Science method

Quantitative Analysis - Science method

In chemistry, quantitative analysis is the determination of the absolute or relative abundance (often expressed as a concentration) of one, several or all particular substance(s) present in a sample.
Questions related to Quantitative Analysis
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There is a research model with one IV, one DV and one mediator. So, when checking the parametric assumptions in SPSS, what will be done to the mediator?
Does the following answer is true?
Path 1: IV to Mediator
For the path where the IV predicts the mediator, the mediator is treated as the outcome.
  • Run a simple regression with the IV as the predictor and the mediator as the dependent variable. Check parametric assumptions for this regression:
    • Linearity
    • Normality of Residuals
    • Homoscedasticity
    • Independence of Errors
Path 2: Mediator to DV (and IV to DV)
In this step, the mediator serves as a predictor of the DV, alongside the IV.
  • Run a regression with the IV and the mediator as predictors and the DV as the outcome. Check assumptions for this second regression model:
    • Linearity
    • Normality of Residuals
    • Homoscedasticity
    • Independence of Errors
    • Multicollinearity
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This sounds correct to me.
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Hello,
I am working on a dissertation examining the feasibility and acceptability of treatment in a small correctional setting. I've drafted a survey for corrections personnel (they are not study staff) to provide both quantitative and qualitative feedback on our implementation practices (questions include items on acceptability, feasibility, beliefs about mental health care, and job satisfaction). The total number of workers we will be sampling is ~300 and we are not completing an inferential statistics.
Is anyone familiar with how to determine an adequate sample size? Meaning, what % of employees completing my survey would be enough based on past literature/standard methodological principles? My literature review as been all but clear.
Thank you in advance.
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What is the population size? If heterogeneous, consider the different stratification. The goal is to report your sample is representative of the population. In general, most formula require samples much larger than most researchers in practice use when the population is small.
Taherdoost, H. (2017). Determining sample size; how to calculate survey sample size. International Journal of Economics and Management Systems, 2.
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I am looking for resources to help teach a course on Social Research Methods, particularly focusing on Quantitative Analysis. This includes syllabi, lecture presentations/notes, assignments, and reading materials. I am interested in finding resources from reputable sources, such as academic institutions, educational repositories, and professional associations. Any recommendations for comprehensive and high-quality materials would be greatly appreciated.
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Hi Ashfaq,
I have attached four syllabi that are used at US universities. I hope they are helpful.
Best,
Don P.
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An elaboration, I'm currently looking for a scale that measures adolescents' trust in their parents, however, I've had difficulties finding one until I found a research conducted by Pathak et al. 2016. In this research, they were measuring trust in adolescents' towards their parents, however, when I looked at the original research for the trust scale that they used, which is Rempel et al. 1985, the scale in this study was more directed towards partners in a romantic relationship and did not mention any subjects that were parents. The study of Pathak et al. 2016 from my belief changed the "subjects" of Rempel et al. 1985 scale from measuring partner's trust in a intimate relationship to measuring trust in adolescent towards their parents.
TLDR; I'm still new to the research endeavors, Rempel et al. 1985 did a research on "close relationship" and I don't know whether we can generalize this into adolescents' trust in parents (I asked this because Pathak et al. 2016 did what I just asked).
References:
Pathak, Sweta & Sinha, Shubhra & Tiwari, Mithilesh. (2016). Role of Parental Control in Adolescents' Level of Trust & Communication with Parents. Recent advances in Psychology: An International Journal. 3. 129-140.
Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985). Trust in close relationships. Journal of Personality and Social Psychology, 49(1), 95–112. https://doi.org/10.1037/0022-3514.49.1.95
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Here is something overlooked in most research that uses scales: Reliability and validity are always sample specific, setting specific, and time specific. Unfortunately, researchers do not always have the access to an adequate sample size and skills to check every time a scale is reused.
My suggestion: If you have time, pilot the study, especially if a different population to draw from, examine validity and reliability with simple statistics, and if you have the skills/large enough sample—run CFA. Often you can’t run a CFA for many reasons. Another possibility is using another scale with concurrent validity. Regardless, you can reuse the scale, but investigating with a pilot and correlations/reliability can bolster your study.
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i just want to know if there are more options to do a quantitative analysis with xrd data
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Diego Santiago I will add that it is very easy to do the Rietveld refinement. I am using the GSAS, an open-source Python-based software for calculating the phase fraction (wt. fraction). Some of my tutorials may help you do these calculations. One of my such tutorials in which I have refined a sample with two phases is at https://youtu.be/Z_aWz7Tx0Pw. Thanks
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I am trying to perform the quantitative analysis of minerals in my samples. I have the XRD raw data of my samples. I started Rietveld refinement in X'pert Highscore and Match!* software but there was an error showing in these two software.
I have attached the image which shows the error during Rietveld refinement.
Please help me, If there are any other method for quantitative analysis of mineral percentage.
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Kaushal Kishor Agnihotri I am using the GSAS, an open source Python-based software for calculating the phase fraction (wt. fraction). Some of my tutorials may help you do these calculations. One of my tutorial is at https://youtu.be/Z_aWz7Tx0Pw. Thanks
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Dear Colleagues,
I have a question regarding the relationship between sample size and the likelihood of obtaining significant results in SEM. If it is generally true that larger sample sizes can increase statistical power and the chances of detecting significant effects, then what can researchers do to make sure that the hypothesis-testing results are reliable and meaningful?
Let's say my SEM study has a sample size of 5000, does this mean that the p values in the hypotheses I will be testing are very likely to be significant due to the large sample size? Are there any effective measures that we may take to deal with this problem?
I am thinking about the following three steps but am unsure if they are useful in effect:
1) reporting precise effect size,
2) lowering the significance threshold from p < 0.05 to p < 0.001,
3) testing the robustness of the structural model across different subgroups.
