for:  Collecting rank ordered data  When all response choices are known  When quantitative statistical tool results are desired Example: In which of the following do you live? o A house o An apartment o A condo unit Other forms closed-ended questions: a. Likert-Scale Best Used for: Assessing a person’s opinion and feelings about something Assessing the subject’s agreement/disagreement or approval/ disapproval on a five point scale-with one end being the most positive answer, and the other end being the most negative answer. The categories correspond to the numerical values 5,4,3,2,1, and are encoded as their numerical equivalent (Singh 2007,75). The total score per item is determined. From here, you formulate your inference. Example: Performance-Based Incentive System The new performance-based incentive system encouraged me to work over-time. b. Multiple Choice Best Used for:  When there are finite number of options Example: Which of the following best describes your current civil status? o Single o Married o Widowed o Divorced c. Rating Scales Best Used for:  Rate things in relation to other things Example: d. Ranking Questions Best Used for:  Ordering answer choices by way of preference. This allows you to not only understand how respondents feel about each answer option, but it also helps you understand each one’s relative popularity. Example: e. Dichotomous Questions Dichotomous questions have two possible answers, often either yes/no, true/false, or agree/ disagree. These questions are used when the researcher wants to clearly distinguish the respondent’s opinion, preference, experience or behavior. Example: HIV/AIDS is transmitted through saliva: o Yes o No f. Multiple- Response Questions There are certain questions that necessitate the respondents to provide more than one answer. For example, a typical advertising survey would ask the question, “How did you find about the particular service or item”? A respondent may have encountered more than one of the probable ways. Example: How were you able to know about the graduate program of Development Policy offered in De La Salle University? Check all that applies. g. Matrix Questions There are instances where a number of questions you intend to ask have the same set of possible answers. Thus, it is possible to construct a matrix of items and answers for the sake of streamlining the survey. Example: Qualities of a Good Leader Beside each of the qualities of a good leader, kindly indicate how well the person in inquiry manifests the said quality with 1 being the lowest and 5 as the highest. h. Contingency Questions Contingency questions are intended for certain respondents only, depending on the provided answers. A familiar example would be a follow-up question provided after a respondent agrees to a certain item. A respondent is asked whether they used any illegal drugs or substances. Only those who answered yes are required to answer the succeeding items. ORDERING THE QUESTIONS (Adapted from Contemporary Communication Research by Smith, M.J., 1988) 1. Adapt a general organizational pattern that complements a survey’s research objectives. Two general patterns: o Funnel pattern – begins with broad questions followed by progressively narrower or more specific ones o Inverted pattern – narrowly focused questions are followed by more general ones. 2. Topically related questions should be grouped together. A researcher should group together questions pertinent to a single topic then move to another topic. It is easier for the answer questions this way. 3. Easy-to-answer questions should be placed first. Easy questions serve as motivation. 4. Questions should be ordered to avoid establishing a response bias. *Response Bias – a tendency of a respondent to answer all closed-questions the same way regardless of content. Example: A respondent check “Somewhat agree” to all criteria. TESTING AND REVISING THE QUESTIONNAIRE Have you experienced asking a dressmaker or a tailor sew your school uniform? What does a tailor or dressmaker usually do before finally giving you your sewn uniform? He or she would let you fit it first, right? Why do you think so? That is the same as the questionnaire. You are the tailor and the questionnaire is the school uniform. You need to check if the questionnaire fits the respondents and your target information. No matter how carefully you design a questionnaire, there is always the POSSIBILITY of error. You are always certain to make some mistake. The surest protection against such error is to PRE-TEST the questionnaire in full or in part. (Baxter, L. & Babbie, E., 2003) That is the last part of designing your questionnaire before finally administering and distributing it to your respondents. There are no fixed steps on how to test your questionnaire but here are some general guidelines that might be helpful. Keep in mind that you are aiming for the questionnaire to be as effective as it can be. Some Practical Tips on Testing a Questionnaire: (Adapted from tools4dev.org) 1. Find 5 to 10 people from your target group 2. Ask them to complete the survey while thinking out loud. *take note of their opinions and feedback 