Conduct-of-Research_for_Beginning_Researchers_matdevFIN_draft_.pdf

Research 2 Research for Beginning Researchers Jovenal V. dela Cruz, Jr. Hentry T. Aguilar Gregie P. Tampon  Research 2: Research for Beginning Researchers PHILIPPINE NORMAL UNIVERSITY The National Center for Teacher Education MINDANAO The Multicultural Edu...

Research 2 Research for Beginning Researchers Jovenal V. dela Cruz, Jr. Hentry T. Aguilar Gregie P. Tampon  Research 2: Research for Beginning Researchers PHILIPPINE NORMAL UNIVERSITY The National Center for Teacher Education MINDANAO The Multicultural Education Hub PUBLICATION AND MATERIALS DEVELOPMENT OFFICE Prosperidad, Agusan del Sur Research 2: Research for Beginning Researchers 1ST EDITION COPYRIGHT©2024 JOVENAL V. DELA CRUZ JR. HENTRY T. AGUILAR GREGIE P. TAMPON ALL RIGHTS RESERVED. No part of this material may be reproduced in any form or by any means without the written permission from the PMDO. Published and exclusively distributed by the PUBLICATION AND MATERIALS DEVELOPMENT OFFICE PHILIPPINE NORMAL UNIVERSITY-MINDANAO PRINTED BY THE PMDO ii  Preface This material arises from the authors' desire to share their knowledge gained from their experiences as graduate students and research educators. Their motivation stems from a genuine desire to help students struggling with the complexities of research courses and new researchers navigate the arduous challenge of initiating and writing research proposals. The knowledge the authors hope to transmit is the result of their own learning journeys guided by their mentors, as well as their responsibilities as teachers and researchers. They are convinced that this knowledge is an invaluable resource for students and new researchers attempting to develop attractive research projects. Consequently, the authors have made diligent efforts to encompass essential ideas, principles, concepts, and the philosophical underpinnings of research methodologies, along with the requisite skills essential for budding researchers to embark on their research endeavors effectively. This material uses commonly used language as much as possible to ensure accessibility of the students. It has been crafted to simplify complex research concepts and ideas for easy comprehension. Additionally, it incorporates observable and straightforward problems and situations to enhance reader-friendliness and relatability. As a result, this material is suitable for use by novice researchers. It comprises 8 chapters. This material and its authors do not assert originality. The research ideas, concepts, and principles as well as examples presented herein are curated from a variety of sources and authors, including the authors’ research mentors and research acquaintances, whom the authors deem essential for novice researchers. Therefore, the authors extend gratitude to all contributors for their ideas, which greatly enrich this material. The authors duly acknowledge these sources in the list of references. The author also expresses his thanks to all the people-mentors, collegues, students, friends and family who help him in one way or another. The authors iii  Contents Preface............................................................................................................................................ iii Chapter 1: Data Collection Methods............................................................................................... 1 Introduction................................................................................................................................. 1 Learning Objectives.................................................................................................................... 1 Essential Questions..................................................................................................................... 1 Exploration.................................................................................................................................. 1 1.1 Data................................................................................................................................ 1 1.2 Data Collection Tools and Techniques.......................................................................... 4 1.3 Principles of Data Collection......................................................................................... 6 1.4 Methods of Data Collection........................................................................................... 7 1.5 Characteristics of a Good Research Instrument/Tool................................................... 15 Learning Activities.................................................................................................................... 16 Checkpoint................................................................................................................................ 16 References:................................................................................................................................ 17 Chapter 2: Validity and Reliability............................................................................................... 19 Introduction............................................................................................................................... 19 Learning Objectives.................................................................................................................. 19 Essential Questions................................................................................................................... 19 Exploration................................................................................................................................ 19 2.1 Validity......................................................................................................................... 19 2.2 Reliabilit........................................................................................................................ 24 Learning Activities.................................................................................................................... 27 Checkpoint................................................................................................................................ 27 References................................................................................................................................. 28 Chapter 3: Sampling Technique.................................................................................................... 29 Introduction............................................................................................................................... 29 Learning Objectives.................................................................................................................. 29 Essential Questions................................................................................................................... 29 Exploration................................................................................................................................ 29 3.1 Basic Concepts............................................................................................................. 29 3.2 Importance of Sampling................................................................................................ 30 3.3 Types of Sampling........................................................................................................ 32 iv  3.4 Probability Sampling................................................................................................... 32 3.5 Sample Size Computation............................................................................................... 39 Learning Activities.................................................................................................................... 43 Checkpoint................................................................................................................................ 45 References................................................................................................................................. 46 Chapter 4: Data Analysis in Quantitative Research...................................................................... 48 Introduction............................................................................................................................... 48 Learning Objectives.................................................................................................................. 48 Essential Questions................................................................................................................... 48 Exploration................................................................................................................................ 48 4.1 Basic Concepts................................................................................................................ 48 4.2 Descriptive Stattistics...................................................................................................... 49 4.3 Inferential Statistics........................................................................................................ 52 4.4 Common Elements in Quantitative Analysis.................................................................. 53 4.5 Hypotheses Testing......................................................................................................... 54 Learning Activities.................................................................................................................... 59 Checkpoint................................................................................................................................ 61 References................................................................................................................................. 61 Chapter 5: Data Analysis in Qualitative Research........................................................................ 63 Introduction............................................................................................................................... 63 Learning Objectives.................................................................................................................. 63 Essential Questions................................................................................................................... 63 Exploration................................................................................................................................ 