RM summarize
Difficulties in developing an attitudinal scale:
Determining which aspects of a situation or issue to include when measuring an attitude can be challenging. For example, when measuring public opinion on healthcare, should aspects like accessibility, affordability, or quality of care be considered? Adopting a procedure for combining different aspects to obtain an overall picture or indicator requires careful consideration. For instance, in political polling, various questions about candidates' policies, leadership qualities, and personal attributes may need to be combined to gauge overall approval ratings. Ensuring that a scale accurately measures what it's supposed to measure involves rigorous validation processes. For example, in educational research, a scale designed to measure student motivation should undergo extensive testing to confirm its reliability and validity.
Methods of data collection in qualitative research:
Unstructured Interviews: In-depth Interviews: One-on-one conversations aimed at understanding individuals' perspectives in detail. Focus Group Interviews: Discussions conducted with a group to explore shared experiences and perspectives. Observations: Systematic watching and listening to understand behaviors, interactions, and phenomena in natural settings. Secondary Sources: Government or quasi-government publications. Earlier research. Personal records or diaries. Mass media reports published in newspapers, magazines, etc. These methods help researchers gather qualitative data by directly engaging with participants or analyzing existing materials.
Choosing Between an Interview Schedule and a Questionnaire:
Nature of the Investigation: The choice between an interview schedule and a questionnaire depends on the nature of the information being collected. For sensitive topics like drug use, researchers may opt for interviews to ensure confidentiality and encourage honest responses. Geographical Distribution of the Study Population: Considerations such as cost and logistics may influence the choice between interviews and questionnaires. For example, conducting interviews may be more challenging and costly for studies covering a wide geographic area. Type of Study Population: Characteristics of the study population, such as age, literacy levels, and disabilities, also influence the selection of data collection methods. For instance, interviews might be more suitable for populations with low literacy rates or individuals with disabilities who may require assistance in completing questionnaires. These details outline various methods of administering questionnaires and factors to consider when choosing between interviews and questionnaires for data collection.
A research design should do:
Name the study design: This means describing what type of study it is, like cross-sectional (looking at a group at one point in time), before-and-after (observing changes before and after an intervention), comparative (comparing different groups), or controlled experiment (testing with and without a treatment). Provide detailed information on: Study population: Who the study is targeting, like adults in a certain city or students in a specific school. Sample: The specific individuals or groups chosen from the population to participate in the study. How to contact the sample: Methods for reaching out to and recruiting participants, such as through emails, phone calls, or in-person visits. Seeking consent: How researchers will ask participants for permission to take part in the study, ensuring ethical standards are met. Data collection method: This includes explaining how information will be gathered, like through surveys, interviews, or observations, and justifying why that method is chosen (e.g., surveys for large-scale data collection). Return of questionnaires: Where participants should send back any forms or questionnaires they've completed. Location for interviews: Where face-to-face interviews will be conducted, ensuring privacy and comfort. Ethical issues: Addressing any potential ethical concerns in the research, such as ensuring participant confidentiality and avoiding harm.
Study design based on the nature of investigation:
"Study designs based on the nature of the investigation" means how researchers set up their studies depending on what they want to find out. In quantitative research, there are three main types: Experimental Studies: Researchers create controlled situations to test ideas and find out cause-and-effect relationships. Examples: Studying how a new medication affects patients by comparing those who take it to those who don't (control group). Non-experimental Studies: Researchers observe and describe things without changing any variables or setting up controlled situations. Examples: Surveys asking people about their habits or behaviors without intervening or manipulating anything. Quasi- or Semi-experimental Studies: These studies are a mix of experimental and non-experimental approaches. Researchers might change some things but can't fully control all factors. Examples: Studying the effects of a program in a community by comparing data before and after its implementation but without a control group for comparison. So, researchers pick the type of study design based on whether they want to control variables (experimental), just observe (non-experimental), or blend both approaches (quasi-experimental).
