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Problems in measurement in research – Validity and Reliability

The worst enemy of researchers, measurement mistakes can seriously undermine the validity and dependability of study findings. Let's examine the typical issues related to each idea and come up with solutionsCheck Here about: <a href="https://www.aprcet.co.in/2023/12/characteristics-of-good-measurement.html" target="_blank">Validity and Reliability in detail </a> Validity:
<ul data-sourcepos="5:1-6:18">
<li data-sourcepos="5:1-6:18">Content validity:
<ul data-sourcepos="6:5-6:18">
<li data-sourcepos="6:5-6:18">Problem: Items don't adequately capture the intended construct.
<ul data-sourcepos="7:9-7:112">
<li data-sourcepos="7:9-7:112">Solution: Conduct expert reviews, pilot testing, and revise items to better reflect the construct.</li>
</ul>
</li>
</ul>
</li>
<li data-sourcepos="8:1-10:121">Criterion validity:
<ul data-sourcepos="9:5-10:121">
<li data-sourcepos="9:5-10:121">Problem: The chosen criterion measure isn't relevant or reliable.
<ul data-sourcepos="10:9-10:121">
<li data-sourcepos="10:9-10:121">Solution: Select a well-established, relevant criterion measure and assess its reliability before using it.</li>
</ul>
</li>
</ul>
</li>
<li data-sourcepos="11:1-14:0">Construct validity:
<ul data-sourcepos="12:5-14:0">
<li data-sourcepos="12:5-14:0">Problem: The measure doesn't truly reflect the theoretical definition of the construct.
<ul data-sourcepos="13:9-14:0">
<li data-sourcepos="13:9-14:0">Solution: Use multi-methodological approaches (e.g., surveys, interviews, observations) and triangulation to gather converging evidence for construct validity.</li>
</ul>
</li>
</ul>
</li>
</ul>
Reliability:
<ul data-sourcepos="17:1-26:0">
<li data-sourcepos="17:1-19:142">Test-retest reliability:
<ul data-sourcepos="18:5-19:142">
<li data-sourcepos="18:5-19:142">Problem: Scores fluctuate significantly over time due to external factors.
<ul data-sourcepos="19:9-19:142">
<li data-sourcepos="19:9-19:142">Solution: Standardize testing conditions, increase time interval between tests, and control for potential confounding variables.</li>
</ul>
</li>
</ul>
</li>
<li data-sourcepos="20:1-22:130">Internal consistency reliability:
<ul data-sourcepos="21:5-22:130">
<li data-sourcepos="21:5-22:130">Problem: Items within a measure don't measure the same construct consistently.
<ul data-sourcepos="22:9-22:130">
<li data-sourcepos="22:9-22:130">Solution: Conduct item analysis, remove poorly performing items, and revise remaining items to increase homogeneity.</li>
</ul>
</li>
</ul>
</li>
<li data-sourcepos="23:1-26:0">Inter-rater reliability:
<ul data-sourcepos="24:5-26:0">
<li data-sourcepos="24:5-26:0">Problem: Different observers or raters disagree on their assessments.
<ul data-sourcepos="25:9-26:0">
<li data-sourcepos="25:9-26:0">Solution: Provide clear rating criteria, train raters thoroughly, and monitor inter-rater agreement throughout the study.</li>
</ul>
</li>
</ul>
</li>
</ul>
General tips for minimizing measurement errors:
<ul data-sourcepos="29:1-33:32">
<li data-sourcepos="29:1-29:94">Pilot test your measures: Identify and address potential problems before the main study.</li>
<li data-sourcepos="30:1-30:86">Use standardized procedures: Ensure consistency in data collection and analysis.</li>
<li data-sourcepos="31:1-31:81">Train and calibrate raters: Standardize assessments to reduce subjectivity.</li>
<li data-sourcepos="32:1-32:103">Report reliability and validity estimates: Be transparent about the quality of your measurements.</li>
<li data-sourcepos="33:1-33:32">Triangulate your findings: Use multiple methods to collect data and corroborate your findings.</li>
</ul>
Remember, good research isn't just about collecting data; it's about collecting accurate and reliable data. By being vigilant about potential measurement errors and taking steps to minimize them, you can ensure your research stands the test of time and contributes meaningfully to your field.

Research Methodology Problems in measurement in research – Validity and Reliability

The worst enemy of researchers, measurement mistakes can seriously undermine the validity and dependability of study findings. Let's ex...