63 5.1 Basic Concepts................................................................................................................ 63 5.2 Common Qualitative Data Analysis Methods................................................................ 65 5.3 Characteristics Shared by the Different Qualitative Data Analysis................................ 69 5.4 Steps in Doing Qualitative Data Analysis...................................................................... 69 Learning Activities.................................................................................................................... 71 Checkpoint................................................................................................................................ 71 References................................................................................................................................. 71 Chapter 6: Research Data Organization, Interpretation and Discussion in Qualitative Study..... 73 Introduction............................................................................................................................... 73 Learning Objectives.................................................................................................................. 73 Essential Questions................................................................................................................... 73 v  Exploration................................................................................................................................ 73 6.1 Basic Concepts................................................................................................................ 73 6.2 Presentation of Results.................................................................................................... 74 6.3 Interpretation and Discussion......................................................................................... 78 Learning Activities.................................................................................................................... 83 Checkpoint................................................................................................................................ 83 References................................................................................................................................. 83 Chapter 7: Writing the Summery, Conclusion and Recommendation.......................................... 85 Introduction............................................................................................................................... 85 Learning Objectives.................................................................................................................. 85 Essential Questions................................................................................................................... 85 Exploration................................................................................................................................ 86 7.1 Summary of the Study.................................................................................................... 86 7.2 Summary of Fidings........................................................................................................ 87 7.3 Conclusion...................................................................................................................... 89 7.4 Recommendation............................................................................................................ 94 Learning Activities.................................................................................................................... 99 Checkpoint.............................................................................................................................. 100 References............................................................................................................................... 100 Chapter 8: Writing the Research Article..................................................................................... 102 Introduction............................................................................................................................. 102 Learning Objectives................................................................................................................ 102 Essential Questions................................................................................................................. 102 Exploration.............................................................................................................................. 103 8.1 Research Report............................................................................................................ 103 8.2 Characteristics of a Good Report.................................................................................. 103 8.3 Research Report Structure............................................................................................ 104 8.4 Publication Process....................................................................................................... 118 8.5 Tips in Writing and Publishing Articles....................................................................... 118 Learning Activities.................................................................................................................. 120 Checkpoint.............................................................................................................................. 120 References............................................................................................................................... 120 vi  Chapter 1: Data Collection Methods Introduction Data are what the researchers looking for when they conduct research. Data are needed for them to answer the research questions. Data in research are not just any kind of data taken anytime, anywhere but are taken with carefull considerations of lots of factors. Thus, the quest for collecting data necessitate to have a good data collection methods to capture the best quality data which the researcher can use as an evidence to answer the research questions. It is also very important to consider that different researches (quantitative and qualitative) need different data thus different data collection is also needed. Regardless of the time, efforts and resources used in the data collection, each has its own value to the researchers. Learning Objectives At the end of the lesson, the students will be able to: a. Discuss the different types of data; b. Discuss the different tools and techniques used in data collection and their advantages and disadvantages; c. Determine the appropriate tools to be used in data gathering; d. Construct data gathering tool for the a research problem; Essential Questions Why do systematic data collection very important? Exploration 1.1 Data It is common association with the word data that it involves numbers. When we say data-driven activities, we might think that it only happens in science. These beliefs might be considered correct but they are very limited description. While it is true that commonly introduced form of data to us are numbers but there are other forms of data that can give meaningful information about a research phenomenon. Data are Data are any collection of unprocessed, raw facts and details, such as numbers, text, observations, graphs, symbols, and objective descriptions of something or phenomenon. By having this description data do not only mean about numbers but anything that can give a meaningful description of a research phenomenon that can be used to answer the research problem. Other examples of these are journal entries, artifacts, videos, audio, expressions and many more.  Data can also be classified ito different classification. It can be classified based on forms as either qualitative or quantitative data. It can also be classified based on the sources as either primary or secondary data. Quantitative versus Qualitative Quantitative data are measures of values or counts and are expressed as numbers. It is about numeric variables (e.g. how many; how much; or how often). Qualitative data are measures of 'types' and may be represented by a name, symbol, or a number code. It can also be about categorical variables (e.g. what type). Table 1. Quantitative versus Qualitative Qualitative Data Quantitative Data numbers-based, countable, or interpretation-based, descriptive, and measurable relating to language tells us how many, how much, or how help us to understand why, how, or what often in calculations. happened behind certain behaviors. fixed and universal subjective and unique Common methods are measuring and Common methods are interviewing and counting observing analyzed using statistical analysis grouping the data into categories and themes When a researcher wants to answer the "what" or "how many" questions in a research topic, they use quantitative data. It consists of information that may be compared numerically or counted. For instance, it may be the proportion of first-year students at Philippine Normal University Mindanao or the evaluations of the implementation of the Modular Distance Learning of the Department of Education on a scale of 1 to 5. This information is typically acquired utilizing tool, such as a questionnaire with a rating scale. Quantitative data analysis frequently https://freeonlinesurveys.com/survey- research/quantitative-data-collection involves the use of statistical analysis tools like SPSS and Mini Tab. 2  When a researcher wants to describes traits or attributes or answer “why”, qualitative data are used. It is commonly gathered through observation or interviews and frequently takes the shape of stories. For instance, it might be the answers to an open-ended question or notes from a focus group on the conceptual understanding about peace education. The data could take the form of descriptive words that can be coded or otherwise analyzed for patterns or significance. The researcher can undertake quantitative analysis and categorize qualitative https://www.ovationmr.com/qualitative-data-analysis/ data using coding to find Primary Data versus Secondary Data Primary Data are data that has been generated by the researcher himself/herself, surveys, interviews, experiments, specially designed for understanding and solving the research problem at hand. In other words they are those that are gathered straight from the data source, bypassing any pre-existing sources. It is typically gathered especially for research projects and may be made publically available