Study designs based on the reference period:
"Study designs based on the reference period" refers to how researchers choose to look at time when conducting their studies. There are three main types: Retrospective Studies: These look at things that have already happened in the past. Researchers use available data or ask people to remember past events. Example: Studying historical records to understand the effects of a particular medication on a population's health. Prospective Studies: These try to predict what will happen in the future. Researchers observe a group of people over time to see how certain factors affect outcomes. Example: Following a group of children from birth to adulthood to study the long-term effects of diet on health. Retrospective-Prospective Studies: These combine elements of both retrospective and prospective approaches. They might analyze past data while also collecting new data for future analysis. Example: Examining medical records of patients who received a certain treatment in the past, while also following new patients who are receiving the same treatment to compare outcomes.
Measurement of attitudes in quantitative and qualitative research:
-Social research often explores people's attitudes towards different conditions, issues, or problems. -Attitudinal scales, like the Likert scale or Thurstone scale, quantify these attitudes, measuring their intensity in quantitative research but not in qualitative.
A research design:
A research design is like a roadmap that helps researchers plan out their study. It lays out how they'll conduct their research from start to finish. This includes deciding how they'll measure things they're studying, picking who or what they'll study, gathering data, and analyzing the results.
Attitudinal Scales in Quantitative Research:
Attitudinal Scales in Quantitative Research: Three common scales used to measure attitudes in quantitative research include : the Likert Scale: Respondents indicate their level of agreement or disagreement with a series of statements, such as "strongly agree" to "strongly disagree. "Thurstone Scale: Items are ranked based on their perceived importance, with respondents selecting items that best represent their attitude. Guttman Scale: Items are arranged in a cumulative order of difficulty, with agreement indicating agreement with all previous items.
Functions of attitudinal scales:
Attitudinal scales serve two main functions: Measure the intensity of respondents' attitudes toward different aspects of a situation or issue. For example, a Likert scale might ask respondents to rate their agreement with statements like "I strongly agree," "I agree," "Neutral," "I disagree," or "I strongly disagree. "Provide techniques to combine attitudes towards various aspects into one overall indicator. For instance, combining responses from multiple Likert scale items related to job satisfaction to create an overall measure of job satisfaction. These scales are essential tools for understanding how individuals feel about different situations or issues in research studies.
In research, there are six characteristics. They are:
Controlled, rigorous, systematic, valid and verifiable, empirical and critical.
The definition of all of those characteristics:
Controlled: Research should involve controlling variables to isolate the effects of interest. For example, in a study investigating the effects of a new drug on blood pressure, researchers may control factors such as diet and exercise to ensure that any changes observed are due to the drug alone. Rigorous: Research should be conducted with a high degree of thoroughness and precision. This involves meticulous attention to detail and adherence to established methodologies. For instance, a rigorous study on climate change might involve extensive data collection, rigorous statistical analysis, and peer review. Rigorous research means being very careful and thorough at every step to make sure the results are accurate and reliable. For example, in a science experiment testing a new medicine, rigorous research would mean following strict procedures, double-checking measurements, and making sure the methods used are trusted by other scientists. Systematic: Research should follow a well-organized and structured approach. This includes clear methodologies, procedures, and protocols. An example would be a systematic literature review where researchers follow a predetermined protocol to identify, analyze, and synthesize relevant studies on a particular topic. Systematic research involves carefully planned steps and procedures to ensure consistency and reliability in gathering and analyzing data. Example: When conducting a survey to study customer satisfaction, systematic research would entail creating a detailed plan for selecting participants, designing the survey questions, collecting responses in an organized manner, and analyzing the data methodically to draw meaningful conclusions. Valid (legally or logical) and verifiable: Research findings should be valid, meaning they accurately represent the phenomena being studied, and v
Formulating a research problem in Qualitative Research:
Formulating a Research Problem in Qualitative Research: Adjusting the Research Problem: In qualitative research, the research problem and how you collect data can change. This might happen to understand the whole picture of something or to focus more closely on specific parts. Example: If you're studying "student motivation," you might initially plan to interview teachers. But then you realize you also need to talk to students to understand the whole picture, so you adjust your approach. Developing a Conceptual Framework: Creating a Structure for Discussion: It's helpful to have a framework, or a sort of outline, to guide your discussions with the people you're studying. Example: If you're studying "community resilience," you might develop a framework that includes factors like social support, economic resources, and community cohesion. This helps you structure your conversations with community members and ensures you cover all relevant topics.