Do you have any thoughts or recommendations? Feel free to recommend any literature that you may find useful!
Thank you!
Best,
Leon
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Let's move beyond AI answers. First, the constructs must be qualitatively connected and theoretically appropriate. Secondly, p values are inversely connected to sample size. The larger the sample size, the more likely even trivial differences will meet the predefined p value threshold. One must move beyond statistically significant to practically significant. Third, the more tests you run, such as with subgroups, increases the likelihood of error. Finally, sample sufficiency must be determined related to what variables are included or excluded. Consider p hacking and HARKing.
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Dear all,
I have been trying to quantify phases in a powder mixture including LiMnO2 (orthorhombic, cubic, space group: Pmnm) and Mn2O3. I'm trying to use the Rietveld method, but concerning formation of amorphous phases can affect to the accuracy of quantification. The RIR method can determine the amount of amorphous phases, but I do not have the RIR value of the above LiMnO2 phase. Thus, I mix this mixture with 10 % of alpha-Al2O3 (corundum) and perform the Rietveld method to determine the amount of crystallite phases (eg. the calculated wt%: 11 % Al2O3, 85% LiMnO2, 4% Mn2O3), so the total amount of amorphous phases is 9.09 %. Is this performance true? And can anyone phease recommend me any reference which has the RIR value of the above LiMnO2 phase?
Thanks for your helps.
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The approach you've described seems reasonable in theory. However, the accuracy of the quantification depends on several factors, including the accuracy of the Rietveld refinement, the appropriateness of the internal standard, and the assumption that there are no interactions between phases that could affect the intensity of diffraction peaks. It's essential to validate your results through comparison with other analytical techniques or reference materials, particularly since you don't have the RIR value for LiMnO2.
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My data are non stationary seasonal data. I need to know is there any forecasting models can handle non stationary data. and I also want to know STL( Seasonal Trend LOESS) and ETS can handle non stationary data.
Thank you.
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Not at all. First look at the data plot. If there are level changes, use d = 1, otherwise 0. I suppose you have a seasonality so D = 1. Look at the plot of the residuals. If there are no more level changes and deterministic seasonality, you can move to the next step. Look at the autocorrelation and partial autocorrelation. A cut in the correlogram indicates the need for moving averages (MA). A cut in the partial correlogram indicates the need of autoregressions (AR). By changing p (for regular AR), q (for regular MA), P (for seasonal AR), and Q (for seasonal MA), once at the time, you will have a good model in a few steps. Each time, check the residual correlogram and partial correlogram for pikes above 2 or 2.5 the standard error. Don't be too strict. Indeed, looking at a large number of lags is doing multiple statistical tests. It is quite normal that 5% of the pikes are out of the interval +/- 2 standard errors. Sometimes, especially when d + D <= 1, a constant should be added. It is not frequent to use P > 1 or Q > 1. It is better not to have large p and q together (because there is the risk of common roots in the AR and MA operators). Note that often p = P = 0 and q = Q = 1 will do the job. I can check if I have an example in English but my book is in French.
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Hello everyone !
We're doing research on the impact of store location on purchase behavior of consumers and we're confused on how to approach data collection and analysis in this research.
After going over the literature, we've defined the variables : Proximity, visibility, accessibility, transportation, parking and local competition, alongside socioeconomic and demographic factors like age, revenue etc.
Should we proceed with a qualitative or quantitative analysis ? Both ? And which method for each ?
Currently we're working on quantitative analysis with descriptive analysis and factorial analysis to deduce correlation between variables. Are we on the right path ?
Any help would be much appreciated !
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If you do want to use mixed methods, I suggest that you start with a design that will integrate the two set of results, rather than just reaching separate conclusions (e.g., one from testing hypotheses and the other from generating themes).
It sounds like you already have a substantial set of quantitative hypotheses and variables to measure them, so one likely mixed method approach would be an explanatory sequential design (QUAN --> qual). In this case, the goal of the qualitative follow-up study is to help understand the results form the primary quantitative study.
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Hello everyone,
I am relatively new to the field of fluorescence microscopy and subcellular localization analysis. Recently, I conducted experiments on HEK293 cells wherein I labeled both the nucleus and the protein of interest. Now, I am in the process of interpreting the fluorescence patterns to predict subcellular localization. I have come across literature suggesting that quantitative analysis in this regard is often carried out by specialists. I am curious if there are any established criteria or guidelines for interpreting these patterns to identify specific organelles.
I would greatly appreciate any advice or insights you can provide on this matter. Thank you very much in advance.
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It would be useful if you provided a picture and explained how you labelled the nucleus and protein of interest and what sort of microscope you used to take the image etc. Qualitative analysis of protein localisation is fairly easy but quantitative analysis depends so much on how your sample is prepared for imaging and how you take your images. A lot depends on the question you want to answer as well. For example you can get numbers from software such as ImageJ (FIJI) (which is free) but you need to know what to measure.
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I am doing research on analyzing what factors affect lumpsum contracts. qualitative analysis using risk breakdown structure and quantitative analysis using analytical hierarcy process. i want to know what steps i need to do.
please help in completing this research, thank you
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To write a multiple-case study, a summary of individual cases should be reported, and researchers need to draw cross-case conclusions and form a cross-case report. With evidence from multiple cases, researchers may have generalizable findings and develop theories.
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What's the relationship between monosaccharides and polysacharides, and does the quantitative analysis of monosaccharides tell us something about polysaccharides?
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Plants produce monosaccharides by photosynthesis. Subsequently, they convert them into polysaccharides either for obtaining structural material (e. g. cellulose) or energy reserves (e. g. starch). There are also events when plants hydrolyse polysaccharides back into oligo- or monosaccharides (e. g. riping of fruits). Therefore, no constant ratio of mono- to polysaccharides exists - especially not over all plant species. You'll have to quantify both fractions, if you need this information. You can do this hydrolysing the plant sample and compare it with an unhydrolysed plant extract.
