Research in Daily Life 2 - 1st Sem Finals PDF

Research in Daily Life 2 1^st^ Sem -- Finals **LESSON 1: RESEARCH ETHICS** Ethics - norms for conduct that distinguish between acceptable and unacceptable behavior Research Ethics - application of fundamental ethical principles to research activity Ethical Principles 1\. Honesty -- r...

Research in Daily Life 2 1^st^ Sem -- Finals **LESSON 1: RESEARCH ETHICS** Ethics - norms for conduct that distinguish between acceptable and unacceptable behavior Research Ethics - application of fundamental ethical principles to research activity Ethical Principles 1\. Honesty -- report data, results, publication statues, methods, procedures. Honesty without fabricating, falsifying, and misinterpreting 2\. Objectivity -- strive to avoid bias data interpretation and other aspects of research where objectivity is expected or required 3\. Integrity -- keep your promises and agreements; act with sincerity; strive for consistency of thought and action 4\. Carefulness -- avoid careless errors and negligence. Keep good records of research activities such as data collection, research design, and correspondence with agencies 5\. Openness -- share data, results, ideas, tools, resources. Be open to criticism, and new ideas 6\. Respect for Intellectual Property -- honor patents, copyrights, and other forms of intellectual property. Do not use unpublished data, methods, or results without permission. Give credit where credit is due 7\. Confidentiality -- protect confidential communications and personal information of your respondents, if any 8\. Social Responsibility -- strive to promote social good and prevent or mitigate social harms through research, public educations, and advocacy 9\. Human Subject Protection -- when conducting research on human subjects, minimize harms and risks, and maximize benefits 10\. Competence -- maintain and improve your own professional competence and expertise through lifelong education and learning 11\. Animal Care -- show proper respect and care for animals **LESSON 2: DATA ANALYSIS IN RESEARCH** Data Analysis -- process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques - process used by researchers to reduce data to a story and interpret it to derive insights Methods Use for Data Analysis in Qualitative Research - Text Analysis - Content Analysis - Thematic Analysis - Narrative Analysis - Discourse Analysis - Grounded Theory Analysis Text Analysis - identify the who, what, when, where, why, and how of a text - Who wrote it and for whom? Consider the author and audience - What was written? Consider what type of text you are analyzing, is it an informative newspaper article or a speech - When was it written and read? Consider the historical context - Where was it written and read? Consider the place and culture in which the text was written - Why was it written and read? Consider the author's intention behind writing the text - How was it written? Consider the purpose of a text. Will analyze the text's structure, central idea, characters, setting, vocabulary, rhetoric, and citations Content Analysis - used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts - e.g. Campaign speech/political speeches - unemployment, jobs, work, and other issues (frequency of use) Thematic Analysis - a research will have to go through the entire transcript and look for meaningful patterns in themes across the data. The patterns can be analyzed by repetitive data reading, data coding, and theme creation Grounded Theory - attempts to uncover the meaning of people's social actions, interactions, and experiences. Grounded because they are grounded in the participants' own explanations or interpretations Narrative - stories, interviews, life histories, journals, photographs, and other artifacts Methods Use for Date Analysis in Quantitative Research a\. Descriptive Statistics - Measure of Frequency - Measures of Central Tendency - Measures of Dispersion or Variation - Measures of Position b\. Inferential Statistics - Correlation - Regression - ANOVA Inferential Statistics 1\. Correlation -- shows a relationship between two variables e.g. Jingle increase in sale Married happy life good grades successful career 2\. Regression Analysis -- relationship between a set of the dependent and independent variable e.g. Age height body weight cholesterol level 3\. Analysis of Variance -- used to determine whether the influence of different independent variables on the dependent variable e.g. Hair color and attraction **LESSON 3: QUANTITATIVE DATA ANALYSIS\ ** Choosing the right statistical test - statistical tests are used in hypothesis testing. They can be used to: - determine whether a predictor variable has a statistically significant relationship with an outcome variable - estimate the difference between two