for use in other studies. Insofar as it was gathered with the intention of solving a specific study issue, primary data is frequently trustworthy, real, and unbiased. The information gathered by businesses during market research, product research, and competition analysis is a typical illustration of primary data. This information is gathered directly from the source, which is typically the current and potential clients. Secondary data are already-gererated and readily available data used by large government institutions, healthcare facilities, and other organizations to maintain organizational records. The information is then retrieved from a wider range of datafiles. In other words these are data already been gathered and made available for use by others. When employed by a third party, they typically start out as primary data but end up being secondary. Because secondary data are typically shared publicly, researchers and users may usually quickly access them. This implies, however, that the data are typically generic and not precisely designed to satisfy the goals of the researcher, as primary data is. For example, when doing a research study, researchers need to review and consult past works and literature related to the topic under study. Some other things like definitions and theorems are secondary data that are be properly referred to and cited, other elements. Table 2 shows the comparison between the two. 3  Table 2 Primary Data versus Secondary Data BASIS FOR PRIMARY DATA SECONDARY DATA COMPARISON Meaning Primary data refers to the first-hand Secondary data means data collected data gathered by the researcher by someone else earlier. himself. Time Generated Real Time Data Past data Process Active efforts of both the research Readings and reviews of existing and participants data Source Surveys, observations, experiments, Government publications, websites, questionnaire, personal interview, etc. books, journal articles, internal records etc. Tools Interview, surveys, questionnaire, Literature review, library works exam Cost Expensive Economical Collection time Long period Short period Details Always specific to the researcher's May or may not be specific to the needs. researcher's need. Form Crude/raw form Refined form Accuracy and More Relatively less Reliability Examples Ratings, grades, interview transcript Government agency report, published study 1.2 Data Collection Tools and Techniques A research instrument is a device that you can use to gather, quantify, and evaluate information on your research interests. In order to evaluate patients, clients, students, teachers, employees, etc., these instruments are most frequently employed in the social sciences, health sciences, and education. A research tool may be an interview guide, an exam, a poll, or a checklist. Different roles are played by the instruments. You can conduct quantitative, qualitative, and mixed investigations with the aid of various tools. Depending on the kind of research you're doing will determine how you select the instrument. In short, The Research Instrument is usually determined by researcher and is tied to the study methodology.Whatever method you use, it must be explained in your research paper's Methods section. It is more likely that someone else will be able to replicate your study to verify its validity the more thoroughly you can explain it, particularly if you have developed your own instrument, as in a survey. Qualitative Research In qualitative research, the researcher himself/herself is considered as the instrument of the study. "Most people who have a sound background in qualitative research realize that the researcher is regarded an instrument," according to Wa- Mbaleka (2019). The research is considered as such because he/she is the one who will personally gather, analyze and interpret the data in which his/her senses are used to have a meaningful and rich description of the data. 4  The research as an instrument can be perceived in four different perspective. First, a qualitative researcher cannot employ instruments created by earlier researchers, research instruments are created by the researcher. Designing various tools and devices for data collection from the various sources is the responsibility of the qualitative researcher. For instance, the qualitative researcher must create all other means of data collection required for a study's implementation, including interview guidelines, observation procedures, manuals, or checklists. Second, our emotions affect the data and our data affect our emotions. People frequently participate in a qualitativer research study because they are curious about the problem, the issue, or the subject. They might have dealt with the issue personally or seen how it affected a loved one. Qualitative researchers are usually in a pretty emotional state since they can relate to the topic they are researching quite well. This emotional state has an impact on the data obtained and the methods used to acquire it. On the other hand, fresh information regarding a problem might have an effect on the qualitative researcher's emotional state as they delve deeper into their investigation. A qualitative research study can hardly be done properly without affecting the researcher in some way. Both the study and the researcher are impacted. Third, emotions bring out participants’ vulnerability. This appears to occur more frequently in interviews where the qualitative researcher and participant are engaged in fruitful conversation than in any other kind of data gathering. The vulnerability of both the participant and the researcher must be planned for by the qualitative researcher. The researcher may occasionally have emotional difficulties while carrying out the study, which may leave them vulnerable to others or jeopardize their ability to do the research well. Thus, when designing and conducting the qualitativer research study, special consideration must be given to the researcher's susceptibility as an instrument. Fourth, qualitative research study is not free from bias. While quantitative research encourages the eradication of subjectivity, even though this is not really attainable, qualitative research study encourages embracing subjectivity. In fact, trying to conceal the bias is common in quantitative research. On the other hand, the credibility of the study is increased by openly stating the researcher's bias. This is accomplished through the process of the researcher's reflexivity, which entails a self-critical review of the researcher's bias, assumptions, background, and competence with regard to the planned qualitative research study. Quantitative Research In quantitative research, instrument refers to a tool used to collect, measure, and analyze data related to your research problem. From the description given by Creswell (2018) that a qunatitave research involves gathering quantifiable data and analyzing these data using mathematically-based methods to answer or explain phenomenon. In quantitative research, we collect numerical data to explain phenomenon. Even though numerous data that do not typically exist in quantitative form, we can still gather them quantitatively. To do this, we develop research instrument that are expressly intended to transform phenomena that do not naturally exist in quantitative form into quantitative data that can be statistically analyzed. 5  Attitudes and beliefs are two examples of this. Data about students' perceptions of their school and teachers may be useful to gather. It is clear that these attitudes do not exist in quantitative form in nature. However, we may create a questionnaire that asks students to score a number of items as either strongly agree, agree, disagree, or disagree strongly (for example, "I think school is uninteresting"), and then assign the responses a number (e.g. 1 for disagree strongly, 4 for agree strongly). We now have quantitative information on how students feel about school. In the same way, using tools for data gathering like surveys or tests, we can gather information on a variety of occurrences and turn it into a quantitative form. As scuh, quantitative research differs from qualitative research in the following ways: ✓ The data is usually gathered using more structured research instruments. ✓ The results provide less detail on behaviour, attitudes and motivation. ✓ The results are based on larger sample sizes that are representative of the population. ✓ The research can usually be replicated or repeated, given it high reliability; ✓ The analysis of the results is more objective. 1.3 Principles of Data Collection The following are the principles of data collection: a. Keeping things simple b. Planning the whole process c. Ensuring reliability, credibility and validity d. Addressing the ethics of data collection. e. Use the 5-Right Principle ✓ Get the right data ✓ Get the data right: ✓ Get the data right away ✓ Get the data the right way ✓ Get the right data management f. Some basic questions to ask before collecting any information: ✓ What information do you intend to gather? ✓ Where will you get this information, and how will it be collected? ✓ Why is the information needed? ✓ What questions is the information going to answer? 6  ✓ Who will use the information once collected? ✓ How will the information be analysed? ✓ How will any analyses be used? 1.4 Methods of Data Collection a. Observation One of the basic method and oldest method of data gathering wherein investigator obtained it by own direct observation with/without asking from the participants. The researcher gets close enough to study subjects to observe (with/without participation) usually to understand whether people do what they say they do, and to access tacit knowledge of subjects Types of Observation 1. Structured. In this approach data are collected systematically in order to describe behaviours accurately and reliably. The observer follows written instructions from a structured observational schedule, which is developed from predetermined and defined categories prior to data collection. 2. Unstructured. This approach is best used to look at a single situation, for example, examining the experiences of elderly people admitted to care. In this situation the researcher will take numerous field notes, and may use tape or video recording. There is a predetermined schedule of events or activities. Role of the Researchers 1. Participant observation. The researcher may join the group under observation, and get to know the individuals. For example, the observer may become a co- worker while keeping his/her identity a secret. This will minimise any change in behaviour which participants may be inclined to make as a result of being observed. 