3. Observe how they complete the survey. *note their hesitations or where they made mistakes in answering. This is an indication that the survey questions and layout are not clear enough and needs improvement. Look at this example:  4. Make improvements based on the results. Other Reminders: ✔ All questionnaires need an introduction. Be sure to have one. ✔ It is useful to begin every questionnaire with basic instructions for completing it. ✔ The format of a questionnaire is as important as the wording and ordering. Be sure that it is spread out and uncluttered. ✔ Physical aspects such as page layout, font type and size, questions spacing, and the type of paper should be considered. NOTES 2 METHODS IN GATHERING DATA Survey The very aim of conducting a survey is to present and explain the actual experiences of a certain population. Conducting survey is done in three (3) steps: 1) by email; 2) telephone; 3) personal interview. The method of data collection can be from observation to content analysis and this can be used in the survey. The challenges limitations of a survey are seen according to the following criteria: 1) appropriateness of the method; 2) accuracy of what to observe; 3) generalizability of findings; 4) administrative constrains; 5) ethical and political difficulties. An example of a survey is the open-ended questions. This is placed in a box form and will permit your respondents to provide a unique answer. This kind of approach is able to provide the respondents the freedom to say what they feel about a topic, which provided you with an exploratory data that may unleash important issues, opportunities, issues, or quotes. (Buensuceso, Dacanay, Manalo, and San Gabriel, 2016, p101) The positive side of this method is that it’s very time efficient. It’s very quick to just come up with a question, ask someone a question and get their answer then record it. This is also nice because you get the right to the point in your question, you’re designing the question to get the exact information you’re looking for so it focuses on the desired response. The negative side of the survey is it’s very likely you could get biased responses and remember biased responses caused us to get skewed data and the reason you could get a biased response is because when you ask someone a question the wording is very important. The wording of the question or the way someone interprets your question can cause you to have biased responses. So you have to careful with how you will ask and formulate your question/s. THREE STEPS IN CONDUCTING A SURVEY There are three steps in conducting a survey. 1. Decide on a four or five option survey question. Then make a tally chart having its heading and appropriate title. The question should follow the guidelines of making an effective survey question. Formulate questions that address to the aim and need of the research. The question should  be clear, concise and efficient. The heading and the title should reflect the focus of the survey. 2. Conduct a survey then tally all the answers. In conducting a survey, ethics should be observed. You should be polite and show respect to the respondents. You should maintain a friendly atmosphere so that respondents may not feel so intimidated. Make sure all answers are noted. Plan for a more systematic way of tallying. 3. Count the answers marking the item having the least to the greatest tallies. Then make a graphic representation of the results. Be careful in tallying so you should observe accuracy and honesty. Results can be presented using any graphics. Most commonly used are charts and organizers. Choose the most appropriate graphics that best represent the result of the survey. Observation According to Buensuceso, Dacanay, Manalo, and San Gabriel (2016), Observation may take place in natural settings and involve the researcher taking lengthy and descriptive notes of what is happening. It is argued that there are limits to the situations that can be observed in their ‘natural’ settings and that the presence of the researcher may lead to problems with validity. Limitations with observation include: a. Change in people’s behavior when they know they are being observed. b. A ‘snap shot’ view of a whole situation c. Think Big Brother… d. The researcher may miss something while they are watching and taking notes e. The researcher may make judgments, make value statements or misunderstand what has been observed Strength of observation a. Can offer a flavor for what is happening b. Can give an insight into the bigger picture c. Can demonstrate sub-groups d. Can be used to assist in the design of the rest of the research e. Sometimes, the researcher becomes or needs to become a participant observer, where he/she is taking part in the situation in order to be accepted and further understand the workings of the social phenomenon. Techniques for collecting data through observation: Written descriptions - The researcher makes written descriptions of the people, situations or environment - Limitations include Researcher might miss out on an observation as they are taking notes The researcher may be focused on a particular event or situation