LESSON - CONDUCT SURVEY EXPERIMENT OR OBSERVATION (1).pptx

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1. HOW TO CONDUCT SURVEY,EXPERIMENT OR OBSERVATION QUARTER 2 LESSON

2. To conduct a survey, experiment, or observation , it's essential to approach each method systematically. Here’s a guide for each:

3. 1. Survey A survey is a method for collecting information from individuals, typically through questions. Surveys are useful for gathering a large amount of data on people's opinions, behaviors, or characteristics .

4. Steps TO CONDUCT A SURVEY: I. Define the Research Problem: What do you want to know? Example: If you're studying stress management, your survey could aim to find out "How do people cope with stress in their daily lives?" II . Design the Survey : Types of Questions : Multiple choice, Likert scale, open-ended. Keep questions clear, concise, and unbiased. Example: "How often do you eat fruits per week?"

5. TYPES OF QUESTIONS: A. Multiple Choice Questions Description: Multiple choice questions provide respondents with a set of predefined answers, from which they must select one (or sometimes more) options. These questions are useful when you want to gather specific, quantifiable data from your respondents.

6. Key Characteristics: Fixed Response Options : Respondents are limited to the choices provided. Objective Data : Easier to analyze because the answers are standardized. Clear Focus : Each question targets specific information. Closed-ended : Respondents cannot provide answers outside the given options . Variations: Single Choice : Respondents choose only one option. Example: “What is your primary mode of transportation?” A) Car B) Bus C) Bicycle D) Walking Multiple Choice (Multiple Answers) : Respondents can select more than one option. Example: "Which of the following fruits do you eat regularly? (Select all that apply)" A) Apples B) Bananas C) Oranges D) Grape s

7. Advantages: Efficient : Respondents answer quickly, making it ideal for large surveys. Easy to Analyze : Quantitative data allows for straightforward statistical analysis. Reduced Ambiguity : Predefined answers reduce misinterpretation. Disadvantages: Limited Insight : Doesn't allow for deep understanding of the respondents' reasoning. Restricted Options : Respondents may not feel represented if their preferred option isn’t listed.

8. B.LIKERT SCALE Description: A Likert scale question asks respondents to rate their level of agreement, frequency, or satisfaction on a gradual scale . These questions are commonly used to measure attitudes, opinions, or behaviors, often on a 5-point or 7-point scale (e.g., Strongly Disagree to Strongly Agree).

9. Key Characteristics: Scale of Responses : Typically ranges from one extreme to another, such as agreement or satisfaction. Measures Intensity : Captures the strength of feelings, attitudes, or behaviors. Quantifiable but Nuanced : Responses are numerical (1-5, 1-7) but also reflect nuanced perceptions. Closed-ended : Although nuanced, the scale is still predefined, with no room for open feedback. Variations: Agreement : Example: "I find this app easy to use." 1 = Strongly Disagree 2 = Disagree 3 = Neutral 4 = Agree 5 = Strongly Agree Frequency : Example: "How often do you exercise?" 1 = Never 2 = Rarely 3 = Sometimes 4 = Often 5 = Always Satisfaction : Example: "How satisfied are you with our customer service?" 1 = Very Dissatisfied 2 = Dissatisfied 3 = Neutral 4 = Satisfied 5 = Very Satisfied

10. Advantages: Measures Gradual Differences : Provides more nuanced insights into attitudes and behaviors than simple yes/no questions. Quantifiable : Easy to convert into numbers for analysis (e.g., average satisfaction score). Flexibility : Can be applied to various topics like satisfaction, agreement, importance, or frequency. Disadvantages: Subjectivity : Different respondents may interpret the scale differently (e.g., one person’s “4 = Agree” might mean “Strong Agreement” to another person). Limited Depth : While it offers gradation, it doesn’t explain why respondents feel a certain way.

11. C. Open-Ended Questions Description: Open-ended questions allow respondents to answer in their own words, without being constrained by predefined options. These questions are useful when you want to gather detailed, qualitative data .