2. Non-participant observer: The observer remains distant from the group and does not interact with participants unless approached. It should be considered that the presence of an observer may affect the situation being observed. Individuals may change their behaviour because they know they are participating in a study a phenomenon known as the 'Hawthorn effect'. Observational Tools 1. Field notes 2. Anecdote 3. Checklist 4. Rubric 7  Advantages ✓ Reliable and valid information can be collected ✓ Subjective bias is eliminated, if observation is done accurately ✓ Researcher get first hand data ✓ Simple Method ✓ This method is particularly suitable non-verbal respondents. ✓ The subjects behave in the desired natural manner and do not get influenced by what the observer wants to listen. ✓ Observation techniques are cost effective and produce valid results. ✓ People are observed and their willingness to participate is not taken into account as in case of focus group discussions or personal interviews. ✓ Behavior is naturally studied and data is not distorted Disadvantages ✓ An expensive method ✓ Information is very limited ✓ Unforeseen factors may interfere with the observational task. ✓ Time consuming and may involve large amount of inactivity. ✓ Observations may lack depth and qualitative richness. ✓ If the ethics are not handled well, legal action can be taken. b. Interview The interview method of collecting of data involves presentation of or a stimuli and reply in terms of oral response. Types of Interview 1. Personal Interviews. Asking question generally in a face to face to collect the information. a. Unstructured Interview. One where there are no prearranged questions and the interviewer bases questions on your responses. It’s more like a conversation where you have the chance to talk about why you want to work with them, your skills and abilities. It’s flexible, more informal and offers a unique opportunity to put forth your ideas. Advantages ✓ Due to the informal nature of unstructured interviews – it becomes extremely easy for researchers to try and develop a friendly rapport with the participants. This leads to gaining insights in extreme detail without much conscious effort. 8  ✓ The participants can clarify all their doubts about the questions and the researcher can take each opportunity to explain his/her intention for better answers. ✓ There are no questions which the researcher has to abide by and this usually increases the flexibility of the entire research process. Disadvantages ✓ As there is no structure to the interview process, researchers take time to execute these interviews. ✓ The absence of a standardized set of questions and guidelines indicates that the reliability of unstructured interviews is questionable. ✓ In many cases, the ethics involved in these interviews are considered borderline upsetting. b. Structured Interview. One where the interviewer prepares a list of common interview questions for all candidates. It helps them compare answers and judge whether you’re the right fit for the role. It’s an effective, unbiased and productive way to assess a candidate’s suitability. Advantages ✓ Structured interviews focus on the accuracy of different responses due to which extremely organized data can be collected. ✓ Different respondents have different type of answers to the same structure of questions – answers obtained can be collectively analyzed. ✓ They can be used to get in touch with a large sample of the target population. ✓ The interview procedure is made easy due to the standardization offered by structured interviews. ✓ Replication across multiple samples becomes easy due to the same structure of interview. ✓ As the scope of detail is already considered while designing the interview, better information can be obtained and the researcher can analyze the research problem in a comprehensive manner by asking accurate research questions. ✓ Since the structure of the interview is fixed, it often generates reliable results and is quick to execute. ✓ The relationship between the researcher and the respondent is not formal due to which the researcher can clearly understand the margin of error in case the respondent either degrees to be a part of the survey or is just not interested in providing the right information. Disadvantages ✓ Limited scope of assessment of obtained results. 9  ✓ The accuracy of information overpowers the detail of information. ✓ Respondents are forced to select from the provided answer options. ✓ The researcher is expected to always adhere to the list of decided questions irrespective of how interesting the conversation is turning out to be with the participants. ✓ A significant amount of time is required for a structured interview. c. Focus group interviews. One where the interviewer interviews a group of participants to collect a variety of information. These interviews can be as small as four participants and sometimes as large as ten, but I would recommend keeping a focus group interview between four and eight participants. Advantages ✓ It is an inexpensive and fast method of acquiring valuable data. ✓ Co-workers and friends are more comfortable in voicing views in each other’s company than on their own with the researcher. ✓ Participants are given a chance to reflect or react to the viewpoint of others with which they may disagree or of which they’re unaware. ✓ The dynamic discussion between participants stimulates their thoughts and reminds them of their own thoughts regarding the research subject. ✓ All individuals along with the researcher have a chance to ask questions, and these will produce more information when compared with individual interviews. ✓ Informants can build on the answers of others. ✓ The researcher can clarify clashes among participants and ask about these diverse opinions. Disadvantages ✓ The researcher has trouble controlling discussion and managing the process in comparison to individual interview. ✓ A few individuals could possibly be introverts while others take control of the debate and impact the end result, or possibly even introduce bias. ✓ The group climate can hinder or fail to energize the individual, or it can be livelier and produce more data. ✓ Recording data can present difficulties; it is actually not possible to record when so many participants are speaking at the same time. Also tape recorders may record just those who are closer. ✓ Data analysis could be time consuming and challenging task. ✓ Focus group discussions usually are not replicable. 10  d. Clinical interview. One is a conversation between a clinician and a patient that is typically intended to develop a diagnosis. It is a "conversation with a purpose" that can be structured, semi-structured, or unstructured. Emphasis is placed on open-ended questions with the focus being on the patient and not the clinician. Clinical interviews are typically used with other measures and methods to diagnose the patient. There are many different types of clinical interviews: diagnostic, termination, orientation, selection, intake, case history, and mental status exams are all examples. Advatages ✓ Very flexible, sensitive and valid. ✓ Fairly reliable and easy to analyse. ✓ Provides in-depth information. Disadvantages ✓ Difficult to replicate. ✓ Possible interviewer bias affecting response or answer interpretation. ✓ Can't guarantee honesty of participants. ✓ Cause and effect cannot be inferred. 2. Phone Interview. This is a data collection method wherein the interviewer communicates with the respondent on the phone in accordance with the prepared questionnaire or interview guide. Usually, standardised questionnaires with closed-ended questions are recommended for this kind of questioning. Consequently, telephone interview is short and focused on a collection of concentrated information. Thing to remember when using Phone Interview (Burke & Miller, 2001) 11  Advantages ✓ To find the interviewees it is enough to have their telephone numbers on hand. ✓ They are usually lower cost. ✓ The information is collected quickly. ✓ Having a personal contact can also clarify doubts, or give more details of the questions. Disadvantages: ✓ Many times researchers observe that people do not answer phone calls because it is an unknown number for the respondent ✓ Target interviewees changed their place of residence and they cannot be locate which causes a bias in the interview. ✓ Do not want to answer and resort to pretexts such as they are busy to answer, they are sick, etc. ✓ Person answered the phone call do not have the authority to answer the questions asked ✓ They are afraid of putting their security at risk. 3. Web Interview or E-Interview. This type of interview refer to in-depth interviews conducted with computer-mediated communications. an online research method conducted using computer-mediated communication (CMC), such as instant messaging, email, or video. Online interviews require different ethical considerations, sampling and rapport than practices found in traditional face-to-face (F2F) interviews. Advantages ✓ Online interviews can provide great savings in costs. ✓ Online interviews present opportunities to interview individuals and/or groups who are widely geographically distributed, without the traditional constraints of travel budgets and costs. ✓ The online interviewer has no need for the traditional tools of interviewing such as tapes, tape recorders, batteries and transcribing machines. ✓ Online interviews can provide great flexibility for the interviewer and participants. ✓ No need to arrange a venue as each individual can take part wherever is convenient ✓ More engagement in the interview process. ✓ Time, efforst and logistics efficient as you do not need to travel to the venue, prepare materials and equipments as well as exerting to much efforts. 