Characteristics of a good measurement

19.12.23 dodda venkatareddy 0

<div>What are the characteristics of a good measurement tool? An intuitive answer to this question is that the tool should be an accurate counter or indicator of what we are interested in measuring. In addition, it should be easy and efficient to use. There are three major criteria for evaluating a measurement tool: validity, reliability, and practicality. <ul style="text-align: left;"><li>Validity is the extent to which a test measures what we actually wish to measure.</li><li>Reliability has to do with the accuracy and precision of a measurement procedure. </li><li>Practicality is concerned with a wide range of factors of economy, convenience, and interpretability</li></ul>1.Validity  </div><div>Many forms of validity are mentioned in the research literature, and the number grows as we expand the concern for more scientific measurement. This text features two major forms: external and internal validity. The external validity of research findings is the data’s ability to be generalized across persons, settings, and times;</div><div> </div> Internal validity is further limited in this discussion to the ability of a research instrument to measure what it is purported to measure.<div> </div> One widely accepted classification of validity consists of three major forms: (1) content validity (2) criterion-related validity and (3) construct validity. <div> </div><div> 1. Content Validity</div><div> </div><div>The content validity of a measuring instrument is the extent to which it provides adequate coverage of the investigative questions guiding the study. </div><div> </div><div>If the instrument contains a representative sample of the universe of subject matter of interest, then content validity is good. </div><div> </div><div>To evaluate the content validity  of an instrument, one must first agree on what elements constitute adequate coverage.</div><div> </div><div style="text-align: justify;">A second way is to use a panel of persons to judge how well the instrument meets the standards. The panel independently assesses the test items for an instrument as essential, useful but not essential, or not necessary. “Essential” responses on each item from each panelist are evaluated by a content validity ratio, and those meeting a statistical significance value are retained. In both informal judgments and this systematic process, “content validity is primarily concerned with inferences about test construction rather than inferences about test scores.</div><div style="text-align: justify;">Content validity, in the realm of research, refers to the degree to which an assessment tool, like a survey, test, or interview, accurately reflects the specific construct it's designed to measure.
Think of it this way: imagine you're trying to measure student engagement in a classroom. Your assessment tool, in this case, could be a questionnaire asking students about their participation in class discussions, their interest in the material, and their feelings of belonging in the classroom environment.
High content validity means that your questionnaire items effectively capture all the different aspects of student engagement. It wouldn't just focus on one specific aspect, like participation, but also consider factors like curiosity, motivation, and sense of community.
On the other hand, low content validity would occur if your questionnaire only asked about participation, neglecting other crucial aspects of engagement. This could lead to inaccurate conclusions about the overall level of engagement in the classroom.(2) criterion-related validity</div><div style="text-align: justify;">Criterion-related validity, sometimes called criterion validity, is an essential concept in research that assesses how well a test or measurement tool relates to a specific, external outcome or criterion. In simpler terms, it's about demonstrating that your test actually measures what it's supposed to measure in a way that has real-world implications.
Here's a deeper dive into how criterion-related validity plays out in research:
Key elements:
<ul data-sourcepos="7:1-7:123">
<li data-sourcepos="7:1-7:123">Predictor: This is the test or measurement tool you're evaluating. It could be anything from a standardized test to a newly developed survey.</li>
<li data-sourcepos="8:1-8:193">Criterion: This is the external variable or outcome that you're trying to predict or relate to. This could be something like job performance, academic achievement, or a specific behavior.</li>
<li data-sourcepos="9:1-10:0">Correlation: This refers to the statistical relationship between the predictor and criterion scores. A strong correlation indicates that there's a significant association between the two variables.</li>
</ul>
Types of criterion-related validity:
There are two main types of criterion-related validity, each with its own focus:
<ol data-sourcepos="15:1-15:55">
<li data-sourcepos="15:1-15:55">Concurrent validity:
 This compares the scores on your new test (predictor) with the scores 
on an established measure (criterion) of the same construct at the same 
time. In simpler terms, you're seeing how well your new test agrees with an already trusted one.</li>
</ol>
Example: Assessing how well scores on a newly developed anxiety questionnaire correlate with scores on a well-validated clinical anxiety scale administered simultaneously.
<ol data-sourcepos="19:1-20:0" start="2">
<li data-sourcepos="19:1-20:0">Predictive validity: This focuses on how well scores on your test (predictor) predict future performance or behavior related to the criterion. In other words, you're seeing how well your test can forecast what happens later.</li>
</ol>
Example: Examining how scores on a standardized college entrance exam relate to subsequent academic performance in the first year of college.
Methods for establishing criterion-related validity:
<ul data-sourcepos="25:1-26:26">
<li data-sourcepos="25:1-25:193">Correlation analysis: Calculating the correlation coefficient (e.g., Pearson's r) between the predictor and criterion scores provides a statistical measure of the relationship's strength.</li>
<li data-sourcepos="26:1-26:26">Regression analysis: This determines how well the predictor scores can predict the criterion scores, allowing for insights into the direction and strength of the relationship.</li>
</ul>
Strengths of criterion-related validity:
<ul data-sourcepos="30:1-33:0">
<li data-sourcepos="30:1-30:75">Provides direct evidence of the practical relevance of a test or measure.</li>
<li data-sourcepos="31:1-31:105">Helps researchers choose appropriate tools for specific research questions or decision-making purposes.</li>
<li data-sourcepos="32:1-33:0">Enhances the credibility and trustworthiness of research findings.</li>
</ul>
Limitations:
<ul data-sourcepos="36:1-38:0">
<li data-sourcepos="36:1-36:74">Relies on the availability of a suitable and reliable criterion measure.</li>
<li data-sourcepos="37:1-38:0">Can be influenced by factors like sample size, measurement error, and external variables.</li></ul> Any criterion measure must be judged in terms of four qualities: (1) relevance (2) freedom from bias (3) reliability (4) availability </div><div> </div><div> When evaluating a criterion measure in research, considering its relevance, freedom from bias, reliability, and availability is crucial for ensuring the validity and quality of your research findings. Let's break down each of these qualities further:
1. Relevance:
<ul data-sourcepos="5:1-8:131">
<li data-sourcepos="5:1-5:172">Definition: Does the criterion measure directly represent the construct or outcome you're trying to predict or relate to? Is it truly measuring what you intend it to?</li>
<li data-sourcepos="6:1-6:209">Importance: A relevant criterion measure ensures your findings are meaningful and insightful regarding the specific research question. Choosing an irrelevant measure would lead to misleading conclusions.</li>
<li data-sourcepos="7:1-8:131">Examples:
<ul data-sourcepos="8:5-8:131">
<li data-sourcepos="8:5-8:131">Predicting job performance: Using supervisor ratings as a criterion measure is more relevant than using employee satisfaction.</li>
<li data-sourcepos="9:5-10:0">Assessing anxiety levels: Utilizing a validated clinical anxiety scale as a criterion measure is more relevant than measuring general stress levels.</li>
</ul>
</li>
</ul>
2. Freedom from Bias:
<ul data-sourcepos="13:1-16:54">
<li data-sourcepos="13:1-13:128">Definition: Is the criterion measure free from systematic errors or prejudices that could unfairly influence the results?</li>
<li data-sourcepos="14:1-14:175">Importance: Bias-free measures ensure fairness and accuracy in your findings. Biased measures can distort the relationship between the predictor and criterion variables.</li>
<li data-sourcepos="15:1-16:54">Examples:
<ul data-sourcepos="16:5-16:54">
<li data-sourcepos="16:5-16:54">Selecting students based on
 socioeconomic status for a study on academic achievement can introduce 
bias based on social background.</li>
<li data-sourcepos="17:5-18:0">Using a self-reported 
measure of health that relies heavily on subjective interpretations can 
be prone to bias due to individual perceptions.</li>
</ul>
</li>
</ul>
3. Reliability:
<ul data-sourcepos="21:1-25:110">
<li data-sourcepos="21:1-21:151">Definition: Does the criterion measure produce consistent and stable results when repeated under similar conditions? Can it be reliably measured?</li>
<li data-sourcepos="22:1-22:183">Importance: Reliable measures ensure confidence in the accuracy and repeatability of your findings. Unreliable measures lead to inconsistent results and hinder generalizability.</li>
<li data-sourcepos="23:1-25:110">Examples:
<ul data-sourcepos="24:5-25:110">
<li data-sourcepos="24:5-24:125">A standardized test with high test-retest reliability ensures consistent scores even if administered on different days.</li>
<li data-sourcepos="25:5-25:110">Using multiple observers to rate participants' behavior in a study can enhance reliability by minimizing individual biases.</li>
</ul>
</li>
</ul>
4. Availability:
<ul data-sourcepos="29:1-34:0">
<li data-sourcepos="29:1-29:165">Definition: Is the criterion measure readily available, accessible, and practical to use in your research context? Can you easily collect and utilize the data?</li>
<li data-sourcepos="30:1-30:171">Importance: Available measures ensure feasibility and efficiency in conducting your research. Difficult-to-obtain or expensive measures can hinder research progress.</li>