Types of variables:
From the Viewpoint of Causal Relationship: Independent Variable (the cause): This is the variable that the researcher manipulates or changes. It's called independent because it's presumed to cause changes in the dependent variable. Example: In a study on the effect of study time on exam scores, study time is the independent variable because researchers can manipulate it by assigning different amounts of study time to participants. Dependent Variable (the outcome or changes): This is the variable that the researcher measures to see if there are any changes due to the independent variable. Example: In the same study, exam scores are the dependent variable because they're expected to change depending on the amount of study time. Extraneous Variables (other factors in a real-life situation): These are variables that might influence the relationship between the independent and dependent variables, but aren't the focus of the study. Example: Factors like participant's motivation or prior knowledge could affect exam scores in our study. Intervening Variable (without it, the cause does not have assumed effect): This variable comes between the independent and dependent variables, affecting the relationship between them. Example: In a study on the effect of teaching methods on student performance, student engagement might be an intervening variable because it mediates the relationship between teaching methods and performance.
Types of variables:
From the Viewpoint of Study Design: Active Variables (can be manipulated, changed, or controlled): These are variables that researchers can deliberately change or control during the study. Example: In a clinical trial testing a new drug, the dosage of the drug administered to participants can be actively controlled by researchers. Attribute Variables (cannot be manipulated, changed, or controlled, and reflect characteristics of the study population): These are variables that describe characteristics of the study participants but cannot be directly manipulated by the researcher. Example: Age, gender, ethnicity, and socioeconomic status are attribute variables that describe characteristics of individuals in a study but cannot be changed by the researcher. Types of Variable Based on Measurement Unit: Categorical or Continuous: Categorical variables have distinct categories or groups, like colors or types of cars. Continuous variables can have any value within a range, like height or weight. Example: Categorical - Types of pets (dog, cat, bird); Continuous - Height of individuals (measured in inches). Qualitative or Quantitative: Qualitative variables describe characteristics or qualities, like gender or favorite color. Quantitative variables represent quantities or amounts, like age or income. Example: Qualitative - Gender (male, female); Quantitative - Age (measured in years).
When choosing a research problem, consider these factors:
Interest: Pick a topic you're genuinely interested in, as research takes time and effort. Example: If you're passionate about environmental conservation, you might explore the impact of plastic pollution on marine life. Magnitude: Focus on a manageable and specific topic given your time and resources. Example: Instead of studying all aspects of climate change, you could narrow it down to the effects of deforestation on local ecosystems. Measurement of Concepts: Be clear about how you'll measure key concepts in your research. Example: If you're studying the effectiveness of a new teaching method, define what "effectiveness" means and how you'll measure it, such as through test scores or student feedback. Level of Expertise: Choose a topic where you have some expertise or are willing to learn. Example: If you're interested in mental health, but lack expertise, you could start by researching common coping mechanisms for stress and anxiety, learning along the way. Relevance: Make sure your study contributes to existing knowledge, fills gaps in understanding, or informs policy decisions. Example: Researching the impact of renewable energy policies on reducing carbon emissions can contribute to environmental policy development. Availability of Data: Ensure that the data you need for your research is accessible and in the format you require. Example: If you're studying historical trends in economic growth, ensure that relevant statistical data is available from reliable sources such as government databases or academic journals.