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Is ex ante power analysis the same as a priori power analysis or is it something different in the domain of SEM and multiple regression analysis? If it is different, then what are the recommended methods or procedures? Any citations for it?
Thank you for precious time and help!
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Zubaida Abdul Sattar Thanks a lot for sharing detailed information.
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I recently conducted a liquid chromatography-mass spectrometry (LC-MS) analysis of my protein sample, which resulted in the identification of over 300 proteins. I need assistance in identifying any novel proteins within this dataset. Can someone guide me through the necessary steps and offer insights on how to interpret the results?
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Contact me
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I am working with NOVATECH ELISA KIT for Brucella. In the leaflet, it is mentioned that it is a qualitative assay with a cutoff starting at 11. When I enrolled the assay through the DS2 automatic ELISA machine, the layout results showed values as numbers, not only positive and negative. What does it mean? Are those numbers the real titre value? Are they the real concentrations of measured antibodies? If yes, why has the company not highlighted the kit as a quantitative ELISA?
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ELISA may be run in a qualitative or quantitative format.
Qualitative results provide a simple positive or negative result (yes or no) for a sample. The cut-off between positive and negative is determined by the analyst and may be statistical.
For diagnostic tests that produce a numerical result, the point above which test results are classified as positive is called the cut-off. These are usually established by testing a large number of infected and non-infected animals and selecting the value that maximizes the sensitivity and specificity of the test. For diagnostic kits the cut-off is established by the kit manufacturer. You may want to refer to the article attached below.
In quantitative ELISA, the optical density of the sample is compared to a standard curve, which is typically a serial dilution of a known concentration solution of the target molecule. For instance, if a test sample shows an absorbance of 1.0, the point on the standard curve that gives an OD = 1.0 will be of the same analyte concentration as the sample.
So in this case, the samples showing values 11 and above are considered positive.
Best.
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In the context of the "Randomized Posttest-Only Control Group Design with Matched Subjects," a question arises regarding the appropriate statistical method for comparing the experimental and control groups. Should we utilize the paired samples t-test or the independent samples t-test, considering the matching process? The matching process aims to make the groups comparable, but does it imply treating them as if measurements were taken from the same individuals? Should we view them as distinct groups, despite the matching, and opt for the independent samples t-test, or is it more appropriate to employ the paired samples t-test, acknowledging the considerations of matched subjects?
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A paired analysis is not restricted to data from the same subjects. The method is to eliminate common sources of (additive) variance from statistically non-independent measurements. It does not matter what the reason for the non-independence is.
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Scenario - There is an IV and DV. IV is measured in 5 point likert scale questions and DV is measured in 7 point likert scale questions.
Doubts -
01. Can we run a test like regression analysis directly irrespective of differences in measures?
02. if NOT, what are the transformation techniques available to transform data into same scale?
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If you have only one item for each of your measures, then you have two ordinal measures and you should not use a procedure such as regression that assumes you have interval-level data. However, if you actually have multi-item measures that you use to form a scale, then you can create interval-variables and use correlation and regression.
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The choice to employ snowball sampling in this study is driven by the unique and sensitive nature of studying smoking habits. It is challenging to identify and explore through conventional sampling methods. Snowball sampling, by relying on existing social networks and participant referrals, provides a valuable approach to reach individuals engaged in smoking within this particular context. Is there any way to change it to probability sampling technique?
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I think "useless" is too strong a word, but your statistical analyses would have to be regarded as "provisional" or "exploratory" rather than actual probabilities.
What could matter most is what previous research has done in this area. In particular, if there is widespread agreement that random sampling is impractical for this population, then you will be providing "useful" information even if it does have limitations.
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Which is the best book for understanding Social Sciences Statistical analysis tools?
Hello Friends!
I have been in search of best book for understanding and applying social sciences statistical analysis tools. I am new in this field please seniors recommend some best books on the topic.
Thanks
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Research made simple by "Dr. Patrick Ndalama".
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How can we perform a quantitative analysis of the XPS peaks using Origin software? Please help, How can we do a quantitative analysis of each peak? The peaks of C1s are attached below.
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Well, yes, your peaks are not correct. Dr. Morgan and I agree on this.
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Can XRD-Rietveld analysis provide components / phases present in the amorphous phase the same way it does for crystalline material? Are there other methods available?
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the evaluation of XRD pattern of amorphous material is is a challenge because it mainly consist of a large broad hump which some times is more or less structured by one or two shoulders. From that perspective you already see, that there is much less information available compared to a 'crystalline' XRD pattern.
In order to compensate the lack of information a bit one uses the small angle scattering technique, the experimental set up of which is in principle similar to an XRD one. The difference is the 'theta' or '2theta' range combined with the wavelength in use. Here the physical parameter known as the magnitude of the momentum transfer vector show up.
Details about the evaluation of such 'amorphous' pattern you will find for example in:
Tutorial on pairdistribution function:
I admit, that it a hard job to dive deeper into this subject...
Good luck and
best regards
G.M.
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The complex interplay between the human brain, mind, and consciousness bears a deep connection to the domains of physical science and mathematics. This connection illuminates how these fundamental aspects of human existence find common ground with empirical investigation and quantitative analysis. Exploring the multifaceted relationship between these aspects of human knowledge and the exacting disciplines of physical science and mathematics, and the relationship between mind and time.
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Furthermore,
As I thank you for your thoughtful response. I respect your perspective on the relationship between consciousness and fundamental concepts, but I think it's important to consider the role of biological sciences, psychology, neurological sciences, and the interplay with physical science and mathematics in this complex discussion. While your "Information as Absolute" framework is intriguing, it is essential to ensure that it accommodates a multidisciplinary approach.