There is room for subjective interpretation of what is happening Video recording - Allows the researcher to also record notes - Limitations may include People acting unnaturally towards the camera or others avoiding the camera The camera may not always see everything Photographs and artifacts - Useful when there is a need to collect observable information or phenomena such as buildings, neighborhoods, dress, and appearance - Artifacts include objects of significance – memorabilia, instruments, tools and others. Documentation Any and all kinds of documentation may be used to provide information – a local paper, information on a notice board, administrative policies and procedures. The positive side of this is that, in this situation you can acquire more details and it’s different from just saying “Do you like rock music?” and getting a Yes or No answer. If you’re observing how someone responds to you playing rock music you’re going to get more data, you’re going to record specifically how they react. The negative side of this method is that it is time-consuming and it can be difficult to observe somebody for a longer period of time. So with rock music, it is not that hard to just play rock music and see how someone reacts but depending on what your study is all about, you might need to be spending more time observing them to get the information you need. This method takes more time and it is not also perfect because you don’t have a control group. Those are the group of people that you are giving different kind of music and seeing how they respond to that. Experiment According to Murdock (2020), in this method, you will randomly select people and you need to split them into groups and they will now your control group. Let’s consider again the example given. You want to know what proportion of your English class likes rock music. In this situation with rock music, your control group could be having a group that listen to a different genre of music so that way you would be observing one group listening to rock and one group listening to something else and you could actually compare. It would show you if people are responding a certain way just because you are giving them a certain treatment versus how do they actually feel about rock music. So the positive side of this is that the control group reduces bias whereas in the survey and observational study, you didn’t have a control group. It also allows you to determine if there’s a cause and an effect happening. So it will really just give you a much deeper understanding of how people are behaving based on your treatment to them, especially to the example medication, you can really determine if the medication is helping people or not. The negative side of this method is, it is time consuming because you’re going have two different groups. It is a little bit harder to keep track of who’s in which group, how are you treating each group, and also this method always have to be concerned with ethics meaning you are not going to persuade them to behave in certain way or treating them in a way that is not okay. Practice Task 2 It’s Your Turn Gathers Information from Surveys and Interviews (Quantitative and Qualitative Data) Data Analysis A data analysis also lends credibility to the researched data. It backs the data up with trustworthy references and gives it a theoretical base to stand on. Data Analysis is also an easy way to evaluate the students regarding their understanding of the research material in general. Your data is the backbone of your research. It is the base on which the entire study will rely upon. After months of grueling researches, scholars amass large amount of data. This data has to be properly integrated and kept in an organized fashion. This article will discuss about the importance of data analysis in a research paper. Providing an insight and interpretation in the form of analysis of the entire data also rules out any chance of human bias. The reader would get a clear and straightforward picture. Similarly, the researcher being devoid of loopholes and hanging ends would deliver the precise intended message across without any incidence of the reader getting biased (Strauss, et al, 1990). A. Process of Quantitative Data Preparation/Analysis Step 1. Data Preparation. Your main task in this step is to collect and prepare data you’ve gathered from a survey. Your aim is to convert raw data into something meaningful and readable. Step 2. Data Validation. The purpose of data validation is for you to find out, as far as possible, whether the data collection was done as per the pre-set standards and without any bias. It is a four-step process, which includes… 1. Fraud, to infer whether each respondent was actually interviewed or not. 2. Screening, to make sure that respondents were chosen as per the research criteria. 3. Procedure, to check whether the data collection procedure was duly followed. 