Ways of Administering a Questionnaire:
Mailed Questionnaire with Covering Letter: Researchers can send out questionnaires via mail along with a covering letter explaining the purpose of the study and encouraging participation. This method is suitable for reaching a wide geographic area and can be cost-effective. Collective Administration: This method involves administering questionnaires to a group of people assembled in one place, such as a classroom or public function. It provides an opportunity to reach a captive audience and gather responses efficiently. Online Questionnaire: Questionnaires can be distributed electronically via email, websites, or social media platforms. This method offers convenience for both researchers and respondents, allowing for widespread distribution and potentially higher response rates. Administration in Public Places: Researchers can approach individuals in public places like shopping centers or schools to administer questionnaires. This method provides direct access to diverse populations but may require careful consideration of privacy and ethical concerns.
Methods for exploring attitudes in qualitative research:
Methods for Exploring Attitudes in Research: Quantitative: Questionnaire: Participants answer predetermined questions independently. Example: Political views survey with respondents rating their agreement. Interview Schedule: The interviewer records respondent answers for consistency. Example: In market research, the interviewer asks shoppers about product preferences. Qualitative: Interview Guide: In-depth discussions to explore attitudes, like focus group interviews or one-on-one sessions. Example: Focus group explores perceptions of a brand's reputation. Example: In-depth interviews reveal employees' feelings about workplace culture.
Sources of Research Problems:
Most research revolves around the 4 Ps: people, problems, programs, and phenomena; actually around 2 combined or more. The 4 Ps: These are common sources of research problems: People: Studying individuals or groups. Problems: Investigating issues or challenges. Programs: Evaluating interventions or initiatives. Phenomena: Exploring observable occurrences or events. Combining 2 or More Ps: Research often involves combining two or more of these elements. For example, studying how a particular program impacts people. Applicability to Quantitative and Qualitative Research: The 4 Ps concept can be applied to both quantitative (numbers-focused) and qualitative (words-focused) research. Difference in Specificity, Dissection, Precision, and Focus: The main distinction between quantitative and qualitative approaches lies in the level of specificity, dissection (breaking down the phenomenon), precision (exactness), and focus (depth of exploration). Approach in Qualitative Research: In qualitative research, the attributes mentioned above (specificity, dissection, precision, and focus) are deliberately kept loose. This flexibility allows researchers to explore more freely, without rigid constraints, enabling them to discover unexpected insights as they delve deeper into the research topic.
Types of measurement scales:
Nominal or Classificatory Scale: This scale categorizes data into distinct groups with no inherent order or ranking. Example: Colors of cars (red, blue, green) - there's no inherent order among these categories. Ordinal or Ranking Scale: This scale orders or ranks data, but the intervals between values are not equal. Example: Rating satisfaction levels (low, medium, high) - there's order, but the difference between "low" and "medium" may not be the same as between "medium" and "high." Interval Scale: This scale has ordered categories with equal intervals between them, but there's no true zero point. Example: Temperature measured in Celsius or Fahrenheit - there's no absolute zero temperature. Ratio Scale: This scale has ordered categories with equal intervals between them and a true zero point. Example: Height, weight, or income - zero represents an absence of the measured quantity.
Collecting Data Using Primary Sources:
Observation: Observation is a method where researchers purposefully and systematically watch and listen to interactions or phenomena. It's often used in social sciences to gather data about human behavior in natural settings. Participant Observation: In this approach, the researcher actively involves themselves in the group being studied, blending in with the members. They may or may not reveal their identity as a researcher. This method allows for a deep understanding of the group's dynamics and culture. Non-participant Observation: Unlike participant observation, non-participant observation involves the researcher remaining an outsider, observing the group without interfering in its activities. This method is more objective but may lack the depth of insight gained from participating directly. Challenges: Hawthorne Effect: This refers to the phenomenon where individuals modify their behavior when they know they are being observed. It can lead to skewed results if not accounted for. Observer Bias: Researchers may unintentionally interpret observations based on their own biases or preconceptions. Missing Interaction: Despite meticulous observation, some interactions or behaviors may still go unnoticed or unrecorded.
Types of Questions:
Open-ended Questions: These are questions that allow respondents to provide detailed, unrestricted answers in their own words. They encourage thoughtful responses and provide insights into respondents' perspectives and experiences. Closed Questions: Closed questions offer respondents a limited set of predefined answer options, such as "yes" or "no," multiple-choice options, or rating scales. They are useful for obtaining specific, quantifiable data and are often easier to analyze.