  1. Fundamental Understanding: I appreciate your emphasis on the need for a fundamental understanding of key notions, including "Matter," "Consciousness," "Space," "Time," "Energy," and "Information." However, I think that a comprehensive understanding should encompass insights from both your framework and established disciplines like psychology, biology, and physics.
  2. Consciousness and the Brain: Your assertion that consciousness operates predominantly outside matter and matter's space while using the brain for power supply and service functions raises intriguing questions. Nevertheless, it's crucial to acknowledge the extensive research and insights from neuroscience and psychology, which offer valuable perspectives on the connection between consciousness and the brain.
  3. Information Processing: While you suggest that most information processing in human consciousness occurs on subconscious levels, I think it's important to recognize that sensory stimuli and cognitive processes are crucial in forming our conscious experiences. Biological sciences, particularly psychology, provide valuable insights into this aspect.
  4. Physics and Mathematics: Physics and mathematics have traditionally played a significant role in understanding the physical world, including space, time, and energy. These disciplines offer valuable tools for quantifying and analyzing various phenomena. Integrating these disciplines with your philosophical framework could lead to a more holistic perspective.
In conclusion, I think a balanced approach that incorporates insights from biological sciences, psychology, and physics alongside your "Information as Absolute" framework can offer a more comprehensive understanding of the relationship between human consciousness and fundamental concepts. This multidisciplinary approach allows us to explore the intricate connections between the mind and the physical world.
Best regards,
Soumendra Nath Thakur
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Hi, we are researching a Comparative Analysis between Personality Traits among Career Interests and Self- determination. We've been trying so hard to find a related literature for personality traits and self-determination. Unfortunately, we still can't find one and we need it. I hope that you can help us thank you so much
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Hi,
You might try Personality traits theory, testing and influences by
Melissa E Jordan or Personality: in search of individuality by Nathan Brody
A good article by Prentice et al., 'Integrating whole trait theory and self‐determination theory," published by Wiley, might also help.
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Hi, I did a quantitative analysis for my transgenic and control plants. The statistical analysis (p<0.05, One Way ANOVA, Turkey) showed that there are no significant differences between both plants. May I know how to indicate that data on the graph? Should I use the same alphabet on both plants to indicate it? Or there are other ways to indicate it?
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Shakeel Ahmad May I know how to confirm mutagenicity?
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"Breaking the Issue: Quantitative Analysis of Public Perception and Reaction to Cancel Culture Incidents on Twitter"
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1. to explore the behavioural influential factors of public perception towards reaction on cancelling cultural incidents in the Twitter platform.
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Dear Colleagues
How are you using ChatGPT and other generative AI in your research? Can it be used for quantitative analysis, to interpret quantitative results, sharpen arguments, or find citations. I know with citations it often 'hallucinates', or makes stuff up. Let me know how it is aiding your research.
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I am using ChatGPT to analyze my qualitative data. My goal is to find the key themes in the data without the time-consuming and tedious process of coding.
As for hallucinations, I have not encountered this problem, but there is no substitute for being familiar with your data and your field of research.
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If you were considering research in a developing import-dependent African country, what would be those contemporary areas of interest that hold both practical and theoretical contributions??? Your opinion is highly appreciated.
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marketing sensing, aligning organizational strategies, considering digital transformation and industry 4.0 perspectives considering customer 4.0, cultural orientation culture 4.0.
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Quantitative Analysis for PFAS Compound
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Yes, it is possible to assess Per- and Polyfluoroalkyl Substances (PFAS) using an Agilent 6420 LC/MS/MS system without the Jet Stream (AJS) ion source. The Agilent 6420 LC/MS/MS system is equipped with an Electrospray Ionization (ESI) source, which is commonly used for analyzing a wide range of compounds, including PFAS.
While the Jet Stream (AJS) technology can enhance sensitivity and performance, it is not strictly required for PFAS analysis. ESI is a widely used ionization technique that generates ions from a solution through the application of an electric field. It produces ions that are suitable for many compounds, including PFAS, without the need for Jet Stream technology.
Here are the steps to perform PFAS analysis on an Agilent 6420 LC/MS/MS system without the Jet Stream ion source:
  1. Method Development:Develop a suitable LC/MS/MS method for PFAS analysis. This involves optimizing the chromatographic conditions, mobile phase composition, and gradient profile.
  2. Sample Preparation:Prepare your PFAS samples according to established protocols. Sample preparation methods may include solid-phase extraction (SPE), liquid-liquid extraction (LLE), or other extraction techniques depending on the sample matrix.
  3. Instrument Setup:Set up your Agilent 6420 LC/MS/MS system without using the Jet Stream ion source. Ensure that the system is properly calibrated and configured for the chosen ionization mode (typically negative ion mode for PFAS analysis).
  4. Ionization Mode:PFAS are typically analyzed in negative ion mode due to their negative charge characteristics. This generates negatively charged ions that are suitable for ESI analysis.
  5. Tune Parameters:Optimize the ESI parameters such as capillary voltage, nebulizer gas flow, drying gas flow, and temperature to achieve the best ionization efficiency and sensitivity for your PFAS compounds.
  6. Calibration and Quality Control Samples:Prepare calibration standards and quality control samples containing known concentrations of your target PFAS compounds. These will be used to establish the calibration curve and assess the accuracy and precision of your analysis.
  7. Data Acquisition and Analysis:Inject your prepared samples into the LC/MS/MS system and acquire data using the optimized method. Process the data using appropriate software to quantify the PFAS compounds based on the calibration curve.