4. Completeness, to ensure that the interviewer asked the respondent all the questions, rather than just a few required ones (Black, 1999). Step 3. Data Editing. Typically, large data sets include errors. For example, respondents may fill fields incorrectly or skip them accidentally. To make sure that there are no such errors, the researcher should conduct basic data checks, check for outliers, and edit the raw research data to identify and clear out any data points that may hamper the accuracy of the results. (Black, 1999) For example, an error could be fields that were left empty by respondents. While editing the data, it is important to make sure to remove or fill all the empty fields. Step 4. Data Coding. Data coding is the process of converting data collected into numeric format. To facilitate the coding process, a codebook should be created to guide the coding process. A codebook is a comprehensive document which contains detailed description or explanation of the following: 1. each variable in a research study, 2. items of measures for that variable, 3. the format of each item (numeric, text, etc.); 4. the response scale for each item (whether it is measured using The four levels of measurements include (Yamashita & Espinosa, 2015): nominal data: basic classification data; lack logical order - e.g. male or female ordinal data: has logical order but lack constant differences between values – e.g. Pizza size (large, medium, small) interval data: has logical order, is continuous, has standardized differences between values but lacks natural zero – e.g. Celsius degrees  ratio data: has logical order, is continuous, has standardized differences between values, and has a natural zero – e.g. height, weight, age, length 5. After identifying a level of measurement, the next step is to use a specific analysis technique in analyzing data. There are several procedures that can be used to analyze data. Main ones include (Yamashita & Espinosa, 2015): Data tabulation (e.g. frequency distributions & percent distributions) Data descriptives (e.g. Mean, medium, mode, minimum and maximum values, etc.) Data disaggregation (tabulation of data across multiple categories) Moderate and advanced analytical methods (regression, correlation, variance analysis) Source: Bhattacherjee 2012, Step 5. Data Entry or Data Recording. After you’ve finished coding the data, your next task is to transfer the information from survey questionnaires or code sheets to computer files for processing. It is done more quickly and more accurately if two persons work together- one reading and typing/entering information (Black, 1999). Smaller data sets with less than 65,000 observations and 256 items can be stored in a spreadsheet such as Microsoft Excel, while larger dataset with millions of observations will require a database. Step 6. Data Transformation. Data transformation is the process of converting data from one format or structure into another format or structure (Black, 1999). Step 7. Data Cleansing. This involves double checking of the data that you’ve entered in the computer. This is important specifically if there are large numbers of respondents (Black, 1999). B. Process of Qualitative Data Analysis According to the National Science Foundation (1997): Qualitative analysis is “unguided by universal rules; has a fluid process that is greatly dependent on the evaluator and to the context of the study.” This involves the identification, examination and interpretation of patterns and themes in textual data. This also determines how these patterns and themes help answer the research questions. Start the analysis process by “getting to know” your data. You do this by listening to your tapes, transcribing interviews from tape to paper, and reading over the written transcripts. Formal systems for the analysis of qualitative data have been developed in order to help researchers get at the meaning of their data more easily. These systems involve: 1. GETTING TO KNOW THE DATA - Reading, listening and playing the recorded responses and taking down notes. 2. FOCUSING THE ANALYSIS - focus by question or topic - focus by case, individual or group 3. CODING - Categorizing the data - is the process of analyzing the data and searching for essential information that answers the research questions. They are considered essential if they occur of have been mentioned several times by the informants. In other words, it is a process of filtering the data (Farber 2006). These essential words are marked or labeled (coded). Codes are words that represent themes or patterns. There are two types of codes in qualitative research: emergent and preset (Taylor- Powell and Renner 2003). Emergent codes are those that show up during analysis while preset are codes that have been identified prior to analysis. Qualitative researchers use codes to easily identify meanings and group similar patterns or themes that occur or transpire in the interview transcript of each participant. By using codes, the researcher can easily make an inference or analysis. 4. ENTERING DATA - Encoding and saving the file. 5. EDITING/REVISING - Editing is checking the format, grammar, etc. -Revising is checking the content and logical organization. 6. IDENTIFYING MEANINGFUL PATTERNS AND THEMES - Content Analysis – identifying patterns ideas, concepts, behaviors, incidents, terms or phrases used and interpreting their meanings. - Thematic Analysis – analyzing the data by grouping them according to themes. 