<li data-sourcepos="31:1-34:0">Examples:
<ul data-sourcepos="32:5-34:0">
<li data-sourcepos="32:5-32:140">Utilizing public databases
 for economic indicators might be more readily available than conducting
 individual surveys with businesses.</li>
<li data-sourcepos="33:5-34:0">Choosing a simple and quick 
self-report measure of well-being might be more feasible than conducting
 in-depth clinical interviews.</li>
</ul>
</li>
</ul>
Remember, considering all four qualities of a criterion measure is crucial for making informed decisions in your research design and ensuring the validity and generalizability of your findings. Choose wisely, and your research will have a stronger foundation for accurate and insightful conclusions.</div><div>(3) construct validity</div><div>Construct validity, a crucial concept in research, goes beyond simply asking what a test or measure assesses. It delves deeper, asking how well it captures the underlying concept itself. In simpler terms, it's about ensuring your measure truly reflects the abstract construct you're interested in.
Imagine you're researching the concept of "student engagement." Surveys asking about participation may offer some insight, but construct validity demands a more multifaceted approach. It asks you to consider:
<ul data-sourcepos="7:1-10:0">
<li data-sourcepos="7:1-7:149">What are the different dimensions of student engagement? Curiosity, motivation, sense of community, and active learning are just a few aspects.</li>
<li data-sourcepos="8:1-8:151">Are your survey items capturing each dimension effectively? Generic questions about class attendance might miss the nuances of deeper engagement.</li>
<li data-sourcepos="9:1-10:0">Do the items hang together well? Do all questions about participation, for example, truly reflect the same underlying construct?</li>
</ul>
Addressing these questions helps establish construct validity through various methods:
1. Convergent Validity:
<ul data-sourcepos="15:1-16:0">
<li data-sourcepos="15:1-16:0">Does your measure correlate with other established measures of the same construct? If your engagement survey aligns with validated scales, it strengthens your confidence.</li>
</ul>
2. Divergent Validity:
<ul data-sourcepos="19:1-20:0">
<li data-sourcepos="19:1-20:0">Does your measure differ from measures of unrelated constructs? If an engagement survey doesn't significantly overlap with, say, a test anxiety scale, it suggests good discriminatory power.</li>
</ul>
3. Known-Groups Validity:
<ul data-sourcepos="23:1-24:0">
<li data-sourcepos="23:1-24:0">Does your measure differently in known groups expected to differ on the construct? For example, do engaged students score higher than disengaged ones on your survey?</li>
</ul>
4. Content Validity:
<ul data-sourcepos="27:1-28:0">
<li data-sourcepos="27:1-28:0">Do your items appropriately represent the theoretical definition of the construct? Expert reviews and pilot testing can ensure your content reflects the intended construct.</li>
</ul>
5. Multitrait-Multimethod (MTMM) Analysis:
<ul data-sourcepos="31:1-32:0">
<li data-sourcepos="31:1-32:0">This statistical technique assesses whether different measures of the same construct (measured in different ways) converge, while measures of different constructs diverge.</li>
</ul>
Construct validity isn't a single "yes" or "no" answer. It's a continuous process of gathering evidence to build confidence in your measure's ability to capture the elusive essence of the construct you're researching. This, in turn, strengthens the trustworthiness and value of your research findings.
Remember, a solid understanding of construct validity is essential for ensuring your research stands on firm ground. Don't hesitate to ask if you have any further questions about this intriguing concept! The following table summaries all types of validities </div><div> </div><div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjpTJdTeGHMn67nyRovV0kNWblHR81WAFCHCEiM8UpIrVQe541TVWqKZ6plZogPzwtBFVC0dNGlU9F5O9h5w12GtD8ZWbD4hvFKM6RYjTl8GMmOCfHws3VcYAN89ycCHO_5IPiTKGoEhuDfOIbLTSgej13AWzqVvMG9G-iFaXNF9oEC-oJCah2k-imw8Lw/s1197/Screenshot%202023-12-28%20210645.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="625" data-original-width="1197" height="334" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjpTJdTeGHMn67nyRovV0kNWblHR81WAFCHCEiM8UpIrVQe541TVWqKZ6plZogPzwtBFVC0dNGlU9F5O9h5w12GtD8ZWbD4hvFKM6RYjTl8GMmOCfHws3VcYAN89ycCHO_5IPiTKGoEhuDfOIbLTSgej13AWzqVvMG9G-iFaXNF9oEC-oJCah2k-imw8Lw/w640-h334/Screenshot%202023-12-28%20210645.png" width="640" /></a></div>  2. Reliability <div _ngcontent-ng-c671223614="" class="resize-observable"></div>In the world of research, reliability is about consistency and dependability. It asks the question: "If I repeat this study, would I get the same results?" Reliable research produces findings that are stable and replicable, increasing their trustworthiness and allowing for meaningful conclusions.
Imagine flipping a coin. A reliable coin toss would consistently land on heads or tails approximately 50% of the time. Similarly, a reliable research study consistently produces the same results when conducted under the same conditions. Here are some key aspects of reliability:
1. Types of Reliability:
<ul data-sourcepos="7:1-7:61">
<li data-sourcepos="7:1-7:61">Test-retest reliability: Assesses consistency over time. Would participants score the same on a test if they took it again later?</li>
<li data-sourcepos="8:1-8:146">Inter-rater reliability: Assesses consistency between different observers or raters. Would different teachers rate student essays similarly?</li>
<li data-sourcepos="9:1-9:169">Internal consistency reliability: Assesses consistency within a measure itself. Do different items on a questionnaire all measure the same construct (e.g., anxiety) consistently?</li>
</ul>
2. Importance of Reliability:
<ul data-sourcepos="13:1-16:0">
<li data-sourcepos="13:1-13:154">Increases confidence in findings: Reliable results are less likely to be due to chance or random error, bolstering the validity of your conclusions.</li>
<li data-sourcepos="14:1-14:143">Facilitates replication: Reliable studies can be replicated by other researchers, allowing for verification and advancement of knowledge.</li>
<li data-sourcepos="15:1-16:0">Enhances generalizability: Findings from reliable studies can be applied more confidently to wider populations or contexts.</li>
</ul>
3. Improving Reliability:
<ul data-sourcepos="19:1-23:0">
<li data-sourcepos="19:1-19:107">Use standardized procedures: Consistent methods of data collection and analysis minimize variability.</li>
<li data-sourcepos="20:1-20:102">Train and calibrate raters: Ensure consistent interpretation and application of rating criteria.</li>
<li data-sourcepos="21:1-21:91">Pilot test your measures: Identify and address potential flaws before the main study.</li>
<li data-sourcepos="22:1-23:0">Report reliability estimates: Share your results on tests of reliability to allow readers to assess the robustness of your findings.</li>
</ul>
Reliability is a cornerstone of strong research. By prioritizing and demonstrating consistency in your methods and measures, you pave the way for trustworthy findings that contribute meaningfully to your field.</div><h1 style="text-align: center;">Validity Versus Reliability</h1><div>Both validity and reliability are crucial concepts in research, but they address different aspects of measurement quality. Understanding their distinction is essential for ensuring rigorous and meaningful research.
Validity:
<ul data-sourcepos="5:1-5:56">
<li data-sourcepos="5:1-5:56">Focus: Accuracy of a measure. Does it actually assess what it's supposed to?</li>
<li data-sourcepos="6:1-6:83">Analogy: A thermometer that accurately reflects ambient temperature is valid.</li>
<li data-sourcepos="7:1-8:27">Types:
<ul data-sourcepos="8:5-8:27">
<li data-sourcepos="8:5-8:27">Content validity: Do items capture the intended construct?</li>
<li data-sourcepos="9:5-9:81">Criterion validity: Does the measure relate to other relevant outcomes?</li>
<li data-sourcepos="10:5-10:29">Construct validity: Does the measure reflect the theoretical definition of the construct?</li>
</ul>
</li>
</ul>
Reliability:
<ul data-sourcepos="14:1-19:47">
<li data-sourcepos="14:1-14:102">Focus: Consistency of a measure. Does it produce the same results under the same conditions?</li>
<li data-sourcepos="15:1-15:76">Analogy: A scale that consistently weighs the same object is reliable.</li>
<li data-sourcepos="16:1-19:47">Types:
<ul data-sourcepos="17:5-19:47">
<li data-sourcepos="17:5-17:73">Test-retest reliability: Do scores remain consistent over time?</li>
<li data-sourcepos="18:5-18:120">Internal consistency reliability: Do different items within a measure consistently reflect the same construct?</li>
<li data-sourcepos="19:5-19:47">Inter-rater reliability: Do different observers agree on their assessments?</li>
</ul>
</li>
</ul>
The Relationship:
<ul data-sourcepos="23:1-25:0">
<li data-sourcepos="23:1-23:185">Reliability is necessary but not sufficient for validity. A consistent measure is not necessarily accurate. Imagine a faulty thermometer consistently producing incorrect readings.</li>
<li data-sourcepos="24:1-25:0">Validity often depends on reliability. If a measure is not consistent, it's difficult to be confident in its accuracy. Think of a scale that fluctuates with every use.</li>
</ul>
In Practice:
<ul data-sourcepos="28:1-31:0">
<li data-sourcepos="28:1-28:102">Both validity and reliability should be considered in choosing and developing research measures.</li>
<li data-sourcepos="29:1-29:80">Methods like pilot testing and expert reviews can help establish validity.</li>
<li data-sourcepos="30:1-31:0">Reliability can be improved through standardized procedures and training.</li>
</ul>
By focusing on both validity and reliability, researchers can ensure their measurements are accurate and consistent, leading to more trustworthy and robust research findings.3.Practicality The scientific requirements of a project call for the measurement process to be reliable and valid, while the operational requirements call for it to be practical. Practicality has been defined as economy, convenience, and interpretability . Although this definition refers to the development of educational and psychological tests, it is meaningful for business measurements as well. </div>