While Jet Stream technology can enhance sensitivity, it's not mandatory for PFAS analysis on the Agilent 6420 LC/MS/MS system. With proper method development, sample preparation, and optimization of ESI parameters, you can successfully analyze PFAS using the standard ESI ion source available on the instrument. Anika Nawar
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Attention Scholars,
I, an assistant professor and researcher in Finance and FinTech, extend a warm invitation for collaborative research projects. My expertise lies in conducting quantitative analysis using SmartPLS, a powerful tool for modeling and analyzing complex data relationships.
If you are working on groundbreaking research in Finance, FinTech, or related Administrative and Financial Sciences and wish to explore the realms of quantitative analysis, I welcome the opportunity to collaborate as a co-author. Together, we can produce impactful research that contributes to our fields.
Please check my Google Scholars and Scopus
Mousa Ajouz (Ph.D)
Palestine Ahliya University
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I am open for research collaboration
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Laptop / pc recommendations for efficient writing, endnote, spss, data storage?
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Processor: intel core 5 or Ryzen 5 or higher
RAM: 8 GB or more
Storage: 256/512 SSD and 1 or 2 TB HDD
SSD can be used for loading OS and programmes. HDD is the safer option for data storage.
Screen size: 14" or 15.6"
14" is more compact, but 15.6 provides enough screen space. Choose the size according to your convenience.
You can buy an additional screen (22" or higher) to connect externally and keep it in your office or home. That will be a great option when you are working with huge data, charts etc.
New laptops have enough features, including USB ports, card reader, backlit keyboard etc.
Graphics cards: A 2GB graphics card would be an additional option, and it will definitely help to handle graphics data if you need to work with 3D graphics and videos. The more 'GB' (4GB, 6 GB), the better the graphics performance. But the size of the laptop will be increased. If you need a slim laptop with greater graphics, you may need to pay a higher amount.
I think for your need, 2 GB graphics would be enough.
I recommend buying an external HDD (not SSD) for data backup. I think 2 TB would be enough. (WD or Seagate are good, ensure password protection feature)
The laptop brand is a personal choice.
If you are comfortable with Apple, go for Macbook. But in my personal experience, the device is so limiting yet efficient!!
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I have already revised some of the data streams (WDI, WID or world income inequality, Unctadstat, Ford) where quite a large number of data (yearwise) are missing. How to recover the data? Can I use data cleaning or other methods when many years of data are missing? Or, is there national data streams such as Department of Statistics which can provide the missing ones?
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The best way to find data is to dig into the national bureaus of a country regarding any variable. You can use VPN and institutional email if they are not providing access.
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Do you believe that your understanding of qualitative or quantitative analysis brings you closer to the truth, given how you define truth? Please explain!
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As a pragmatist, I have wholly agnostic view toward issues of "truth." Instead, I would emphasize the classic research standard of a contribution to knowledge. So, if you have a meaningful research question and an appropriate way to answer it, then it doesn't matter whether you are doing qualitative or quantitative research.
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A researcher has deployed a qualitative approach in the data collection phase of research but has later decided to spice the analysis with some aspect of quantitative analysis. What can such mixing be called? Literature references will be appreciated.
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You do not say anything about where the quantitative data came from. Is it a quantitative analysis of the qualitative data? If so, this called quantizing (or quantizing) the qualitative data.
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Greetings.
Up until now didn't need to quantify minerals, a qualitative analysis was enough, but I wonder if needed what are the best and more feasible/acessible methods to quantify the percentage(even approximately) of different phases in a mineral precipitate?
I know there is one method that if we know that one ion is only present in a mineral in our sample, ththenan we may infer how much of that mineral is present, but is still based on suppositions so I wanted to avoid it and doesn't give a full picture.
The other one I know of is the Rietveld method using X-Ray Diffraction that allows quantification. But I guess if the sample has amorphous materials it leaves those minerals out right? and is it common to be able to perform this method in any XRD devices or only the most recent ones?
What other methods are there that maybe of use, that aren't excessively expensive or hard to find where to perform them? Even if it mainly gives us a distribution of the percentage of each of the known phases.
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Generally, qualitative and quantitative determination of mineral phases is performed by using X-ray diffraction (XRD) methods.
Even amorphous content can be quantified using an internal or external standard. Another variant to deal with even multiple semi-amorphous phases is the so called PONKCS method.
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What does it mean when quantitative analysis of the compositional elements (from EDS analysis) gives the weight percentage and Atomic percentage of the elements with a negative sign, for example( -1.05) while the existence of this element from XRD analysis and the elemental mapping images?
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Negative sign means absence of element (usually because of poor understanding of the method by operator). And "XRD analysis and the elemental mapping" usually cannot prove presence of an element. Use spectra to make a qualitative analysis before conducting a quantitative one.
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Have you worked with standard scales/measures/instruments and have modified them in any way?
What modifications are acceptable to standard scales?
What are the steps to be taken in order to ensure these modifications:
  • dropping of scale item(s)
  • Changing the likert scale for responses: adding anchors, changing what the anchors read, etc.
  • splitting double barrelled items into two
  • Changing the order of the items.
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There are several guidelines, including:
• Using the least burdensome approach when developing, modifying, or adapting instruments.
• Obtaining permission from the developer of the scale before modifying it.
• Ensuring that the modifications do not affect the validity or reliability of the instrument.
• Conducting content and construct validation to ensure that the modified instrument is still measuring what it intends to measure.
• Following established standards and measurements to ensure the modified instrument is accurate and reliable.
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I am investigating the effect of environment on gca, sca and heritability degree. Line x tester = 4 x 5.
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There are several software options available for conducting line x tester analysis that can handle multiple environments, including drought and control treatments. Here are a few options:
  1. SAS (Statistical Analysis System): SAS is a powerful statistical software suite that is widely used in the field of agriculture and plant breeding. SAS can handle complex experimental designs, including line x tester analysis with multiple environments.