12 Disadvantages ✓ There is the tendency for the interviewee not able to give their full attention to the interview ✓ Participation in the virtual interview requires a far higher level of motivation and interest from the interviewee than would be the case in a conventional interview. ✓ Non-verbal communication, so important in face-to-face interviews, is largely absent from online interaction. ✓ Interviewer and interviewees need to have a sufficient level of technological expertise with the technology adopted for the process ✓ The issue of access to online communities and website ✓ There is a need to have strong and continuous internet signal. ✓ Non-verbal clues which can help to contextualise the interviewee in a face- to-face scenario are lost. General Advatages of Interview ✓ Can be done face to face or over the phone ✓ The researcher can ask further questions to gain more in-depth information ✓ Interviewees can be given a sample of questions to prepare for the interview ✓ Allows researcher to collect people’s ideas, opinions, values and beliefs about a certain topic ✓ it provides only information “filtered” through the views of the interviewers. ✓ the presence of the researcher may affect how the interviewee responds. ✓ interviewee responses also may not be articulate, perceptive, or clear. ✓ Conducting interview studies can be very costly as well as very time- consuming. ✓ An interview can cause biases. ✓ Interview studies provide less anonymity, which is a big concern for many respondents. ✓ There is a lack of accessibility to respondents. General Disadvatages of Interview ✓ Can be time consuming for both researcher and interviewee ✓ Can be diffi cult to arrange a suitable place and time between researcher and interviewee ✓ Usually results in small numbers of people interviewed due to time restrictions 13  ✓ Limited amount of data collected may not accurately reflect the views of the wider population ✓ it provides only information “filtered” through the views of the interviewers. ✓ the presence of the researcher may affect how the interviewee responds. ✓ interviewee responses also may not be articulate, perceptive, or clear. ✓ Conducting interview studies can be very costly as well as very time- consuming. ✓ An interview can cause biases. ✓ Interview studies provide less anonymity, which is a big concern for many respondents. ✓ There is a lack of accessibility to respondents. c. Questionnaire The questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaire can be used to collect quantitative and/or qialutative information. Structure of Questions a. Closed-ended, or restricted-choice. Questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables. b. Open-ended, or long-form. Questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. Types of Questionnaire a. Dichotomous Questions. Belonging to the closed-ended family of questions, which only offer two possible answers, which are typically presented to survey takers in the following format – Yes or No, True or False, Agree or Disagree and Fair or Unfair. b. Multi-choice questions. A type of questionnaire/survey question that provides respondents with multiple answer options. Sometimes called objective response questions, it requires respondents to select only correct answers from the choice options. c. Rank-order questions. It asks respondents to rank target concept along some continuum such as most favorable or most to least important. d. Rating scale. It asks the respondent to judge something along an order dimension. 14  e. Visual Analog Scale. It is a measurement instrument that tries to measure a characteristic or attitude that is believed to range across a continuum of values and cannot easily be directly measured. It is often used in epidemiologic and clinical research to measure the intensity or frequency of various symptoms. f. Checklist. Checklists are used to encourage or verify that a number of specific lines of inquiry, steps, or actions are being taken, or have been taken, by a researcher. Table 3 Advatages and Disadvantages Advatages Disadvantages ✓ Inexpensive ✓ Dishonest answer ✓ Practical ✓ Skipped Questions ✓ Fast results ✓ Interpretation issues ✓ Scalibility ✓ Lack of nuance ✓ Comparability ✓ Analysis issues ✓ Easy analysis ✓ Hidden agenda ✓ Validity and Reliability ✓ Lack of personalization ✓ Standardized ✓ Uncosientious responses ✓ No pressure ✓ Accessibility issues ✓ Respndents anonymity ✓ Survey fatigue d. Physiological measurement. This refers to the measure that predominantly focus on assessing the function of major organ systems, providing information on the extent of disease or disability and the provision and/or response to therapeutic interventions. It covers the quantitative measurement and visualization of physiological structure and function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Types of Physiological Measurement a. In Vivo includes measurements of those which are performed directly within or living organisms themselves; such as blood flow determination through radiography, IVP, cholecystography. b. An In Vitro measurement by contrast is performed outside the organism’s body, such as blood glucose, blood urea, serum Na+, K+. 1.5 Characteristics of a Good Research Instrument/Tool a. The instrument/tool must be valid and reliable. b. The instrument/tool must be based upon the conceptual framework or what the research wants to find out. c. The instrument/tool must gather data suitable for and relevant to the research topic. 15  d. The instrument/tool must gather data that would test the hypothesis (quantitative) or answer the questions under investigation. e. The instrument/tool must be free from all kinds of bias. f. The instrument/tool must contain only questions or items that are unequivocal. g. The instrument/tool must contain clear and definite directions to accomplish it. h. The instrument/tool, if mechanical must be of the best quality or latest model i. The instrument/tool must be accompanied by a good cover letter. j. The instrument/tool must be accompanied, if possible by a letter of recommendation from a sponsor. Learning Activities Activity 1: You are going to conduct a study on the learning achievements of the students based on the most essential learning competencies during the pandemic. a. How will you gather the data? b. Based on your answer in “a”, construct a sample data gathering tool. Activity 2. You want to know the experiences of the teachers teaching children with special needs during the pandemic. a. How will you gather the data? b. Based on your answer in “a”, construct a sample data gathering tool. Activity 3: Construct a survey instrument on a research topic of your choice. You can also research and adopt research instruments from other researches. Checkpoint Discuss your answers to the following questions 1. What data is best suited to answer the research problem, “How do teachers describe the challenges they experiences in teaching students in the Alternative Learning System?”. Justify your answer 2. What data gathering tool should be used to answer the research problem in item 1? 3. When is questionnaire appropriate to use as data gathering tool? 4. How are data classified according to sources? What are the advantages and disadvantages of each source? 5. What are the characters of a good research data collection method? 16 References: Abdaiano, M. (2016). Research in Daily Life 1: Qualitative Research Menthod.Cronica Bookhaus. ISBN: 978-621-8019-02-7 Alameida, A. B. , Gaerlan, A.A. & Manly, N. E. (2016). Research Fundamentals from Concept to Output. A Guide for Researchers and Thesis Writers. Adriana Publishing Co., Inc. Aurora Blvd., cor Boston St., Cubao, Quezon City Manila. Canals, L. (2017). Canals, L. (2017). Instruments for gathering data. In E. Moore & M. Dooly (Eds), Qualitative approaches to research on plurilingual education (pp. 390- 401). Research-publishing.net. https://doi.org/10.14705/ rpnet.2017.emmd2016.637 Creswell, J. W. (2012). EDUCATIONAL RESEARCH Planning, Conducting and Evaluating Quantitative and Qualitative Research. (P. A. Smith, C. Robb, M. Buchholtz, J. Sabella, & K. Mason, Eds.) (4th ed.). Pearson Education, Inc., 501 Boylston Street, Coston, MA 02116. Creswell, J. W. (2016). 30 Essential Skills for the Qualitative Researcher. Copyright © 2016 by SAGE Publications, Inc. Creswell, W. & Poth, C. (2018). Qualitative Inquiry & Research Design: Choosing Among Five Approaches [Fourth edition.] 9781506330204, 1506330207 Davis, A. (1996). Instrument Development: Getting Started. Research Corner Barbara Habermann. Heiss, K. (2017) The International Encyclopedia of Communication Research Methods. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc. DOI: 10.1002/9781118901731.iecrm0062 Kabir, S.M.J. (2016). Methods of Data Collection. Basic Guidelines for Research: An Introductory Approach for All Disciplines. Book Zone Publication, ISBN: 978-984- 33-9565-8, Chittagong-4203, Bangladesh Oben, A. I. (2021). Research instruments: a questionnaire and an interview guide used to investigate the implementation of higher education objectives and the attainment of cameroon's vision 2035. European Journal of Education. DOI: 1046827/ejes.v8i7.3808. ISSN: 2501-1111 Volume 8 │ Issue 7 │ 2021 Pentang, J. T. (2023). Quantitative Research Instrumentation for Educators. Lecture Series on Research Process and Publication Pickell, D (2023). Qualitative vs. Quantitative Data: What's the Difference? Retrieved on December 22, 2023 from https://www.coursesidekick.com/marketing/2938869 Primary vs Secondary Data:15 KeyDifferences & Similarities. Retrieved on October 4, 2022 from https://www.formpl.us/blog/primary-secondary-data#:~:text=Primary %20data%20is%20very%20reliable,people%20to%20conduct%20secondary%20research. 17 Rivera, M. M. & Rivera R. V. (2007). Practical Guide to Thesis and Dissertation Writing. Katha Publishing, Inc. Quezon City, Philippines Roberts, C., & Hyatt, L. (2018). The Dissertation Journey: A Practical and Comprehensive Guide to Planning, Writing, and Defending Your Dissertation (Updated) (Third Edition (Revised Edition) ed.). Corwin. Simister, N., James, D. & Scholz, V. (2020). Quantitative and Qualitative Methods. INTRAC for civil society Surbhi, S. (2020). Difference Between Primary and Secondary Data. Retrieved on July 2, 2021 from https://keydifferences.com/author/surbhi. Turabian, K. L. (2018). Manual for Writers of Research Papers, Theses, and Dissertations,: Chicago Style for Students and Researchers (9th ed.). University of Chicago Press. Wagh, S. (2020). Public Health Research Guide: Primary & Secondary Data Definitions. Retrieved on November 5, 2021. https://researchguides.ben.edu/c.php?g=282050&p= 4036581 [6/30/2020 2:40:18 PM] What are the Sources of Data?. Retrieved on March 3, 2022 from https://byjus.com/ commerce /difference-between-primary-data-and-secondary-data/. Wilkinson, D. & Birmingham, P. (2003). Using Research Instruments. RoutledgeFalmer 11 New Fetter Lane, London EC4P 4EE ISBN 0-203-42299-6 Master e-book ISBN 18  Chapter 2: Validity and Reliability Introduction The foundation and the hallmark of a good research is the trustworthiness of the data used to make decisions. Two ideas are regarded by researchers as being of utmost relevance whether formulating a question to quantify a goal or choosing a data instrument to guarantee the results to that query are validity and reliability. The suitability of the tool or technique used to acquire the data can influence how trustworhy the data is. How can researchers assess the quality of the data? Researchers must just assume that their data are of best quality. Thus, they must ensure that the tool or technique to be used is the appropriate ones to collect the data. Learning Objectives At the end of the lesson, the students will be able to: a. Define reliability, including the different types and how they are assessed. b. Define validity, including the different types and how they are assessed. c. Describe the kinds of evidence that would be relevant to assessing the reliability and validity of a particular measure. Essential Questions Is a reliable tool or technique valid? Exploration Validity and reliability are closely related but two different concepts. These concepts are used to assess the quality of research. They demonstrate how effectively a methodology, method, or test measures something. Validity is concerned with a measure's correctness or accuracy, whereas reliability is concerned with consistency. 