or more groups What does a statistical test do? - work by calculating a test statistic -- a number that describes how much the relationship between variables in your test differs from the null hypothesis of no relationship When to perform a statistical test - you can perform statistical tests on data that have been collected in a statistically valid manner -- either through an experiment, or through observations made using probability sampling methods To determine which statistical test to use, you need to know: 1\. Whether your data meets certain assumptions 2\. The types of variables through you're dealing with Types of Variables a\. Quantitative Variables - Continuous - Discrete b\. Categorical Variables - Ordinal - Binary - Nominal Choosing a Parametric Test: Regression, Comparison, or Correlation - usually have stricter requirement than nonparametric tests and are able to make stronger inferences from the data - most common types include regression tests, comparison tests, and correlations tests Tools for the Respondents' Profile 1\. Frequency and Percentage Distribution 2\. Measures of Central Tendency a\. Mean -- average of a set b\. Median -- middle number in a set c\. Mode -- most common number in a set Comparison Tests - look for differences among group means. Used to test the effect of a categorical variable on the mean value of some other characteristic T-tests - used when comparing the means of precisely two groups - used in hypothesis testing to determine whether a process or treatment actually has an effect on the population of interest, or whether two groups are different from one another - e.g. the average heights of men and women The result of the T-test will determine whether your hypothesis is accepted or not Ha -- varied reading materials can improve the reading comprehension of the students Ho -- varied reading materials does not help in improving the reading comprehension of the students Correlation Tests - check whether variables are related without hypothesizing a cause-and-effect relationship 1\. Correlating 2 nominal data = Chi-square Test of Goodness Fit Chi-square Test of Independence Lambda Coefficient 2\. Determining significant relationship or association between 2 ordinal variables = Pearson-Product Moment Correlation Chi-square Test of Goodness Fit Spearman Rank Order Correlation Gamma Coefficient 3\. Determining significant relationship or association between an ordinal data and a nominal data = 4\. Tools to determine significant differences T-test or Z-test One-way ANOVA Two-way ANOVA 5\. Tools for experimental research T-test or Z-tests One-way ANOVA **LESSON 4: SAMPLING IN QUANTITATIVE RESEARCH** Sampling - process of obtaining the participants of a study from a larger pool of potential participants turned as the populations Non-Probability Sampling 1\. Convenience 2\. Purposive 3\. Snowball 4\. Quota 5\. Volunteer Probability Sampling - each number population has a chance of being selected Types of Probability 1\. Simple Random Sampling -- equal chance of being selected Sampling procedure: a\. Assigns a member to all members of the population b\. Randomly select or draw a predetermined number by using a table of random numbers 2\. Stratified Random Sampling -- dividing the population into homogeneous sub-groups and then taking a simple random sample in each group a\. Divide into subgroups or strata b\. Randomly select members for each subgroup 3\. Systematic Random Sampling -- when simple random or stratified is too complicated, use this - nth number of participants 4\. Cluster or Area Sampling -- when members of population are dispersed across or wide geographic area, you may use this a\. Divide population into cluster using geographic boundaries b\. Randomly select clusters c\. Randomly select units from each sampled cluster How to calculate sample size Slovin's Formula n = N / 1 + Ne 2 n= Sample size; N = population; e = margin of error - Margin of error is set at 0.05 when study is descriptive - The smaller the margin of error, the larger the sample size **LESSON 5: QUANTITATIVE RESEARCH DESIGNS** Experimental Research Design 1\. True Experimental - cause and effect relationship of variables involved in the study - scientific method - may use laboratory - variable manipulation - randomly formed groups - comparison among groups - statistical analysis - most accurate type of design - with or without pre-test - control and text group - intervention is present 2\. Quasi-Experimental - rigid manipulation of variables - intact groups or participants - also determine causal relationships - non-randomly assigned - pretest and posttest - intervention is present - no control groups Non-Experimental Research a\. According to Purpose 1\. Descriptive - describes characteristics of the population or phenomenon