Research Methodology Characteristics of a good measurement

What are the characteristics of a good measurement tool? An intuitive answer to this question is that the tool should be an accurate counter...

Levels of measurement – Nominal, Ordinal, Interval, Ratio.

13.12.23 dodda venkatareddy 0

In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. For each concept or construct, several types of measurement are possible; the appropriate choice depends on what you assume about the mapping rules. Each one has its own set of underlying assumptions about how the numerical symbols correspond to real-world observations.Mapping rules have four assumptions: 1. Numbers are used to classify, group, or sort responses. No order exists. 2. Numbers are ordered. One number is greater than, less than, or equal to another number. 3. Differences between numbers are ordered. The difference between any pair of numbers is greater than,less than, or equal to the difference between any other pair of numbers. 4. The number series has a unique origin indicated by the number zero. This is an absolute and meaningful zero point<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAIscD8OnlKhLS-fw1nwuRirv9J-ngiYl4qNc8jAUuTD4VRgnBzGwYi56nX0B7Hj5WvurPhJUJhYNH3RSt-T_zY5hcPkuHGRFfGm2fBhbeFIkWVY2gk_4ur4c9C_duQtBWUj_ykd_WuI1OXp92xg5KL4NTbunC1AZalKEZj7KCucJ06tvBdTCjRkSDsOE/s1398/Screenshot%202023-12-13%20204426.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="407" data-original-width="1398" height="186" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhAIscD8OnlKhLS-fw1nwuRirv9J-ngiYl4qNc8jAUuTD4VRgnBzGwYi56nX0B7Hj5WvurPhJUJhYNH3RSt-T_zY5hcPkuHGRFfGm2fBhbeFIkWVY2gk_4ur4c9C_duQtBWUj_ykd_WuI1OXp92xg5KL4NTbunC1AZalKEZj7KCucJ06tvBdTCjRkSDsOE/w640-h186/Screenshot%202023-12-13%20204426.png" width="640" /></a></div><h2 id="why-is-level-of-measurement-important">Why is Level of Measurement Important?</h2>First,
 knowing the level of measurement helps you decide how to interpret the 
data from that variable. When you know that a measure is nominal (like 
the one just described), then you know that the numerical values are 
just short codes for the longer names. Second, knowing the level of 
measurement helps you decide what statistical analysis is appropriate on
 the values that were assigned. If a measure is nominal, then you know 
that you would never average the data values or do a t-test on the data.There are typically four levels of measurement that are defined:<ul><li>Nominal</li><li>Ordinal</li><li>Interval</li><li>Ratio</li></ul>In nominal
 measurement the numerical values just “name” the attribute uniquely. No
 ordering of the cases is implied. For example, jersey numbers in 
basketball are measures at the nominal level. A player with number <code>30</code> is not more of anything than a player with number <code>15</code>, and is certainly not twice whatever number <code>15</code> is.In ordinal
 measurement the attributes can be rank-ordered. Here, distances between
 attributes do not have any meaning. For example, on a survey you might 
code Educational Attainment as 0=less than high school; 1=some high 
school.; 2=high school degree; 3=some college; 4=college degree; 5=post 
college. In this measure, higher numbers mean more education. 
But is distance from 0 to 1 same as 3 to 4? Of course not. The interval 
between values is not interpretable in an ordinal measure.<div class="separator" style="clear: both; text-align: center;"><a href="https://conjointly.com/img/kb/Assets/images/measlev2.gif" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="268" data-original-width="400" height="268" src="https://conjointly.com/img/kb/Assets/images/measlev2.gif" width="400" /></a></div>In interval measurement the distance between attributes does
 have meaning. For example, when we measure temperature (in Fahrenheit),
 the distance from 30-40 is same as distance from 70-80. The interval 
between values is interpretable. Because of this, it makes sense to 
compute an average of an interval variable, where it doesn’t make sense 
to do so for ordinal scales. But note that in interval measurement 
ratios don’t make any sense - 80 degrees is not twice as hot as 40 
degrees (although the attribute value is twice as large).Finally, in ratio
 measurement there is always an absolute zero that is meaningful. This 
means that you can construct a meaningful fraction (or ratio) with a 
ratio variable. Weight is a ratio variable. In applied social research 
most “count” variables are ratio, for example, the number of clients in 
past six months. Why? Because you can have zero clients and because it 
is meaningful to say that “…we had twice as many clients in the past six
 months as we did in the previous six months.”It’s important to 
recognize that there is a hierarchy implied in the level of measurement 
idea. At lower levels of measurement, assumptions tend to be less 
restrictive and data analyses tend to be less sensitive. At each level 
up the hierarchy, the current level includes all of the qualities of the
 one below it and adds something new. In general, it is desirable to 
have a higher level of measurement (e.g., interval or ratio) rather than
 a lower one (nominal or ordinal).Importance of Measurement:
Measurement plays a crucial role in research by:
<ul data-sourcepos="28:1-32:0">
<li data-sourcepos="28:1-28:138">Providing a common language: Numbers and categories allow researchers to communicate their findings clearly and concisely to others.</li>
<li data-sourcepos="29:1-29:173">Enabling data analysis: Measurement allows researchers to use statistical and other analytical tools to explore patterns, relationships, and differences in their data.</li>
<li data-sourcepos="30:1-30:165">Enhancing objectivity: By assigning numbers or labels to characteristics, researchers can minimize subjectivity and bias in their data collection and analysis.</li>
<li data-sourcepos="31:1-32:0">Facilitating generalization: Measurement allows researchers to draw conclusions about populations based on data collected from samples.</li>
</ul>
Challenges of Measurement:
However, measurement also presents several challenges:
<ul data-sourcepos="37:1-41:0">
<li data-sourcepos="37:1-37:197">Choosing the right level of measurement:
 Selecting the appropriate level of measurement depends on the nature of
 the characteristics being measured and the research questions being 
addressed.</li>
<li data-sourcepos="38:1-38:115">Ensuring measurement reliability: Measurement should be consistent and reproducible to provide accurate data.</li>
<li data-sourcepos="39:1-39:119">Ensuring measurement validity: Measurement should actually reflect the characteristics it is intended to measure.</li>
<li data-sourcepos="40:1-41:0">Dealing with missing data: Missing data can arise due to various reasons and can complicate data analysis.</li></ul> Source: <a href="https://conjointly.com/kb/levels-of-measurement/">https://conjointly.com/kb/levels-of-measurement/</a>