Other designs that commonly used in quantitative research:
Other Designs Commonly Used in Quantitative Research: A. The Cross-Over Comparative Experimental Design: This design switches who gets the new treatment and who doesn't during the study. It helps see if the new treatment really works by comparing the same people before and after they get it. Example: Students in one class try a new studying method for a month, then switch back to their old way for another month. The researchers see if their grades change when they switch methods. B. The Replicated Cross-Sectional Design: This design looks at how things change at different stages of a program. It compares different groups of people before, during, and after a program to see if it's working. Example: Researchers study how exercise affects people's health by comparing groups of people who start exercising at different times. They see if there's a difference in health between the groups at different stages of starting the exercise routine. These designs offer researchers different ways to study and evaluate interventions or programs, helping to understand their effectiveness and impact.
Differences in the methods of data collection:
Philosophical Ideas: Quantitative Research: This method is grounded in the belief that there is one objective truth that can be measured and analyzed using numerical data. For example, conducting a survey to measure the satisfaction level of customers with a product on a scale of 1 to 5. Qualitative Research: This method is based on the belief that reality is subjective and multiple perspectives exist. An example would be conducting in-depth interviews to understand the lived experiences of individuals dealing with a particular health condition. How Data is Gathered: Structured Data Collection (Quantitative): Using standardized questionnaires with fixed response options, like multiple-choice questions or Likert scales. Flexible Data Collection (Qualitative): Conducting open-ended interviews or participant observations where the researcher has the freedom to explore different aspects and follow-up on unexpected insights. Researcher's Freedom: High Control (Quantitative): Researchers have strict protocols to follow, ensuring consistency in data collection. For instance, controlling variables in an experiment to test the effect of a drug on blood pressure. More Flexibility (Qualitative): Researchers can adapt their approach based on emerging insights during data collection. For example, adjusting interview questions based on initial responses to delve deeper into certain topics. These examples illustrate how the classification of research methods (quantitative, qualitative) is determined by philosophical underpinnings, data collection structure, and the freedom given to researchers.
Five steps in conducting a literature review:
Procedures for reviewing the literature There are five steps involved in conducting a literature review: Search for existing literature: You'd begin by searching databases, libraries, and academic journals for any studies, articles, or books that talk about social media and mental health in teenagers. Review the literature selected: Next, you'd carefully read and analyze each piece of literature you found. You'd take notes on what each study says about the topic, what methods they used, and what their findings were. Develop a theoretical framework: Based on what you've learned from the literature, you might start to develop theories or ideas about how social media could affect teenagers' mental health. This helps guide your own research. Develop a conceptual framework: You'll also create a structure or framework for how you'll approach your research based on what you've learned from the literature. This could involve deciding what variables you'll study and how you'll measure them. Writing up the literature reviewed: Finally, you'd write up your literature review, summarizing what you found in the existing literature. You'd discuss common themes, controversies, and gaps in knowledge that your research will address.
Collecting data using primary sources
Recording Observations: Narrative Recordings: Researchers describe observations in detail using their own words. This method allows for rich descriptions and contextual understanding. Categorical Recording: Observations are categorized into predefined types or classifications. For example, behaviors may be categorized as positive/negative, verbal/non-verbal, etc. This approach facilitates systematic analysis and comparison of observations. Video Cameras or Electronic Devices: Utilizing technology such as video cameras or audio recorders to capture observations. This method provides a visual or auditory record of events, enabling researchers to review and analyze data more accurately. The choice of recording method depends on various factors, including: Purpose of Observation: Whether the goal is to understand individual behaviors, group dynamics, or environmental factors. Complexity of Interactions: The level of detail required to capture the interactions accurately. Type of Population: Characteristics of the group being observed, such as age, culture, and social norms, may influence the choice of recording method. These details offer a comprehensive understanding of observation as a data collection method and the diverse approaches to recording observations effectively.