2.1 Validity Validity is commonly referred to as how well a method measures what it is supposed to measure. When a study's findings are highly valid, it means that they accurately reflect the genuine features, traits, and variations in the physical or social reality. For example, a teacher gives a test to measure if the students know about how on addition of two-digit numbers. The items are given as follows: (1) 14 + ___ = 23; (2) ___ + 15 = 31. In this case, the students might answer each item correctly but the data will not tell us wether the students really can add two-digit numbers. Looking at the items, if the students answer the items correctly, then we can say that they know how to subtract two-digit numbers. But the test items can be considered as an invalid test items as they do not measure what they are supposed to measure. 19  If the wrong tool or technique is employed, the results or data become meaningless or difficult to interpret. These make them useless for answering the research questions. Hence, validity should be viewed as the primary consideration in data gathering. It is essential that the conclusions and decisions we draw from the data we gathered are thoroughly supported if we consider them to be of any use at all. How then do we determine validity? There are many types of validity that we must considered in order to have a meaningful data. These are: A. Face validity (Do the assessment items appear to be appropriate?) Face validity is a subjective assessment (considered the weakest among the types of validity) of how well a construct has been operationalized. The degree to which a measure appears to be related to a certain construct in the eyes of nonexperts, such as test-takers is known as face validity. In other words, a test has face validity if the content only seems relevant to the test-taker. It assesses the instrument's visual appeal in terms of its viability, readability, uniformity of style and formatting, and the use of clear language. If the items in the instrument appear to be relevant, rational, unambiguous, and clear, that is what is meant by the term "facial validity," which refers to researchers' subjective evaluations of the presentation and relevance of the measuring instrument. It can be achieved by having an experts in the field to assess the suitability of the instrument to its intended use. The criteria are but not limited to: 1. the structure of the instrument in terms of construction and well-thought out format 2. the clarity and unambiguity of items 3. appropriateness of difficulty level for the respondents 4. correct spelling of difficult words 5. spacing of items between lines 6. adequacy of instruction on the instrument 7. reasonableness of items in relation to the perceived purpose of the instrument 8. legibility of printout 9. attractiveness of paper used 10. other criteria that satisfy face validity The dichotomous scale can be used with the categorical options "Yes" and "No," which denote a favorable and unfavorable item accordingly, to test the face validity. The face validity of the instrument is then assessed using Cohen's Kappa Index (CKI) analysis of the acquired data. The recommended acceptable CKI value is 0.60 and above. B. Content validity (Does the assessment content cover what you want to assess?) Content validity refers to the degree on how well an instrument or technique of data gathering demonstrates evidence of fairly and thorough coverage of the range 20 of objects that it claims to cover. In other words, it refers to how well the items in the test cover the actual content of topics it is intended to measure. For an instrument to be legitimate in its content, all elements that define the objective must be sufficiently examined. For example, an instrument to determine the online learning readiness of the students is said to be content-valid if it covers all the aspects or elements of online learning. Content validity requires reviewing the instrument to make sure it contains all the necessary items and omits any that are not relevant to a certain construct area. Review of the related literature are the first step in the judging approach to establishing content validity. Then reviewing the test items if they conform to the table of specification. Experts suggest that follow-up evaluations by panels or judges who are subject matter experts or specalists on how well the test items represents the content is necessary. In order to apply content validity following steps are followed: 1. An exhaustive literature reviews to extract the related items. 2. A content validity survey is generated (e.g. each item is assessed using three point scale - not necessary, useful but not essential and essential) against the table of specification. 3. The survey should sent to the experts in the same field of the research. 4. The content validity ratio (CVR) is then calculated for each item by employing Lawshe (1975) ‘s method (a linear transformation of a proportional level of agreement on how many “experts” within a panel rate an item “essential”). Lawshe CVR Formula 𝑁 𝑛𝑒 − 2 𝐶𝑉𝑅 = 𝑁 2 CVR = Lawshe’s Content Validty Ratio ne = number of experts/validators/panel rated the item “essential” N = total number of experts/validators/panel Note: a. the CVR value is dependent on the number of panelist or validators whether to accept or reject the item (Refer to the CVR Table of values). b. the lower the number of experts, the greater the CVR value needed for an item to be accepted and vice versa. c. If we want to know the content validity of the entire instrument or tool, we can calculate a CVI. The CVI is simply the mean of the CVR values for all items meeting the CVR threshold of 0.78 and retained for the final instrument. 5. Items that do not attain the CVR value are eliminated. 21  Example: Only items 2 and 4 are acceptable based on the CVR table of values Another way of determining content validity is using the modified content validity calculation. Modified Content Validty Calculation Criteria: 5 - the item is very appropriate based on the indicated topic 4 - the item is appropriate based on the indicated topic 3 - the item is appropriate but needs improvement (Please indicate the needed improvement under the remarks) 2 - the item as it is written, is not appropriate: it should be revised. (Please write the suggested revision under the remarks) 1 - the item is totally inappropriate; discard. From the data, items 2 and 4 are to be rejected. 22 C. Criterion-related validity (How well does the test measure what you want it to?) Criterion validity refers to the correlation between a test and a criterion that is already accepted as a valid measure of the goal or question. If a test is highly correlated with another valid criterion, it is more likely that the test is also valid. In other words, criterion validity (or criterion-related validity) measures how well one measure predicts an outcome for another measure. A test has this type of validity if it is useful for predicting performance or behavior in another situation (past, present, or future). Example: A teacher applicant takes a performance test during the interview process. If this test accurately predicts how well the teacher will perform on the job, the test is said to have criterion validity. PNU student take a qualifying exam for Social Science Major. If the qualifying exam can predict how will a student will perform as a Social Science major then the qualifying exam has criterion validity. The first measure (in the above examples, the job performance test and the GRE) is sometimes called the predictor variable or the estimator. The second measure is called the criterion variable as long as the measure is known to be a valid tool for predicting outcomes. D. Construct validity (Are you measuring what you think you're measuring?) Construct validity is the extent to which the survey measures the theoretical construct it is intended to measure. Construct validity refers to how well we translated or transformed a concept, idea, or behaviour that is a construct into a functioning and operating reality, the operationalization. Construct validity test whether a variable's operational definition accurately reflects its conceptual meanings. In other words, construct validity shows the degree to which inferences are legitimately made from the operationalisations in one’s study to the theoretical constructs on which those operationalisations are based. Construct validity “is at the heart of any study in which researchers use a measure as an index of a variable that is itself not directly observable”. Constructs are higher level concepts which are not directly observable or measurable (nature) while 23  variables (sometimes used interchangeably with indicators or measures) seek to measure the underlying construct (nature exposed to our method of reasoning). For example, hard work can be seen as a construct (not directly measurable), while number of hours spent working on a research paper can be seen as a way of measuring hard work. There can be more than one measure or indicator for the same construct. Thus, when we expose nature to our method of questioning, we come up with operational definitions and measures for our constructs. This process is generally understood as the process of operationalization. As such, we ensures the interpretability of results, thereby paving the way for effective and efficient data- based decision making. Confirmatory factor analysis (CFA) is a technique used to assess construct validity. With CFA, we state how we believe the questionnaire items are correlated by specifying a theoretical model. Our theoretical model may be based on an earlier exploratory factor analysis (EFA), on previous research or from our own a priori theory. We calculate the statistical likelihood that the data from the questionnaire items fit with this model, thus confirming our theory (the computation is not anymore part of this module). 