Research Methodology Levels of measurement – Nominal, Ordinal, Interval, Ratio.

In measuring, one devises some mapping rule and then translates the observation of property indicants using this rule. For each concept or c...

Measurement: Concept of measurement– what is measured?

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Measurement in research consists of assigning numbers to empirical events, objects or properties, or activities in compliance with a set of rules. This definition implies that measurement is a three-part process:1. Selecting observable empirical events. 2. Developing a set of mapping rules: a scheme for assigning numbers or symbols to represent aspects of the event being measured. 3. Applying the mapping rule(s) to each observation of that event.<h3 style="text-align: left;">Concept of measurement</h3>n research, measurement is the process of assigning numbers or labels to characteristics of objects or events to quantify and analyze them. It is a fundamental tool for researchers across various disciplines, allowing them to collect and analyze data objectively and draw meaningful conclusions.
There are two main types of measurement:
1. Quantitative Measurement: This involves assigning numerical values to characteristics that can be ordered or ranked. Examples include measuring the height of students, the weight of objects, or the time it takes to complete a task. Quantitative data can be analyzed statistically to identify patterns, relationships, and differences between.2. Qualitative Measurement: This involves assigning labels or categories to characteristics that cannot be easily ordered or ranked. Examples include classifying rocks by type, describing the color of a flower, or identifying the emotions expressed by a character in a story. Qualitative data can be analyzed through various methods such as coding, thematic analysis, and discourse analysis.  <h3 style="text-align: left;"> what is measured? </h3>Research studies study variables as objects or properties, including tangible items like furniture, genes, attitudes, and peer-group pressures. Properties include physical, psychological, and social characteristics of an individual, such as weight, height, posture, leadership ability, class affiliation, and status.Researchers measure indicates of properties rather than objects or properties. For instance, analyzing a sales force to understand personal properties like age, experience, and weekly calls, the indicates are so accepted that they can be directly observed.  Measurement of constructs like lifestyles, opinion leadership, distribution channel structure, and persuasiveness is challenging due to inferences based on indicate measurements. Quality of a study depends on the selection of measures, their fit to the circumstances, nature of measurement scales, sources of error, and sound measurement characteristics.

Research Methodology Measurement: Concept of measurement– what is measured?

Measurement in research consists of assigning numbers to empirical events, objects or properties, or activities in compliance with a set of...

Merging the two approaches - Qualitative and Quantitative

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Contrasts between Quantitative and Qualitative Research
Here's a table summarizing some key contrasts between quantitative and qualitative research:<div class="horizontal-scroll-wrapper">
<div class="table-block-component"><div _ngcontent-ng-c1009100734="" class="table-block"><div _ngcontent-ng-c1009100734="" class="table-content"><table data-sourcepos="5:1-15:146"><tbody><tr data-sourcepos="5:1-5:74"><th data-sourcepos="5:1-5:25">Feature</th><th data-sourcepos="5:27-5:49">Quantitative Research</th><th data-sourcepos="5:51-5:72">Qualitative Research</th></tr><tr data-sourcepos="7:1-7:76"><td data-sourcepos="7:1-7:25">Data Type</td><td data-sourcepos="7:27-7:51">Numerical, quantifiable</td><td data-sourcepos="7:53-7:74">Textual, descriptive</td></tr><tr data-sourcepos="8:1-8:129"><td data-sourcepos="8:1-8:25">Focus</td><td data-sourcepos="8:27-8:75">Establishing relationships, measuring variables</td><td data-sourcepos="8:77-8:127">Understanding meanings, experiences, and contexts</td></tr><tr data-sourcepos="9:1-9:136"><td data-sourcepos="9:1-9:25">Goals</td><td data-sourcepos="9:27-9:69">Testing hypotheses, generalizing findings</td><td data-sourcepos="9:71-9:134">Describing phenomena, generating insights, exploring new areas</td></tr><tr data-sourcepos="10:1-10:99"><td data-sourcepos="10:1-10:24">Research Questions</td><td data-sourcepos="10:26-10:64">Specific, focused, often closed-ended</td><td data-sourcepos="10:66-10:97">Broad, open-ended, exploratory</td></tr><tr data-sourcepos="11:1-11:156"><td data-sourcepos="11:1-11:29">Data Collection Methods</td><td data-sourcepos="11:31-11:79">Surveys, experiments, observations (structured)</td><td data-sourcepos="11:81-11:154">Interviews, focus groups, observations (unstructured), document analysis</td></tr><tr data-sourcepos="12:1-12:123"><td data-sourcepos="12:1-12:27">Data Analysis Methods</td><td data-sourcepos="12:29-12:73">Statistical analysis, quantitative software</td><td data-sourcepos="12:75-12:121">Coding, thematic analysis, discourse analysis</td></tr><tr data-sourcepos="13:1-13:135"><td data-sourcepos="13:1-13:22">Generalizability</td><td data-sourcepos="13:24-13:73">Aims to generalize findings to wider populations</td><td data-sourcepos="13:75-13:133">Findings specific to the context and participants studied</td></tr><tr data-sourcepos="14:1-14:143"><td data-sourcepos="14:1-14:15">Causality</td><td data-sourcepos="14:17-14:72">Focuses on establishing cause-and-effect relationships</td><td data-sourcepos="14:74-14:141">Explores the complex interplay of factors influencing a phenomenon</td></tr><tr data-sourcepos="15:1-15:146"><td data-sourcepos="15:1-15:17">Replication</td><td data-sourcepos="15:19-15:57">Aims for exact replication of studies</td><td data-sourcepos="15:59-15:144">Focuses on transferability of findings to other contexts and theoretical replication</td></tr></tbody></table></div><div _ngcontent-ng-c1009100734="" class="table-footer hide-from-message-actions ng-star-inserted"></div></div></div></div><div class="horizontal-scroll-wrapper"><div class="table-block-component"><div _ngcontent-ng-c1009100734="" class="table-block"></div></div>
</div>
 