Report findings:
Reporting findings The attitudinal score only places respondents in a position relative to one another. Remember that the Likert scale does not measure the attitude per se, but helps you to rate a group of individuals in descending or ascending order with respect to their attitudes towards the issues in question. Reporting Findings: Attitudinal Score: Places respondents in a relative position, showing overall attitudes. The Likert scale ranks individuals but doesn't directly measure attitudes. Example: Survey on music preferences, and attitudinal score reveals the most favored genre. Example: Likert scale used to gauge customer satisfaction in a restaurant.
The formulation of objectives; the study population; and Establishing operational definitions:
Research Objectives: These are the goals you want to achieve with your study. Main Objective: The big goal of your study, is what you're trying to find out overall. Example: Main objective - Understand how exercise impacts mental health. Sub-Objectives: Smaller goals that help you achieve the main objective by focusing on specific aspects. Example: Sub-objective - Investigate the relationship between exercise frequency and stress levels. Study Population: One part of your study is about the people or things you're studying. One aspect of a study = the research problem; a second aspect = the study population, Example: If you're studying the effects of a new drug, the study population would be the group of patients who are taking that drug. Establishing Operational Definitions: This means clearly defining the terms you're using in a way that can be measured. Example: If you're studying "happiness," you might define it as "self-reported satisfaction with life on a scale of 1 to 10." This way, everyone knows exactly what you mean by "happiness" and how to measure it.
Constructing a research instrument in quantitative research:
Step 1: Define Objectives, Research Questions, or Hypotheses Start by clearly outlining what you want to achieve in your study. This could be specific goals, questions you want to answer, or hypotheses you want to test. Step 2: Break Down Objectives into Associated Questions For each objective, research question, or hypothesis, think about all the detailed questions you need to ask to address them. These questions help you break down the main objectives into smaller, more manageable parts. Step 3: Identify Required Information Look at each question from Step 2 and figure out what kind of information you need to answer it. This could include data on demographics, opinions, behaviors, etc. Step 4: Formulate Questions for Respondents Based on the information needed in Step 3, craft the specific questions you'll ask your participants. Make sure these questions are clear, easy to understand, and directly related to your research objectives. These steps provide a structured approach to developing a research instrument, ensuring that you collect relevant data to address your research goals effectively.
The process of research:
Step 1: Formulating a Research Problem: This is the foundational step in any research process. It involves identifying and defining a specific issue or question that you want to investigate. Your research problem should be clear, focused, and relevant to your field of study. It serves as the guiding force behind your entire research endeavor, influencing the subsequent steps you take. Conceptualizing a Research Design (Step 2): This step involves outlining how you will conduct your research. It's like planning the blueprint for your study. For example, if you're studying the impact of social media on teenagers' mental health, you need to decide on the methods you'll use to gather and analyze data. Constructing an Instrument for Data Collection (Step 3): This step focuses on creating tools or instruments to collect data. These could be surveys, questionnaires, interviews, etc. It's crucial to design these instruments carefully to ensure they gather the information you need accurately and effectively. Selecting a Sample (Step 4): Here, you decide who will participate in your study. For instance, if you're researching the effects of a new teaching method, you need to choose which schools or classrooms to include in your study and which students to survey or observe. Collecting Data (Step 6): This is where you actually gather the information according to your research design and using the instruments you've created. You might conduct surveys, interviews, experiments, or observations to collect data. Writing a Research Proposal (Step 5): Before starting your research, it's essential to outline your plan in a research proposal. This document explains your research question, methodology, and anticipated outcomes. It helps you get approval and funding for your study. Processing and Displaying Your Data (Step 7): On
The 4 steps in constructing a research instrument:
Step 1: Objectives/Research Questions/Hypotheses This step involves identifying the main objectives of your study, along with any specific research questions or hypotheses you want to explore. These objectives, questions, or hypotheses serve as the foundation for your research instrument. Step 2: Main and Associated Research Questions In this step, you break down each main objective, research question, or hypothesis into smaller, more detailed questions. These associated questions help provide clarity and specificity to your research objectives, guiding the data collection process. Step 3: Information Required For each of the questions identified in Step 2, determine the type of information or data you need to answer them effectively. This step involves specifying the variables, measures, or indicators required to address each question. Step 4: Questions Based on the information identified in Step 3, formulate the actual questions that you will ask your respondents. These questions should be clear, concise, and directly related to your research objectives. They serve as the instruments through which you gather data from your participants. By following these four steps, researchers can systematically develop a research instrument that aligns with their objectives, addresses their research questions, and collects the necessary data to support their study's findings.