7. INTERPRETING THE DATA - After identifying the themes and patterns and after analyzing the identified themes and patterns, these must be synthesized as a whole. Meaning and significance are attached to the analysis of data. This plus the patterns and themes identified will all help formulate the Findings, Conclusions and Recommendations of the study. Example: Research Title: The Effects of Cyberbullying among Teenagers Statement of the Problem 1. What are the experiences of selected teenagers in relation to cyberbullying? 2. What are the effects of cyberbullying among these teenagers particularly in the following variables:  a. Physical b. Psychological c. Emotional d. Social This chapter presents the data gathered by the researchers regarding the cyberbullying experiences of teenagers as well as the effects in their (a) physical, (b) psychological, (c) emotional and (d) social aspects of their life. Similarly, this section provides the analysis of these data and finally the interpretation of these analyzed data. Cyberbullying Experiences. Respondent A Respondent A is a girl, 17 years of age and an active Facebook user. Her first experience of bullying is…. Gathering Information from Observations Types of Definition Examples Strengths and Weaknesses Observation Participant vs. Non-participant For example, a For example, a researcher who Non- observation: the researcher who wants wants to study “paghagot” could participant researcher is to study “paghagot” first watch parhagots they are Observation separate from the could first watch often most effective when activity. parhagots (i.e. non- used together to develop a participant more complete picture of what’s observation) to get an being studied. overview of how they do their job. Participant Then the researcher observation: the could participate in researcher is “paghagot” (i.e. involved in the Participant activity observation) to directly interact with the ‘parhagot’ and  learn more about its internal dynamics. Simple observation: The researcher Both of these forms of the researcher counts how many observation are most valuable collects simple students fail when used together to numerical data mathematics class in understand details within a a specific grade level bigger picture. For example, a Behavioral How engaging a researcher may combine simple observation: the lecturer on Observational data (how many researcher Mathematics is or people attend a workshop) with Simple vs. interprets people’s how motivated the behavioral observational data Behavioral behavior “Failing students” are (how actively people participate Observation in the said subject in the workshop) to assess how matter? effective a workshop is. Even seasoned professionals are susceptible to researcher bias — errors due to bias and mental shortcuts. Watch out for these shortcomings that can discredit even the best surveys. Direct observation: (e.g. they are Direct observation is valuable the researcher watching students in because it offers real-time observes an activity the cafeteria at lunch information. Its weakness, as it happens to learn about their however, is that it misses eating habits) anything outside of the observation. Direct vs. Indirect (e.g. they examine the The value in indirect Indirect observation: the trash left over after observation lies in the fact that Observation researcher students’ lunches to it is non-invasive and people’s observes the learn about their food behavior will not be affected by results of an activity waste habits) the presence of an observer. Its weakness, however, is that information collected could be limited depending on what is being indirectly observed. Covert observation: Covert observation Covert observation raises the researcher takes places when a immediate ethical issues (since observes secretly researcher is people involved in a study observing the activity should give informed consent in secret (perhaps first). However, covert through a hidden observation allows researchers video camera). to access groups that otherwise would not participate in studies, Covert vs. allowing researchers to expand Overt knowledge on lesser-known Observation social groups. Overt observation: In overt observation, An advantage of overt people know the as the name observation is that it lets researcher is describes, the people researchers be honest with observing them. being observed know participants and tell them a researcher is they’re being observed. This observing them. avoids any ethical issues, like the lack of informed consent. However, a related  disadvantage is that the participants understand the aims of the observer, so they’re more likely to alter their behavior. Observational data is a valuable form of research that can give researchers information that goes beyond numbers and statistics. In general, observation is a systematic way to collect data by observing people in natural situations or settings. There are many different types of observation, each with its strengths and weaknesses (Ferguson, 2018). Sample Coding Summary