Researcher Role: Quantitative research often adopts a more objective stance, with the researcher minimizing their influence on the study. Qualitative research, on the other hand, acknowledges the subjective nature of research and emphasizes the importance of the researcher's role in shaping the study.
Flexibility: Quantitative research is typically more structured and follows a predetermined research design. Qualitative research is more flexible and allows for changes to the research design as needed throughout the research process.
Validity and Reliability: Quantitative research emphasizes internal validity (ensuring the study measures what it intends to measure) and external validity (generalizability of findings). Qualitative research focuses on credibility (believable findings), dependability (consistency in data collection and analysis), transferability (applicability of findings to other contexts), and confirmability (neutrality of findings).
Dissemination: Quantitative research findings are typically disseminated through research articles and other formal publications. Qualitative research findings can be disseminated through various formats such as research reports, articles, books, and even creative media like documentaries.
Strengths and Weaknesses: Each approach has its own strengths and weaknesses depending on the research question and goals. Quantitative research is powerful for establishing relationships, testing hypotheses, and making generalizations. However, it may overlook the nuances of human experience and social phenomena. Qualitative research excels at exploring complex issues, understanding meanings, and generating rich insights. However, it can be challenging to generalize findings and establish clear causal relationships.
Ultimately, the choice between quantitative and qualitative research methods depends on the specific research question and the researcher's goals. Often, the most effective research utilizes mixed methods, combining both quantitative and qualitative approaches to gain a more comprehensive understanding of the research topic.<h2 class="" data-sourcepos="1:1-1:61">Similarities between Quantitative and Qualitative Research</h2>
Despite their differences, quantitative and qualitative research share some key similarities:
1. Rigor and Credibility: Both approaches seek to conduct rigorous and credible research, adhering to ethical principles and employing methods that ensure the reliability and validity of findings.
2. Systematic Process: Both involve a systematic process of defining research questions, selecting appropriate methods, collecting data, analyzing data, and interpreting findings.
3. Importance of Theory: Both rely on existing theories to inform the research questions and interpret the findings, ultimately contributing to the development and refinement of theoretical frameworks.
4. Data Analysis and Interpretation: Both involve data analysis and interpretation, seeking to identify patterns, themes, and insights from the collected data.
5. Goal of Understanding: Both ultimately aim to understand and explain phenomena, whether by testing hypotheses, exploring new areas, or generating rich descriptions.
6. Contribution to Knowledge: Both contribute to the broader body of knowledge in their respective fields, advancing our understanding of the world around us.<h2 class="" data-sourcepos="1:1-1:74">Merging Qualitative and Quantitative Approaches: Mixed Methods Research</h2>
Merging qualitative and quantitative approaches, often referred to as mixed methods research, offers a powerful way to gain a more comprehensive understanding of a research topic. By combining the strengths of both approaches, researchers can:
<ul data-sourcepos="5:1-5:42">
<li data-sourcepos="5:1-5:42">Triangulate findings: Strengthen the credibility of findings by corroborating them through different methods and perspectives.</li>
<li data-sourcepos="6:1-6:83">Gain deeper insights: Explore the "why" behind the "what," using quantitative data to provide context and generalization, and qualitative data to delve into the meanings and experiences.</li>
<li data-sourcepos="7:1-7:91">Address complex research questions: Investigate multifaceted topics that require both quantitative and qualitative data to fully understand.</li>
</ul>
Here are some common ways to merge quantitative and qualitative approaches:
Sequential design:
<ul data-sourcepos="13:1-15:0">
<li data-sourcepos="13:1-13:118">Conduct qualitative research first to explore the research question and inform the design of the quantitative study.</li>
<li data-sourcepos="14:1-15:0">Follow up with quantitative research to test hypotheses or generalize findings.</li>
</ul>
Concurrent design:
<ul data-sourcepos="18:1-20:0">
<li data-sourcepos="18:1-18:64">Collect both quantitative and qualitative data simultaneously.</li>
<li data-sourcepos="19:1-20:0">Analyze each type of data separately, then integrate findings to gain a holistic understanding.</li>
</ul>
Embedded design:
<ul data-sourcepos="23:1-24:0">
<li data-sourcepos="23:1-24:0">Integrate qualitative elements within a quantitative study, such as conducting interviews with a subset of participants to explore their experiences or motivations.</li>
</ul>
Mixed methods research can be applied in various fields, including:
<ul data-sourcepos="27:1-30:0">
<li data-sourcepos="27:1-27:194">Education:
 Studying the effectiveness of educational interventions by combining 
quantitative data on student learning outcomes with qualitative data on 
student experiences and perceptions.</li>
<li data-sourcepos="28:1-28:211">Healthcare:
 Exploring the factors influencing patient adherence to treatment plans 
by combining quantitative data on medication adherence rates with 
qualitative data on patient interviews and focus groups.</li>
<li data-sourcepos="29:1-30:0">Marketing: 
Understanding consumer preferences and motivations by combining 
quantitative data on market share and sales figures with qualitative 
data on consumer surveys and focus groups.</li></ul><div aria-hidden="true" class="cdk-visually-hidden cdk-focus-trap-anchor"></div><div _ngcontent-ng-c2406812362="" aria-atomic="true" aria-live="polite" class="container" role="dialog"><div _ngcontent-ng-c2459111301="" aria-atomic="true" aria-live="polite" class="recitation-tooltip"><a _ngcontent-ng-c2459111301="" class="recitation-link" href="https://link.springer.com/article/10.1007/s11528-018-0314-5" rel="noopener noreferrer" target="_blank"><div _ngcontent-ng-c2459111301="" class="text-container"></div></a></div></div><div aria-hidden="true" class="cdk-visually-hidden cdk-focus-trap-anchor"></div><div aria-hidden="true" class="cdk-visually-hidden cdk-focus-trap-anchor"></div><div _ngcontent-ng-c2406812362="" aria-atomic="true" aria-live="polite" class="container" role="dialog"><div _ngcontent-ng-c2459111301="" aria-atomic="true" aria-live="polite" class="recitation-tooltip"><a _ngcontent-ng-c2459111301="" class="recitation-link" href="https://cyberleninka.org/article/n/893712" rel="noopener noreferrer" target="_blank"><div _ngcontent-ng-c2459111301="" class="text-container"></div></a></div></div><div aria-hidden="true" class="cdk-visually-hidden cdk-focus-trap-anchor"></div>Challenges of mixed methods research:
<ul data-sourcepos="39:1-40:72">
<li data-sourcepos="39:1-39:137">Increased complexity: Designing, conducting, and analyzing mixed methods research can be more complex than using a single approach.</li>
<li data-sourcepos="40:1-40:72">Integration issues: Combining and interpreting data from different sources requires careful planning and thoughtful analysis.</li>
<li data-sourcepos="41:1-42:0">Resource limitations: Mixed methods research may require additional resources, such as time, funding, and expertise.</li>
</ul>
Despite these challenges, mixed methods research offers a valuable approach for researchers seeking to gain a comprehensive understanding of complex phenomena. By effectively merging quantitative and qualitative approaches, researchers can contribute significantly to the advancement of knowledge and address critical issues in diverse fields.<h4 _ngcontent-ng-c1550019022="" class="gmat-overline header ng-star-inserted"> Sources </h4><a _ngcontent-ng-c1550019022="" class="icon-link ng-star-inserted" href="https://bard.google.com/faq#citation" rel="noopener noreferrer" target="_blank"></a><div _ngcontent-ng-c1550019022="" class="sources-header"><a _ngcontent-ng-c1550019022="" class="icon-link ng-star-inserted" href="https://bard.google.com/faq#citation" rel="noopener noreferrer" target="_blank"></a></div><ol _ngcontent-ng-c1550019022="" class="recitations-list ng-star-inserted" id="message-content-recitation-list" start="1"><li _ngcontent-ng-c1550019022="" class="recitation-list-content ng-star-inserted"><div _ngcontent-ng-c1550019022=""><div _ngcontent-ng-c1550019022="" class="recitation-link"><a _ngcontent-ng-c1550019022="" href="https://link.springer.com/article/10.1007/s11528-018-0314-5" rel="noopener noreferrer" target="_blank"> link.springer.com/article/10.1007/s11528-018-0314-5 </a></div></div></li><li _ngcontent-ng-c1550019022="" class="recitation-list-content ng-star-inserted"><div _ngcontent-ng-c1550019022=""><div _ngcontent-ng-c1550019022="" class="recitation-link"><a _ngcontent-ng-c1550019022="" href="https://cyberleninka.org/article/n/893712" rel="noopener noreferrer" target="_blank"> cyberleninka.org/article/n/893712 </a></div></div></li></ol>