Collecting Data using primary sources:
The Interview: Structured Interviews: These interviews follow a predetermined format with standardized questions. They are rigid and closed, meaning that the questions are fixed and asked in a specific order. Before being used, structured interviews are thoroughly pretested to ensure that the wording, meaning, and interpretation of questions are consistent. Unstructured Interviews: In contrast, unstructured interviews are more flexible and open-ended. They evolve as the conversation progresses, allowing the interviewer to delve deeply into topics. Unstructured interviews are particularly useful for exploring situations, phenomena, issues, or problems extensively. They provide varied and in-depth information, making them well-suited for identifying diversity and variety within responses. Collecting Data Using Primary Sources: The Questionnaire: A questionnaire consists of a written list of questions, with respondents providing written or verbal answers. A good questionnaire should be clear and easy to understand to ensure accurate responses. It should be well-organized, with questions logically sequenced to facilitate completion. The questionnaire should also be visually appealing, which can encourage respondents to participate and provide thoughtful responses. These details highlight the different approaches to conducting interviews (structured vs. unstructured) and the characteristics of a well-designed questionnaire as methods of data collection.
The functions of research design:
The functions of a research design are to: Identify and develop procedures: This means figuring out the steps and methods needed to carry out the study. For example, if researchers want to find out if exercise improves mood, they need to decide how they'll measure mood, how often they'll have participants exercise, and how they'll gather data. Ensure quality: Quality is crucial to make sure the study's results are reliable. This involves making sure the procedures are valid (actually measuring what they're supposed to), objective (free from bias), and accurate (giving true results). For instance, if researchers are conducting a survey on happiness, they need to ask questions in a way that doesn't lead participants to give certain answers, ensuring objectivity.
Types of research:
There are types of researches that is viewed by three perspectives: Applications of the findings: Pure Research: This type of research is like exploring the unknown just for the sake of understanding it better. Imagine you're an explorer, wandering through a mysterious forest, discovering different species of plants and animals. You're not looking for anything specific; you're just curious about what's out there. Applied Research: Now, imagine you're a problem solver. You've discovered a valuable plant in the forest, and you want to find out if it can be used to cure a disease affecting your village. So, you start experimenting to develop a medicine from this plant. Applied research is all about finding practical solutions to real-world problems. Objectives of the study: Descriptive Research: It's like taking a picture. You want to capture what something looks like at a particular moment. For example, if you're studying the eating habits of people in your town, you might conduct a survey to describe what kinds of food they eat most often. Correlational Research: Imagine you're trying to figure out if there's a connection between how much it rains and how many flowers bloom. You collect data over several months, and you notice that when it rains a lot, more flowers seem to bloom. Correlational research is about spotting patterns and relationships between different things. Explanatory Research: This type of research goes a step further. You're not just interested in spotting patterns; you want to understand why those patterns exist. For instance, if you find that people who exercise regularly tend to have lower stress levels, explanatory research would aim to uncover the reasons behind this connection. Exploratory Research: Think of exploratory research as setting off on an adventure. You're venturing into unknown terri
Formulating Effective Questions:
Use Simple and Everyday Language: Questions should be clear and easy for respondents to understand. Avoid using jargon or technical terms that might confuse participants. Avoid Ambiguity: Questions should be unambiguous to prevent confusion or misinterpretation. Ensure that each question has only one clear meaning. Avoid Double-Barrelled Questions: Double-barrelled questions combine multiple issues or topics within a single question. These should be avoided as they can lead to confusion and may yield unreliable responses. Avoid Leading Questions: Leading questions subtly guide respondents towards a particular answer or viewpoint. They should be avoided to maintain the neutrality and objectivity of the survey. Avoid Presumptions: Questions should not be based on assumptions about respondents' knowledge, beliefs, or experiences. Ensure that questions are neutral and do not make unwarranted assumptions. By following these guidelines, researchers can create effective survey questions that elicit accurate and meaningful responses from participants, thereby enhancing the quality of the data collected.