  2. R software: R is a free and open-source programming language that is widely used in statistical analysis and data science. There are several packages available in R for conducting line x tester analysis, including the "AGHmatrix" and "lineXtester" packages.
  3. GenStat: GenStat is a comprehensive statistical software package that is specifically designed for use in agricultural research. It includes a range of tools for analyzing complex experimental designs, including line x tester analysis.
  4. SPSS: SPSS is a statistical software package widely used in social science and other fields. It also has the capability to conduct line x tester analysis with multiple environments.
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I have ten regions and I created dummy variables for them. When I run my model one of them shows omitted, so I had to exclude the one showing omitted,and then I got significant result. But I need "the coefficient value of the excluded variable" to estimate Total Factor Productivity.
If possible, could you please explain how I can calculate it,dear colleagues.
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See my answer to the same question that you asked at
it is about dummy variables that form a closed set.
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Dear all,
I'm currently looking on the expression of specific isoforms from multiple candidate genes.
In order to confirm the expression of these isoforms, my first reaction was to prepare PCR primers enabling to amplify these isoforms either specifically or in duo (two primers for two amplifications of different sizes) and see if the expression of these isoforms changes between my experimental conditions.
However, this method will require further analysis by qPCR and sequencing to confirm the identity of these amplifications and to quantify their changes, which will take a large amount of time.
I was wondering if any faster method could exist to quantitatively analyse specific mRNA isoforms expression. The goal would be to not use omics strategy (as the candidate genes were identified this way) but to use a more targeted and precise approach to look at these specific genes of interest.
Thank you very much for your help.
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Actually, RT-qPCR and RNA-sequencing are the gold standards. Alternatively, Exome Microarray can be used, which is faster but much more expensive and suits more for large-scale studies.
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I have some complex physiological data that varies quite a bit across participants. Best way to capture this variability? For more background...participants were exposed to different kinds of prejudice and some participants varied as to what type they responded more to. Any thoughts would be helpful.
Thanks!
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' Partial' correlation istead of correlation controls the confounding effect due to the variabe exposure of prejudices.
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*UPDATE*
Hello everyone!
I'm looking for papers that demonstrate/show how to quantitatively measure knowledge management (in general or part of it, like KM processes). In particular, I need to know which elements of a financial statement/balance sheet can be considered to offer an "objective measure" of KM within a company.
I really appreciate any help you can provide!
Please, note that I would like to study the relationship between servitization and KM. Given that I will measure servitization through panel data taken from companies' financial statements/balance sheets, I would like to find - in the financial statements/balance sheets - data that can allow me to measure the KM (or the CI, intended as a proxy for the KM). Do you have any suggestions according to this specific purpose? I was looking at the VAIC as well as the modified VAIC, but not sure it can be the best approach...
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Modifying Accounting Data For Managerial Decision
1 Oprating asset and total oprating capital
2 Net operating profit after taxes (NOPAT)
from pages 102 through 105.
Source:
Financial Management: Theory and Practice
Eugene F. Brigham and Michael C. Ehrhardt 2005.
I hope this helps you.
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I want to use NaBH4 to reduce some aldehyde compounds into alcohols.
If I directly inject the solution into GC, will it cause the column contamination?
If so, is there any better method to conduct quantitative analysis?
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If you use an injector liner designed to capture non-volatile components, such as a glass wool packed injector, the salts will be capture in the liner. The liner will need to be replaced more often but this is not a significant issue. Depending upon the quantity of salt injected, there may or may not be a significant difference in the rate of replacement. Peak widths and peak tailing would likely be indicators of the need to change the liners.
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Hi everyone,
As part of my PhD I'm validating a patient-reported disease severity scale for patients with a rare condition. It assesses the severity across 5 symptom groups using a 0-5 likert rating. It's been adapted from a previously validated clinician-reported version to form a lay-reported version so that patients can report their own disease severity. The symptom groups are the same but the ways in which the response options are worded are different between the two questionnaires, which means this version needs validating. Initial testing done on the questionnaire suggests the isn't much differentiation across the response options on most of the items. I was thinking about interviewing patients, amending the questionnaire and then running some quantitative analyses to validate the scale.
I'm looking at using IRT, as the scale is not to be utilized in clinical settings, as there is already a validated clinician-reported tool to measure disease severity in the population. However the main problem I face is the patient population is incredibly small and I'm unlikely to get more than say 100 participants, all the stuff I'm reading on scale development says I need a lot more data otherwise the analysis won't have sufficient power.
Has anyone got any experience validating questionnaires using small sample sizes or has any advice regarding different validation strategies?
Many thanks!
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Before testing the content domain, I will strongly suggest reading the following paper. If conceptualization is based on well-developed measurement theory, then a small sample size is sufficient for your scale validation:
(20) (PDF) Specifying the Problem of Measurement Models Misspecification in Management Sciences Literature (researchgate.net)
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Qualitative data indicates interviews, open-ended questions etc
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Analysis tool NVIVO is recommended...thematic analysis or IPA is also used for qualitative data analysis
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Hello guys,
I read some nice articles explain the MSDO-MDSO for reducing number of conditions used within qualitative-comparative analysis. However, all of them lack the explanation for interpretation of tables created at the fourth phase of analysis - identification of relevant causal conditions - Outstanding pairs
The results were generated through online MSDO-MDSO software: version 1.1 - spring 2015 (jchr.be), available at the website https://compasss.org/software/
Thank you in advance!