2.2 Reliability Reliability is concerned on asking whether the test used to collect data produces consistent results. Repeatability is another aspect of reliability. For instance, a scale or test is considered dependable if it consistently produces the same result when repeated measurements are conducted under the same condition. Another example is a tape measure, if it measures inches differently each time it was used, then it is ot reliable. Likewise, instruments such as classroom tests and national standardized exams should be reliable – it should not make any difference whether a student takes the assessment in the morning or afternoon; one day or the next. Research findings are dependable if they are consistently reproduced. The degree of reliability can be evaluated using a correlation coefficient. A test should display a strong positive correlation if it is reliable. Of course, it is unlikely the exact same results will be obtained each time as participants and situations vary, but a strong positive correlation between the results of the same test indicates reliability. To evaluate reliability, we look for: ✓ Consistency across time—would the results have been the same if the test or assessment had taken place on another day, or at another time? ✓ Consistency across tasks—would the result have been the same if other tasks had been chosen to assess the learning? ✓ Consistency across markers—would the results have been similar if another marker had scored the assessment? These types of consistency are also known as—test-retest, internal, and inter- rater reliability. Typically, appraising these forms of reliability involves taking multiple measures of the same person, object, or construct and assessing scatterplots and 24 correlations of the measurements. Reliable measurements have high correlations because the scores are similar. A. Test-Retest Reliability (Do you get the same results when you repeat the measurement?) Test-retest reliability is a measure of reliability obtained by administering the same test twice over a period of time to a group of individuals. It is also called as stability reliability. The scores from Time 1 and Time 2 can then be correlated in order to evaluate the test for stability over time. For instance, a test designed to assess student achievement in mechanics could be given to a group of students twice, with the second administration perhaps coming a week after the first. The obtained correlation coefficient would indicate the stability of the scores. A positive correlation coefficient, r of 0.6 denotes relatively strong relationship. In as much as this type of reliability establishes the degree to which a test can produce stable, consistent scores across time. However, in practice, measurement instruments are never entirely consistent. We need to be cautious in using this reliability test as some characteristics would not be consistent across time. A good example is your mood, which can change from moment to moment. A test-retest assessment of mood is not likely to produce a high correlation even though it might be a useful measurement instrument. Thus, in this type of reliability time is of essence and the interval of the administration of the test should of short perion of time. B. Internal Consistency (Do you get the same results from different parts of a test that are designed to measure the same thing?) Internal consistency is the consistency of the measurement itself. It is also reffered to as the split-half method. It assesses the internal consistency of a test, such as psychometric tests and questionnaires. There, it measures the extent to which all parts of the test contribute equally to what is being measured. This is done by comparing the results of one half of a test with the results from the other half. A test can be split in half in several ways, e.g. first half and second half, or by odd and even numbers. If the two halves of the test provide similar results this would suggest that the test has internal reliability. This type of reliability assesses consistency across items within a single instrument. Researchers evaluate internal reliability when they’re using instruments such as a survey or personality inventories. In these instruments, multiple items relate to a single construct. Questions that measure the same characteristic should have a high correlation. People who indicate they are risk-takers should also note that they participate in dangerous activities. If items that supposedly measure the 25  same underlying construct have a low correlation, they are not consistent with each other and might not measure the same thing. The reliability of a test could be improved through using this method by removing the item or items that have low correlation coefficient value. Internal consistency can be measured using Cronbach’s alpha. The Cronbach’s alpha will help determine whether a collection of items consistently measures the same characteristic. Cronbach’s alpha gives us a simple way to measure whether or not a score is reliable. It is used under the assumption that you have multiple items measuring the same underlying construct. Example, the Happiness Survey (https://www.questionpro.com/survey-templates/happiness-survey-template/), we might have five questions all asking different things, but when combined, could be said to measure overall happiness. The general rule of thumb is that a Cronbach’s alpha of 0.70 and above is good, 0.80 and above is better, and 0.90 and above is best. This is a quick and easy way to establish reliability. However, it can only be effective with large questionnaires in which all questions measure the same construct. C. Inter-rater Consistency (do you get the same results when different people conduct the same measurement?) Inter-rater Consistency is the consistency of a measure across raters or observers. It is a measure of reliability used to assess the degree to which different judges or raters agree in their assessment decisions. Inter-rater reliability is useful because human observers will not necessarily interpret answers the same way; raters may disagree as to how well certain responses or material demonstrate knowledge of the construct or skill being assessed. So, inter-rater reliability assesses consistency across different observers, judges, or evaluators. When various observers produce similar measurements for the same item or person, their scores are highly correlated. Inter-rater reliability is essential when the subjectivity or skill of the evaluator plays a role. For example, assessing the quality of a writing sample involves subjectivity. Researchers can employ rating guidelines to reduce subjectivity. Comparing the scores from different evaluators for the same writing sample helps establish the measure’s reliability. 2.3 Reliability vs Validity It’s important to consider reliability and validity when you are creating your research design, planning your methods, and writing up your results, especially in quantitative research. 26  Table 3 Reliability vs Validity Basis of Comparison Reliability Validity What does it tell you? The extent to which the The extent to which the results can be reproduced results really measure what when the research is repeated they are supposed to under the same conditions. measure. How is it assessed? By checking the consistency By checking how well the of results across time, across results correspond to different observers, and established theories and other across parts of the test itself. measures of the same concept. How do they relate? A reliable measurement is not A valid measurement is always valid: the results generally reliable: if a test might be reproducible, but produces accurate results, they’re not necessarily they should be reproducible. correct. Learning Activities Activity 1: Refer to activity 3 in chapter 1. a. Ask 5 experts to validated the instrument. b. Determine the CVR and CVI of the instrument. Activity 2. Refer to the validated instrument in Activity 1. Determine the reliability of the instrument through: 1. Stability reliability 2. Internal Consistency Checkpoint Discuss your answers to the following questions 1. A parent called you to ask about the reliability coefficient on a recent standardized test. The coefficient was reported as.89, and the parent thinks that must be a very low number. How would you explain to the parent that.89 is an acceptable coefficient? 2. Your school district is looking for an assessment instrument to measure reading ability. They have narrowed the selection to two possibilities -- Test A provides data indicating that it has high validity, but there is no information about its 27  reliability. Test B provides data indicating that it has high reliability, but there is no information about its validity. Which test would you recommend? Why? References Abdaiano, M. (2016). Research in Daily Life 1: Qualitative Research Menthod.Cronica Bookhaus. ISBN: 978-621-8019-02-7 Creswell, J. W. (2012). EDUCATIONAL RESEARCH Planning, Conducting and Evaluating Quantitative and Qualitative Research. (P. A. Smith, C. Robb, M. Buchholtz, J. Sabella, & K. Mason, Eds.) (4th ed.). Pearson Education, Inc., 501 Boylston Street, Coston, MA 02116. Creswell, J. W. (2016). 30 Essential Skills for the Qualitative Researcher. Copyright © 2016 by SAGE Publications, Inc. Creswell, W. & Poth, C. (2018). Qualitative Inquiry & Research Design: Choosing Among Five Approaches [Fourth edition.] 