What is a variable? The difference between a concept and a variable; converting concepts into variables.
What is a Variable? A variable is something that can change or vary in a research study. It's a measurable aspect of a concept. Example: In a study on student performance, variables could include test scores, attendance rates, or study hours. Difference between a Concept and a Variable A concept is an abstract idea or notion, while a variable is a measurable aspect of that concept. Example: "Happiness" is a concept, but "self-reported satisfaction with life on a scale of 1 to 10" is a variable because it can be measured. Converting Concepts into Variables To turn a concept into a variable, you need indicators that represent the concept and can be measured. Example: If the concept is "teacher effectiveness," indicators might include student test scores, classroom observations, and student feedback. These indicators then become variables that can be measured.
A literature review:
is like the foundation of a building for any research project. It involves thoroughly looking at what other people have already written about the topic you're interested in. This includes reading both published (like books and articles) and unpublished (like theses and dissertations) works related to your field of study. A literature review serves several purposes. Bring clarity and focus to your research problem (Clarifies your research question): It helps you understand what's already known so you can focus on what's new. For example, if you want to study how video games affect children's behavior, a literature review would show you what other researchers have discovered about this topic. Improve your methodology (Helps you plan your study better): By looking at past studies, you can learn how to do yours effectively. Let's say you find a study that used surveys to ask kids about their gaming habits. You might decide to use a similar method in your own research. Broaden your knowledge base in your research area (teaches you more about your topic): It gives you a deeper understanding of what others have found. Reading different studies on video games and behavior could teach you about the different theories and ideas in this area. Contextualize your findings (Connect your work to what's been done before): It shows how your research fits into the bigger picture. After doing your study, you'd discuss how your findings add to what's already known about video games and children's behavior.
Steps in Formulating a Research Problem:
• Formulation of a research problem = most crucial part of research journey, • Every step that constitutes the how part of research journey depends on the way you formulated your research problem, • If you do not know what specific research topic, idea, questions or issue you want to research, the following steps are of immense help: Identify a broad field or subject area of interest: Start by choosing a general topic or area that you find interesting. For example, you might be interested in exploring the field of education. Dissect the broad area into subareas: Break down the broad topic into smaller, more specific subtopics or subareas. Continuing with the example of education, you could focus on areas like early childhood education, STEM education, or education policy. Select what is of most interest to you: Narrow down your focus to the specific subarea that you find most intriguing or relevant. If you're particularly passionate about STEM education, you might choose that as your focus. Raise research questions: Develop specific questions that you want to explore within your chosen subarea. For instance, you might ask, "How do hands-on STEM activities impact students' interest in science?" Formulate objectives: Define clear objectives or goals for your research that outline what you hope to achieve. This could include objectives such as understanding the effectiveness of different teaching methods or identifying barriers to STEM education. Assess your objectives: Evaluate whether your objectives are clear, achievable, and aligned with your research questions and overall goals. Double-check: Take a final look at your research questions and objectives to ensure they are well-defined and coherent before moving forward with your study.
The way you formulate a problem determines:
• type of study design • type of sampling • research instrument • type of analysis. Study design: How you plan to carry out your research, like doing surveys, experiments, or interviews. Sampling: How you choose the people or things to study. This could involve picking a random group, selecting based on certain criteria, or using other methods. Research tools: The methods or instruments you use to collect information, such as surveys, interviews, or observations. Data analysis: How you make sense of the information you gather, like using statistics or looking for patterns in people's responses.