Research Methodology Merging the two approaches - Qualitative and Quantitative

Contrasts between Quantitative and Qualitative Research Here's a table summarizing some key contrasts between quantitative and qualitat...

causality, generalization, replication in qualitative research

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While quantitative research often emphasizes establishing causal relationships, generalizing findings, and replicating studies, these concepts are approached differently in qualitative research. Let's explore each concept:1. Causality:<ul data-sourcepos="7:1-11:0"><li data-sourcepos="7:1-7:131">Qualitative research aims to understand 
how and why things happen rather than simply establishing 
cause-and-effect relationships.</li><li data-sourcepos="8:1-8:139">Due to the complex nature of social 
phenomena and the influence of various factors, establishing definitive 
causality can be challenging.</li><li data-sourcepos="9:1-9:96">Researchers often explore how different factors interact and contribute to a specific outcome.</li><li data-sourcepos="10:1-11:0">Strategies like thick descriptions, triangulation, and member checking can help enhance the credibility of causal claims.</li></ul>2. Generalization:<ul data-sourcepos="14:1-18:0"><li data-sourcepos="14:1-14:128">Traditional notions of generalizability (applying findings to a broader population) are less relevant in qualitative research.</li><li data-sourcepos="15:1-15:59">Instead, the concept of transferability is preferred.</li><li data-sourcepos="16:1-16:144">Transferability focuses on the applicability of the findings to different contexts based on similarities and differences between contexts.</li><li data-sourcepos="17:1-18:0">Researchers provide rich descriptions and
 contextual details to enable readers to judge the relevance of the 
findings to their own context. </li></ul>3. Replication:<ul data-sourcepos="21:1-25:0"><li data-sourcepos="21:1-21:179">Exact replication of qualitative 
research is often impractical due to the dynamic nature of social 
settings and the influence of context and researcher-participant 
interactions.</li><li data-sourcepos="22:1-22:141">Instead, researchers may conduct comparative studies across different contexts to examine the similarities and differences in findings.</li><li data-sourcepos="23:1-23:129">Theoretical replication focuses on using the findings of a study to contribute to or refine existing theories in the field.</li><li data-sourcepos="24:1-25:0">Repeatable elements like data collection methods and analysis procedures can be documented to allow for future studies to build upon the findings.</li></ul>Here are some key points to remember:<ul data-sourcepos="28:1-31:0"><li data-sourcepos="28:1-28:148">Qualitative research focuses on 
understanding the complexities of social phenomena rather than 
establishing simple cause-and-effect relationships.</li><li data-sourcepos="29:1-29:101">Generalizability is less relevant, and transferability of findings to other contexts is emphasized.</li><li data-sourcepos="30:1-31:0">Replication often takes the form of comparative studies or building upon existing theories.</li></ul>References:<ul data-sourcepos="34:1-36:152"><li data-sourcepos="34:1-34:125">Denzin, N. K., & Lincoln, Y. S. (2013). Collecting and interpreting qualitative materials (4th ed.). Sage Publications.</li><li data-sourcepos="35:1-35:83">Silverman, D. (2016). Interpreting qualitative data (5th ed.). Sage Publications.</li><li data-sourcepos="36:1-36:152">Shenton,
 A. K. (2004). Strategies for ensuring trustworthiness in qualitative 
research projects. Education for information, 22(2), 63-75.</li></ul>

Research Methodology causality, generalization, replication in qualitative research

While quantitative research often emphasizes establishing causal relationships, generalizing findings, and replicating studies, these conce...