12. Key Characteristics: No Predefined Answers : Respondents provide their own thoughts, opinions, or explanations. Deep Insight : Allows for complex, detailed responses that reveal the "why" behind people's choices. Qualitative Data : Produces text-based responses that require interpretation. Exploratory : Useful for discovering new insights or exploring areas you may not have thought of. Variations: General Questions : Broad questions that allow respondents to share their thoughts. Example: "What do you like most about our product?“ Specific Questions : Focus on a particular topic but still leave room for expansive answers. Example: "Describe how you manage stress during exams."

13. Advantages: Rich Detail : Can reveal the reasoning behind people’s thoughts or behaviors, providing in-depth understanding. Unrestricted Responses : Allows respondents to express themselves freely, leading to unexpected insights. Exploratory Nature : Ideal for exploring new areas or gaining initial understanding before using more structured questions . Disadvantages : Time-Consuming for Respondents : Respondents may take longer to answer open-ended questions, leading to potential survey fatigue or incomplete responses. Difficult to Analyze : Responses are qualitative, so they require more time to analyze, often needing thematic coding or qualitative analysis software. Variability in Response Quality : Some respondents may give short or unclear answers, making it difficult to extract useful information.

14. COMPARISON OF THE THREE TYPES Question Type Format Type of Data Best For Ease of Analysis Multiple Choice Fixed answers (A, B, C, D) Quantitative Measuring simple facts or behaviors High Likert Scale Agreement scale (1 to 5, or 1 to 7) Ordinal, Quantitative Measuring attitudes or intensity Moderate to High Open-Ended Freeform response Qualitative Exploring in-depth opinions or ideas Low (requires more effort)

15. III. SAMPLING Target Population : Decide who you want to survey (e.g., age group, profession). Sample Size : Determine how many people you need to survey to get valid results. Methods: Random sampling, stratified sampling, convenience sampling.

16. 1. Random Sampling is a technique where every member of the population has an equal chance of being selected . It is the most straightforward and unbiased sampling method when applied correctly.

17. Key Features: Equal Probability : Every individual in the population has the same likelihood of being chosen. Unbiased : There is no favoritism or systematic exclusion of certain individuals. Representativeness : If done properly, it tends to create a sample that represents the entire population . How to Use Random Sampling: Define the Population : Identify the full set of individuals or units you want to study. Example: If you’re studying job satisfaction, your population could be all employees in a company. Assign Numbers : Assign a unique number to each member of the population. Use a Random Selection Method : Use a random number generator, lottery method, or software (like Excel) to randomly select individuals. Example: If you have 1,000 employees, you could use a random number generator to select 100 employees to participate in your study.

18. When to Use: Best for large populations where you need to eliminate bias. Generalizing results : Results can be generalized to the entire population because the sample is representative. Example: In a study to assess customer satisfaction at a retail store, a random sampling method might be used to select 200 customers out of 10,000. Every customer has an equal chance of being included.

19. 2. Stratified Sampling Stratified sampling is a method where the population is divided into subgroups (strata) that share similar characteristics, and then a random sample is taken from each stratum. This ensures that each subgroup is represented proportionally in the sample.

20. Key Features: Division into Strata : The population is divided into distinct subgroups based on a specific characteristic (e.g., age, gender, income level). Random Sampling within Strata : After dividing the population, individuals are randomly selected from each subgroup. Improved Representativeness : This method ensures that key subgroups are proportionally represented. How to Use Stratified Sampling: Identify Key Strata : Divide the population into subgroups based on a relevant characteristic. Example: If you're studying employee job satisfaction, you might divide employees into strata based on their job role (e.g., managers, clerks, technical staff). Determine the Sample Size for Each Stratum : Decide how many people to sample from each group. This can be proportional (based on the size of each group) or equal. Example: If managers make up 10% of the workforce, 10% of your sample should come from the manager group. Randomly Select Individuals from Each Stratum : Use random sampling within each stratum to select the participants. Example: From a company of 500 employees, where 50 are managers and 450 are clerks, you might sample 10 managers and 90 clerks.