studied - focuses on describing nature of a demographic segment - current status of variables - gives information - does not begin with a hypothesis - tests hypothesis - what caused a situation, can't be used for causal relationship - does not cover why something happens - large sample size - uncontrolled variables 2\. Predictive/Correlative (Correlational) - forecasting event or phenomenon - cause and effect not necessary - does not know factors yet - relationship between 2 or more variables - variables that seem to interact with each other - no variables are changed (no intervention) - variables are only measured 3\. Explanatory - develop tests - test a theory - explain how and why something operates - identify causal factors 4\. Causal-Comparative - established cause-effect relationships - independent variable is established but not manipulated - determine the cause or consequences of differences that already exist between groups or individuals According to Dimension 1\. Cross-sectional - data collected in a single point in time - comparisons are made across the variables of interest 2\. Retrospective - comparisons are made between the past and the present for the cases in the data 3\. Longitudinal - data collected at the present and collected again sometime in the future - from present to future **LESSON 5.5: RESPONDENTS AND PARTICIPANTS** Participants - quantitative Respondents - qualitative **LESSON 6: DATA COLLECTION AND ANALYSIS PROCEDURES** Data Collection - systematic process of gathering the research data - allows the researcher to gain first-hand knowledge, information, and original insights into the research problem 1\. Close-Ended Survey and Questionnaires - involve structured questionnaires with a limited number of close-ended questions and rating scales used to generate numerical data Close-Ended Survey - data collection method - tool used: close-ended questionnaire 2\. Structured Close-Ended Interview - systematically follows carefully organized questions that only allow a limited range of answers, such as "yes/no" or expressed by a rating/number on a scale Closed-Ended (Structured) Interview - tool used: Interview Schedule (Structured Interview Schedule) - organized systematically - questions should be given the same way to each respondents or participants 3\. Experimentation/Experimental Research - effect of the independent variables on the dependent variables is observed and recorded to draw a reasonable conclusion 4\. Structured Observation - prepared items that you will just observe on a particular subject Structured Observation - tool used: Observation Guide **LESSON 7: RESEARCH INSTRUMENT** Relationship of RRL to Questionnaire - review of related literature and studies must have sufficient info and data to enable the researcher to thoroughly understand the variables being investigated in the study - Indicators for the Specific Variable: refer to the descriptive info gathered from different sources - used to make sure that the content of the questionnaire is valid Types of Questions 1\. Yes or No Type - items are answerable by yes or no 2\. Recognition Type - choice/alternatives responses are provided - contains close-ended questions 3\. Completion Type - respondents fill in the blanks with necessary information - contains open-ended questions 4\. Coding Type - numbers are assigned to names, choices, etc - application of statistical formula is necessary 5\. Subjective Type - respondents are free to give their answers/opinions on issue or topic of concerns 6\. Combination Type - use of two or more types of questions Wordings of Questions 1\. Questions are stated in affirmative manner 2\. Avoid ambiguous questions 3\. Refrain from using double negative questions 4\. Avoid double-barreled questions (2 questions in one statement) Commonly Used Scales 1\. Likert Scale - common rating system used in questionnaires - designed to measure people's attitudes, opinion, or perceptions - consists of declarative statements that express a viewpoint on a topic 2\. Semantic Differential Scale - used to ask people to rate a product, brand or any entity within the frames of a multi-point rating option - flexible and easy to construct - Example: Expensive -- Inexpensive Characteristics of Good Data Collection 1\. Concise but able to elicit needed data 2\. Seeks info that cannot be obtained from other sources 3\. Questions are arranged in sequence (simple to complex) 4\. Questions are arranged to SOP 5\. Should pass the validity and reliability 6\. Easily tabulated and interpreted **LESSON 8: PRESENTATION, ANALYSIS, INTERPRETATION OF DATA** Behind the scenes process: 1\. Developing the research instruments/adapting or adopting an instrument - Developing questionnaire (don't copy and paste) 2\. Distributing the instrument to