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Dear Andrei,
if you think you have too many variables for a reasonable application of Qualitative Comparative Analysis (QCA), you can use a much more direct approach to their selection straight from within QCA. In
Lankoski, Jussi, and Alrik Thiem. 2020. "Linkages between agricultural policies, productivity and environmental sustainability." Ecological Economics 178:106809
and
Haesebrouck, Tim, and Alrik Thiem. 2018. "Burden Sharing in CSDP Military Operations." Defence and Peace Economics 29 (7):748-65
I demonstrate the logic and application of a feasible variable selection procedure for QCA that it methodologically grounded (Occam's razor) and guaranteed to be computationally exhaustive. Since replication code for the R environment is available for both articles, you can easily adapt this procedure to your needs.
Best wishes
Alrik
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I have panel data (T=10, N=26) where all variables are integrated I(1) with cross-section dependence. I applied Westerlund test and found no cointegration. So I proceeded with Pvar (Panel var) estimation. However, I want to confirm the robustness of my analysis by applying another estimation technique. Any advice?
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You can try AMG and CCMG for robustness.
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I have bought a compound from macrocylics. I have analysed it using LC-MS where it shows multiple peaks. I have selected the peaks and did an area integration. The quantitative analysis of TIC gave be 85% purity whereas the product is endorsed as 95% pure.
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Your real question..... Additionally, you appear to be making an assumption that your results should equal the results obtained by the supplier for your sample ( a reasonable starting assumption, but the real question is far more complicated). There are thousands of variables present in any LC-MS analysis. Any "purity" values assigned to a compound are relative to the EXACT method of analysis used, on that system. With LC-MS (or MS, MS-MS) analysis, the instrument operator, the settings (and their are a LOT of settings), the HPLC method and the specific instrument used all contribute to the results obtain. Just as with your method, the original method used to determine "purity" may or may not be comprehensive (we have no idea). All methods need to be evaluated by professionals to insure they are fit-for-purpose and selective for the sample under analysis before any conclusions are drawn.
Any MS system can output data that is or is not accurate. The system is just a "dumb" tool and to obtain quality output requires a very skilled user with a great deal of practical experience.
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I am attempting to perform XRD analysis on the 21R Sialon polytype, by seeing how much aluminium nitride is within each sample. Although, I cannot perform Rietveld refinements on this as I have no XRD data for pure 21R. I have been unable to find any data through research.
What data do I need to perform this task, and is it possible to perform Rietveld calculations if it does not exist within my Match database.
I am using Match! to attempt to perform these calculations.
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Yes I do, although onsite we do not have access too MS or XRF equipment, just XRD analysis. Appreciate the help!
Regards,
Luke
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If we use SmartPLS to analyse the structural equation modeling (SEM) then what could be the appropriate sample size? Is there any minimum and maximum sample size is required to analyse the PLS-SEM?
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The synthesized hydroxyapatite powder can be doped with tricalcium phosphate. Can thermogravimetric analysis (TGA) be used for quantitative analysis of tricalcium phosphate in hydroxyapatite powders?
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Tru Nhut Quynh What exactly are you trying to quantify (e.g. percent TCP in the composite, change in mass of TCP vs HAp in composite with decomposition)? I don’t TGA will not offer much information if you are looking for specific information about the TCP in the composite, if you are only getting TGA on the pure powder mixture. If anything, I’d recommend doing the TGA of the powders separately and then together at whatever concentration you are aiming for. However, this data can easily be found in the literature as the thermal stability/decomposition properties of these materials are well documented in the research. It is your composite material that would be more unique.
There is a number of other methods you can use, such as XRD, in combination with the TGA that would provide a more comprehensive characterization/quantitative analysis of uour material.
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Hello,
I have a set of items that would need to be slightly adapted to fit my research.
1) Let's assume I have the following item: "Introduce a new generation of products/services."
Is it possible to change the tense to: "introducED a new generation of products/services"?
2) Let's assume I have the following item: "We introduce a new generation of products/services."
Is it possible to change the personal pronoun from we to I: "I introduce a new generation of products/services."?
Are these two changes possible without any further testing?
David
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Questionnaire adaption to suit research context is common.
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If I use 320 sample size using a purposive sampling technique, how can validate the sample size for generalizing results? Are 320 responses could be statistically sufficient to generalize the results?
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All due respect to you for this question
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To define quantitative analysis as such in a mixed methods approach, is it necessary to include a regression analysis?
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I agree with Muhammad Tanveer Afzal , i.e. you conduct apt statistically analysis for each approach, and then integrate the results. So you may or may not conduct regression analysis for your quantitative data, which is dependent on your research questions or hypotheses.
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As we know, atomic and molecular emission lines of laser-induced breakdown spectra can be used for quantitative analysis, classification, etc. Does continuous radiation, which is usually subtracted in quantitative analysis, contain any useful physical information?Are there any applications for continuous radiation in LIBS?
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Plasma emission occurs due to three types of transition in plasma that are free free, Free bound and bound bound transition. All the transitions provides various emission out of which bound bound transition provides line emission and others are continuum emission. All these processes are manly dependent on the plasma density and its temperature. Therefore, a continuum emission also can provide information about plasma density and Temperature.
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Im, a beginner at Raman analysis and anyone can help me to identify the E1 2g, A 1g,C and D peaks in the Raman spectrum of MoS2 and MoS2/rGO. and what is the importance of identifying them for a qualitative or quantitative analysis?
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Pasangi Premadasa The G peak is caused by the bond stretching of all sp2 atom pairs in both rings and chains. The D peak is caused by sp2 atoms in rings breathing.
In most cases, Raman spectra are plotted with the intensity, or "Count Rate," on the y-axis and the frequency of the "Raman Shift" along the x-axis. The difference in frequency between the laser light and the dispersed light is referred to as the Raman shift. This distinction is stated as wave numbers and has nothing to do with the wavelength of the laser.
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I have seen papers where PSM has been performed using cross sectional and panel data. I want to know if PSM can be used for time series data too.