9781506330204, 1506330207 Eldridge, J. (2020). Chapter 12: Data Analysis. In Boswell C. & Cannon, S. (5th Ed.). Introduction to Nursing Research: Incorporating Evidence-Based Practice, Fifth Edition. ©2020 ISBN: 9780000149794 Mohajan, H. (2017). Two Criteria for Good Measurements in Research: Validity and Reliability. Annals of Spiru Haret University, 17(3): 58-82 Oluwatayo, J. A. (2012). Validity and Reliability Issues in Educational Research. Journal of Educational and Social Research Vol. 2 (2) May 2012. ISSN 2240‐0524. DOI:10.5901/jesr.2012.v2n2.391 Rivera, M. M. & Rivera R. V. (2007). Practical Guide to Thesis and Dissertation Writing. Katha Publishing, Inc. Quezon City, Philippines Rozali, M. Z., Puteh, S. Nur Yunus, F. A., Hamdan, N. H., Latif, H.F.M. (2020). Reliability and Validity of Instrument on Academic Enhancement Support for Student-Athlete Using Rasch Measurement Model. Asian Journal of University Education (AJUE) Volume 18, Number 1, January 2022. https://doi.org/10.24191/ajue.v18i1.17199 Sürücü, L. & Maslakçı, A., Validity And Reliability In Quantitative Research, BMIJ, (2020), 8(3): 2694-2726, doi: http://dx.doi.org/10.15295/bmij.v8i3.1540 Taherdoost, Hamed, Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research (August 10, 2016). Available at SSRN: https://ssrn.com/abstract=3205040 or http://dx.doi.org/10.2139/ssrn.3205040 Tavakol, M. & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education. 2011 Jun 27. doi: 10.5116/ijme.4dfb.8dfd 28  Chapter 3: Sampling Technique Introduction In many study fields, statistical estimates from a sample are used to draw conclusions – known as population parameters – about an interest population. When designing a study, researchers must specify the target group from which they hope to draw these conclusions. The challenge of accurately selecting a target group is frequently difficult, but it is crucial for determining how the research findings will ultimately be used. With the challenges of including all members of the population, it is of utmost importance to choose a sample or sub-group of the population that is most likely to be representative of the target population we are interested in. This is crucial because we want to extrapolate from the sample to the intended audience. The researcher can be more certain that the results can be applied to the target population if the sample is more representative. Learning Objectives At the end of the lesson, the students will be able to: a. Differentiate random sampling and purposive sampling; b. Determine the types of sampling techniques; c. Calculate the sample size to be used in quantitative research; Essential Questions Why do we use samples for our research? Exploration 3.1 Basic Concepts When you conduct research about a group of people, it’s rarely possible to collect datafrom every person in that group. Instead, you select a sample. Population is the group of individuals restricted to a geographical region (neighborhood, city, state, country, continent etc.), or certain institutions (hospitals, schools, health centers etc.), that is, a set of individuals that have at least one characteristic in common. Target population corresponds to a portion of the previously mentioned population, about which one intends to draw conclusions, that is to say, it is a part of the population whose characteristics are an object of interest of the investigator. Study population is that which will actually be part of the study, which will be evaluated and will allow conclusions to be drawn about the target population, as long as it is representative of the latter. 29  Population Target Population Study Population Figure 1. Graphical presentation of the difference of the population, the tarhet population and the study population Sample is the small portion of the study population selected to participate in the study Sampling is the process used for selecting a sample or individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population. Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population. Sample size refers to the number of participants or observations included in a study. 3.2 Importance of Sampling Everyone who has ever worked on a research project is aware that resources are finite; there is never an endless supply of time, money, or people. Because of this, the 30 majority of research studies focus on gathering information from a sample of people rather than the full population (the census being one of the few exceptions). It makes sense to utilize sampling techniques in research studies of various shapes and sizes, even if the concept of sampling is easier to comprehend when you consider a very big population. After all, why wouldn't you perform a study if you could make it easier and cheaper to do so? Additionally, sampling greatly expands research opportunities because it enables you to study larger target populations with the same resources as you would for smaller ones. Sampling allows researchers to: Save Time. Random sampling is much faster than surveying everyone in a population, and obtaining a non-random sample is almost always faster than random sampling. Thus, sampling saves researchers lots of time. Save Money. The number of people a researcher contacts is directly related to the cost of a study. Sampling saves money by allowing researchers to gather the same answers from a sample that they would receive from the population. Collect Richer Data. Sometimes, the goal of research is to collect a little bit of data from a lot of people (e.g., an opinion poll). At other times, the goal is to collect a lot of information from just a few people (e.g., a user study or ethnographic interview). Either way, sampling allows researchers to ask participants more questions and to gather richer data than does contacting everyone in a population. Scope of sampling is high. The investigator is concerned with the generalization of data. To study a whole population in order to arrive at generalizations would be impractical. Some populations are so large that their characteristics could not be measured. Before the measurement has been completed, the population would have changed. But the process of sampling makes it possible to arrive at generalizations by studying the variables within a relatively small proportion of the population. Accuracy of data is high. Having drawn a sample and computed the desired descriptive statistics, it is possible to determine the stability of the obtained sample value. A sample represents the population from which its is drawn. It permits a high degree of accuracy due to a limited area of operations. Moreover, careful execution of field work is possible. Ultimately, the results of sampling studies turn out to be sufficiently accurate. Organization of convenience. Organizational problems involved in sampling are very few. Since sample is of a small size, vast facilities are not required. Sampling is therefore economical in respect of resources. Study of samples involves less space and equipment. Intensive and exhaustive data. In sample studies, measurements or observations are made of a limited number. So, intensive and exhaustive data are collected. Suitable in limited resources. The resources available within an organization may be limited. Studying the entire universe is not viable. The population can be 31  satisfactorily covered through sampling. Where limited resources exist, use of sampling is an appropriate strategy while conducting marketing research. Better rapport with the respondents. An effective research study requires a good rapport between the researcher and the respondents. When the population of the study is large, the problem of rapport arises. But manageable samples permit the researcher to establish adequate rapport with the respondents. 3.3 Types of Sampling Although other classification systems exist, sampling strategies are often categorized as probability sampling and non-probability sampling. There are two major types of sampling methods – probability and non-probability sampling. Probability sampling, also known as random sampling, is a kind of sample selection where randomization is used instead of deliberate choice. Non-probability sampling techniques are where the researcher deliberately picks items or individuals for the sample based on their research goals or knowledge. 3.4 Probability Sampling Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice. A. Simple Random Sampling. (lottery sampling). Each item in the population has an equal and likely possibility of getting selected in the sample. When simple random sampling is used, all elements have an equal probability of being selected. Because this sampling method gives equal probability to all elements, it is useful when researchers are interested in associations that would apply to the whole population. Example, a teachers puts students' names in a hat and chooses without looking to get a sample of students. Steps to select sample using a simple random sampling. Step 1: Define the population Start by deciding on the population that you want to study. It’s important to ensure that you have access to every individual member of the population, so that you can collect data from all those who are selected for the sample. Step 2: Decide on the sample size. 32  Step 3: Compute the sample size. Step 4: Randomly select your sample This can be done in one of two ways: the lottery or random number method. In the lottery method, you choose the sample at random by “drawing from a hat” or by using a computer program that will simulate the same action. In the random number method, you assign every individual a number. By using a random number generator or random number tables, you then randomly pick a subset of the population. Step 4: Collect data from your sample. To ensure the validity of your findings, you need to make sure every individual selected actually participates in your study. B. Systematic sampling. Element selection in systematic random sampling is based on a predetermined interval. In its simplest form, every element is listed, starting with a random number, and every kth element after that is chosen. By dividing the desired sample size by the population size, the sampling interval, k, is derived.Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. Since, this type of sampling method has a predefined range, hence it is the least time-consuming. Example, all students of the school are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people. Steps to select sample using systematic sampling. Step 1: Define the population Start by deciding on the population that you want to study. In systematic sampling, you have two choices for data collection: (a) You can select your sample ahead of time from a list and then approach the selected subjects to collect 33  data, or (b) You can approach every kth member of your target population to ask them to participate in your study. Step 2: Decide on your sample size and sampling interval Compute the sample size. When you know your target sample size, you can calculate your interval, k, by dividing your total estimated population size by your sample size.