respondents/participants 3\. Collecting data - Plan your data collection 4\. Applying statistical technique/computation to the data PRESENTATION OF DATA - data are usually presented in charts, tables, or figures with textual interpretation Common tools used in data presentation 1\. Graph/Figure - shows relations, comparisons, and distributions in a set of data like absolute values, percentages, or index numbers - Types of Graphs: - Area Graph, Column Graph, Bar Graph, Line Graph, Pie Graph 2\. Table - provided exact values and illustrated results efficiently as they enable the researcher to present a large amount of data in a small amount of space - Elements of a Table: Title, Rows, Label, Columns, Data ANALYSIS OF DATA Data Analysis - process of inspecting, rearranging, modifying, and transforming data to extract useful information from it Reminders: - maintaining the integrity of data - credible data analyst - improper analysis always distorts the scientific finding and lead the readers to believe in a wrong notion In analyzing data, the following must be considered: 1\. the highest numerical value 2\. the lowest numerical value 3\. the most common numerical value 4\. the final numerical value INTERPRETATION OF DATA Data Interpretation - intelligence and logic of the researcher are required. Analysis and interpretation will be the basis of the findings of the study Levels of Interpretation Level 1 -- data collected are compared and contrasted Level 2 -- explain the internal validity, consistency, and reliability of results Level 3 -- explain the external validity of the results, the applicability to external conditions Level 4 -- related or connect the interpretation of data with theory or with review or related literature **LESSON 9: SUMMARY, CONCLUSION, AND RECOMMENDATION** Remember: 1\. Everything here is based on the results of the data analysis 2\. Restate the main and sub-problems 3\. Reiterate the type of research, nature, size of sample, and place of the study 4\. Enumerate the major findings, if you have hypothesis, tell whether it is accepted or not Purpose of Summary of Findings - to highlight the major statistical findings from the result section and interpret them Remember: - There is NO NEED to put numbers or repeat the statistical computation in the Chapter III - Just explain in simple language the summary of results - You may also related the findings of your study with the other findings CONCLUSION - not a summary of your paper or re-statement of the research problem but a SYNTHESIS of key points - based on the summary of findings and should be aligned with it Characteristics of a Conclusion 1\. Must be short and brief 2\. Should not be a repetition of what is already mentioned previously in other parts of your paper 3\. Must avoid being bias 4\. Must avoid incorrect generalization and limited information RECOMMENDATION - purpose is to solve a new problem that was found in the study conducted - suggest ways for the improvement of the study - recommend for the continuance of the study - address the concerned authorities, persons, agency, or office that in position to implement the action you are recommending (Who, what action to be implemented?) - must be feasible, attainable, doable and practical - may refer to the significance of the study for the construction of recommendation - do not give recommendation outside your studies **LESSON 10: TYPES OF QUESTIONNAIRES** Closed or restricted form of questionnaire - offers respondents a number of alternative replies, from which the subjects must choose the one that most likely matches the appropriate answer Types of Closed form of questionnaires 1\. Dichotomous -- respondent to make a choice between two responses such as yes/no or male/female 2\. Multiple Choice Questions -- respondents to make a choice between more than two response alternatives 3\. Cafeteria Questions -- respondents to select a response that most closely corresponds to their view 4\. Rank Order Questions -- respondents to rank their responses from most favorable to least favorable 5\. Contingency Questions -- questions that is asked further only if the respondent gives a particular response to previous question 6\. Rating Questions -- most favorable to lease favorable, asked to rate a particular issue on a scale that ranges from poor to good 7\. Likert Questions -- helps know how strongly the respondent agrees with a particular statement. These questions help to know how responder feel towards a certain issue 8\. Bipolar Questions -- have to extreme answer between two opposite ends of the scale 9\. Matrix Questions -- includes multiple questions and identical categories are assigned that are placed under another forming a matrix. Response categories are paced along the top and list of questions down on the side