21. When to Use: When the population has distinct subgroups that you want to ensure are represented in your study. Improving accuracy : When specific subgroups are small but critical to the analysis, stratified sampling ensures they are adequately represented. Example: In a study on political opinions, you may divide the population into subgroups by age (e.g., 18-29, 30-49, 50-69, 70+), then randomly sample from each age group to ensure that all age brackets are represented. Advantages: Ensures representation of all subgroups : This method guarantees that important subgroups are not underrepresented. Improves precision : Because it ensures proportionate representation, the results tend to be more accurate and reflective of the population. Disadvantages: More complex : It requires knowledge about the population and the ability to divide it into relevant strata. Time-consuming : Dividing the population into strata and conducting random sampling within each can take more time and effort compared to simple random sampling.

22. 3. Convenience Sampling Convenience sampling involves selecting participants who are easily accessible or convenient to reach. This method is non-random and often used when time, resources, or access to the full population are limited.

23. IV . Distribution : Online (Google Forms, SurveyMonkey), phone, or in-person. Make sure your survey reaches your intended audience. V . Collect Data : Distribute the survey and ensure responses are recorded properly. VI . Analyze Data : Organize and interpret the responses. Statistical tools like Excel or Google Sheets can be helpful. VII . Report Results : Summarize key findings and draw conclusions based on the survey results.

24. 2. Experiment An experiment tests a hypothesis by manipulating one or more variables while controlling others.

25. STEPS: 1. Identify the Hypothesis : What are you testing? Example: "Does exercise reduce anxiety?" 2. Variables : Independent Variable : What you change (e.g., amount of exercise). Dependent Variable : What you measure (e.g., level of anxiety). Control Variables : Factors you keep constant (e.g., diet, sleep). 3. Design the Experiment : Experimental Group : The group exposed to the independent variable. Control Group : The group not exposed to the independent variable (acts as a baseline). Ensure random assignment to reduce bias.

26. STEPS: 4 . Conduct the Experiment : Follow the procedure consistently for all participants. Collect data (e.g., anxiety levels before and after exercise). 5. Analyze Data : Use statistical tools to determine if the results are significant. Example: T-test to compare anxiety levels between the two groups. 6. Draw Conclusions : Based on the data, accept or reject your hypothesis. 7. Report Findings : Share the process and results through a written report or presentation .

27. 3. Observation Observation involves systematically watching and recording behaviors or events without interference. This method is often used in social sciences or natural settings .

28. STEPS: 1. Define the Purpose : What do you want to observe? Example: "I want to observe how people interact in group settings." 2. Decide on Structured vs. Unstructured Observation : Structured : You have a predefined checklist or criteria. Unstructured : You watch and record what happens naturally. 3. Setting : Natural Setting : The observation occurs in a setting where people behave naturally (e.g., a classroom, workplace). Controlled Setting : The observation is done in a lab or simulated environment where variables can be controlled.

29. STEPS: 4. Decide on Participant vs. Non-Participant Observation: A. Participant Observation : You engage with the group while observing them. Example: You are part of the team meeting while also observing behaviors. B. Non-Participant Observation : You do not interact, simply observe from a distance. Example: Watching from the back of the room.

30. STEPS: 5. Record Data : a. Field Notes : Write down observations in real time. b. Video/Audio Recordings : Useful for capturing detailed behaviors or interactions for later analysis. c. Checklists : If you are using structured observation, you may have a checklist of behaviors or actions to look for (e.g., number of interruptions, gestures of agreement). 6. Analyze the Data Quantitative : Record behaviors or events as numbers (e.g., how many people speak in a meeting). Qualitative : Record descriptions or patterns (e.g., the type of body language used).

31. Steps: . Avoid Bias : Ensure you don’t influence the participants' behavior by your presence. 7. Report Findings : Present observations in a logical, clear manner with examples or relevant data points .