Qualitative research process Research Methodology

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Qualitative research involves collecting and analyzing non-numerical 
data (e.g., text, video, or audio) to understand concepts, opinions, or 
experiences. It can be used to gather in-depth insights into a problem 
or generate new ideas for research.
Qualitative research is the opposite of quantitative research, which involves collecting and analyzing numerical data for statistical analysis.
Qualitative research is commonly used in the humanities and social 
sciences, in subjects such as anthropology, sociology, education, health
 sciences, history, etc.Qualitative research is a research strategy that usually emphasizes words rather than quantifi cation in the collection and analysis of data. As a research strategy it is inductivist, constructionist, and interpretivist, but qualitative researchers do not always subscribe to all three of these methods.<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcILKKPgFxjopHiB5Aynpvi7dEHTeM_3unHWQ-2mWlcI3q09yauerZ3yUnHfnX12YitZAuSLI09OSjQojE66FmFdKJwAlT4ZFbUzKXOPKgpM3iCoBomnzZRQ04uTfWkEbhjkMuwCbjWJChcGRwghw8ZLWtwLUh-oF2qoMF2hTU8OJ4LSF8TnlrM8lku8U/s846/Screenshot%202023-12-13%20184511.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="547" data-original-width="846" height="414" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjcILKKPgFxjopHiB5Aynpvi7dEHTeM_3unHWQ-2mWlcI3q09yauerZ3yUnHfnX12YitZAuSLI09OSjQojE66FmFdKJwAlT4ZFbUzKXOPKgpM3iCoBomnzZRQ04uTfWkEbhjkMuwCbjWJChcGRwghw8ZLWtwLUh-oF2qoMF2hTU8OJ4LSF8TnlrM8lku8U/w640-h414/Screenshot%202023-12-13%20184511.png" width="640" /></a></div>  <ol data-sourcepos="3:1-3:129"><li data-sourcepos="3:1-3:129">General research questions: This step involves developing broad research questions that guide the entire research process.</li>
<li data-sourcepos="4:1-4:129">Selecting relevant site(s) and subjects: This step involves identifying the appropriate research setting and participants.</li>
<li data-sourcepos="5:1-5:32">Collection of relevant data: This step involves collecting data using a variety of qualitative methods, such as interviews, focus groups, observations, and document analysis.</li>
<li data-sourcepos="6:1-6:22">Interpretation of data: This step involves transcribing and organizing the data, identifying patterns and themes, and developing a theoretical framework to explain the findings.</li>
<li data-sourcepos="7:1-7:15">Conceptual and theoretical work:
 This step involves developing a conceptual or theoretical framework to 
explain the findings and situating the research within the broader body 
of knowledge.</li>
<li data-sourcepos="8:1-9:0">Writing up findings/conclusions: This step involves writing a research report that presents the findings, conclusions, and implications of the research.</li>
</ol>
The two additional steps:
<ul data-sourcepos="12:1-14:0">
<li data-sourcepos="12:1-12:147">5a. Tighter specification of the research question(s): This step involves refining the research questions based on the initial data analysis.</li>
<li data-sourcepos="13:1-14:0">5b. Collection of further data: This step involves collecting additional data to clarify or expand on the findings.</li>
</ul>
These two steps are not always necessary, but they can be helpful in ensuring the rigor and completeness of the research.
<h2 class="" data-sourcepos="17:1-17:27">Explanation of each step</h2>
1. General research questions
The first step in qualitative research is to develop broad research questions that guide the entire research process. The research questions should be specific enough to focus the research, but broad enough to allow for exploration and discovery. Examples of general research questions include:
<ul data-sourcepos="23:1-26:0">
<li data-sourcepos="23:1-23:55">What are the experiences of people living in poverty?</li>
<li data-sourcepos="24:1-24:58">What are the factors that contribute to student success?</li>
<li data-sourcepos="25:1-26:0">How does organizational culture impact employee engagement?</li>
</ul>
2. Selecting relevant site(s) and subjects
Once the research questions have been developed, the next step is to identify the appropriate research setting and participants. This involves considering the following factors:
<ul data-sourcepos="31:1-33:0">
<li data-sourcepos="31:1-31:266">Research setting: Where is the best place to collect data to answer the research questions? For example, if you are interested in studying the experiences of people living in poverty, you might conduct research in a low-income neighborhood or community center.</li>
<li data-sourcepos="32:1-33:0">Participants: Who are the best people to provide data to answer the research questions? For example, if you are interested in studying student success, you might recruit participants from a variety of academic backgrounds and disciplines.</li>
</ul>
3. Collection of relevant data
The third step in qualitative research is to collect data using a variety of qualitative methods. Some common qualitative methods include:
<ul data-sourcepos="38:1-42:0">
<li data-sourcepos="38:1-38:186">Interviews: Interviews involve one-on-one conversations with participants. Interviews can be structured (with pre-determined questions) or unstructured (with open-ended questions).</li>
<li data-sourcepos="39:1-39:155">Focus groups: Focus groups involve group discussions with participants. Focus groups are often used to explore a specific topic or to generate ideas.</li>
<li data-sourcepos="40:1-40:275">Observations: Observations involve watching and recording participants' behavior in a natural setting.
 Observations can be overt (where the participants are aware of the 
researcher's presence) or covert (where the participants are unaware of 
the researcher's presence).</li>
<li data-sourcepos="41:1-42:0">Document analysis: Document analysis involves analyzing existing documents, such as diaries, letters, news articles, or government reports.
 Document analysis can be used to learn about participants' experiences 
or to understand the social and cultural context of a research topic.</li>
</ul>
4. Interpretation of data
The fourth step in qualitative research is to interpret and analyze the data. This step involves transcribing and organizing the data, identifying patterns and themes, and developing a theoretical framework to explain the findings.
Data transcription involves converting audio or video data into text. Once the data is transcribed, it can be organized and coded in order to identify patterns and themes. Coding is the process of assigning labels or categories to pieces of data. For example, if you are interviewing participants about their experiences with poverty, you might code the data with labels such as "income," "housing," and "food security."
Once the data has been coded, you can begin to identify patterns and themes. Patterns are regularities in the data, while themes are broader concepts or ideas that emerge from the data. For example, you might identify a pattern of participants describing difficulty affording food and housing, and a theme of poverty being a barrier to opportunity.
Once you have identified patterns and themes, you can begin to develop a theoretical framework to explain the findings. A theoretical framework is a set of concepts and relationships that helps to explain a phenomenon. For example, you might use a theoretical framework such as social class theory to explain how poverty can lead to barriers to opportunity.
5. Conceptual and theoretical work
The fifth step in qualitative research is to develop a conceptual or theoretical framework to explain the findings and situating the research within the broader body of knowledge. This involves:
<ul data-sourcepos="57:1-57:71">
<li data-sourcepos="57:1-57:71">Conceptual framework: A conceptual framework is a set of concepts</li></ul>

Research Methodology Qualitative research process Research Methodology

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, ...

APRCET-UGC-Net/JRF: Paper I Portal

Problems in measurement in research – Validity and Reliability

Characteristics of a good measurement

Levels of measurement – Nominal, Ordinal, Interval, Ratio.

Measurement: Concept of measurement– what is measured?

Merging the two approaches - Qualitative and Quantitative

causality, generalization, replication in qualitative research

Qualitative research process Research Methodology

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