I also have a question that which quantitative method should one use for analysing the impact of a policy intervention. The dataset is time series in nature.
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A way to deal with this problem is finding the trends before and after by fitting to a linear, quadratic, ... curves as well. However it is enough to confirm existence of change point using a test like Mann-Kendall.
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Could you please elaborate on the specific differences between scale development and index development (based on formative measurement) in the context of management research? Is it essential to use only the pre-defined or pre-tested scales to develop an index, such as brand equity index, brand relationship quality index? Suggest some relevant references.
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Kishalay Adhikari, you might find some useful information in Chapter 12 of the following book:
Hair, J. F., Babin, B. J., Anderson, R. E., & Black, W. C. (2019). Multivariate data analysis (8th ed.). Cengage.
I think that some of this chapter could have been written a bit more effectively, but overall it is helpful in drawing distinctions between scales and indexes.
All the best with your research.
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I'm doing a structured observation tallying how many times a teachers does a certain practice in 10 minute intervals. How would you analyse this data?
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You have count data which can be modelled as a log NBD regression. There are book length treatments by Hilbe.
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Usually, mediators and moderators are tested in quantitative studies. However, can we test them in a qualitative study such as a case study?
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The basic answer is that if you test anything, it would not be a qualitative study, but a mixed methods study. Testings as it is understood in quantitative analyses does not exist in quantitative studies. Potential mediators and moderators could be examined using qualitative methods. I hope this is helpful!
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I have searched this question myself but I am still confuse about it. In one article, I read that such quantitative analysis of ethylene glycol was done via HPLC. I am using ethylene glycol as carbon source to grow a bacterial strain. Now I wish to do spectrophotometry to measure its quantity in culture media at different intervals but I am not sure whether it is doable and what wavelength should be selected. I really need guidance on it from relevant expert.
Thanks in anticipation!
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Ethylene glycol can not be observed by UV analysis using HPLC. It must be derivatized to detect using UV. To measure it, you will need to resolve it away from all of the other compounds present in your sample so UV/VIS spectrophotometry is not select enough. Depending on what types of compounds are presnet in the mix, sample prep procedures will need to worked out, then methods using techniques such as HPLC with derivatization or GC may be used.
When researching basic questions, skip these web forums. Instead, try perform a simple keyword search on the web (via Google, Bing etc...) to find additional info on the topic and/or question. You can easily answer your own question(s) by looking up the structure of the compound, its UV absorbance, examples of papers describing analysis of the same compound in mixtures using different analytical techniques etc. Learning HOW to research a question is one of the most important skills you can learn.
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I have six kinds of compounds which I then tested for antioxidant activity using the DDPH assay and also anticancer activity on five types of cell lines, so I got two types of data groups:
1. Antioxidant activity data
2. Anticancer activity (5 types of cancer cell line)
Each data consisted of 3 replications. Which correlation test is the most appropriate to determine whether there is a relationship between the two activities?
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Just do logistic regression is what I had in mind. The DV might be antcancer activity (yes /no) same for antioxidant activity. Best wishes David Booth
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Hello Seniors I hope you are doing well
Recently I've read some very good research articles. In those articles datasets were taken from V-Dem, Polity and Freedom House. Though they have shared the link of supplementary datasets and the process of how they analyzed these datasets in SPSS or R in brief but I couldn't understand and replicate these findings. It may be because I am not very good at quantitative data analysis.
So I want to know how could I better understand this Datasets analysis easily like V-Dem etc. Is there any good course online, lectures or conference video etc. Or good book?
Article links
Any help would be appreciated.
Thanks in anticipation.
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Please find some online course for learning R on Edx and Coursera platforms.
Thanks ~PB
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Some of parameters such as LT50 & LD50 needs to evaluate insecticide.
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Use
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Dear all,
I am using the Imodpoly algorithm with python to fit very noisy fluorescence data. However, in a few instances, I notice that changing the polynomial degree or using arpls algorithm will fit my data better. If I am running many data sets and my goal is to perform quantitative analysis and comparison, do I have to use the same fitting algorithm for each data set or can I mix and match algorithms for better fitting?
Thanks
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Polynomials fit better with everything, but they don't explain anything.
What is the expected relationship between your variables?
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I plan to develop a semi-structured assessment tool and further validate it on a relatively small sample of below 50 (clinical sample). I have been asked by the research committee to consider factor analysis.
So in this context, I wanted to know if anyone has used regularized factor analysis for tool validation which is recommended for small sample sizes?
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The sample size is relatively quite small but if the size is above 100 then you can try. There had been studies who have opted for exploratory factor analysis on such smaller sample. But you got to check the KMO and Bartlett's Test of Sphericity to see the adequacy of your data. Try reading the following research papers who support smaller samples for EFA.
De Winter, J.C.F., Dodou, D., & Wieringa, P.A. (2009). Exploratory factor analysis with small sample sizes. Multivariate Behavioral Research, 44, 147–181.
Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: current approaches and future directions. Psychological Methods, 12, 58-79.
Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815-824.
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Hello everyone! Currently I'm busy with finalizing my master's thesis and due to a high drop-out rate in my intervention I was not able to conduct the initial analysis to test one of my hypotheses. Instead of doing a quantitative analysis, I have analyzed the answers to the evaluation questions after each part of the intervention. The purpose of the evaluation questions was only to evaluate how the participant perceived the intervention and not specifically related to the central construct I am examining in my paper (Psychological Flexibility), whereas the initial quantitative analysis would test whether the scores on the Psy-flex (measure for Psychological Flexibility) would improve after the intervention (compared to the first measurement).
Since I modified the analysis for this part, I had the following questions:
1. Can I still formulate the initial hypothesis in the introduction and write down in the data-analysis that a qualitative analysis is conducted due to small sample size?
- My supervisor says this is not possible and that I should formulate a hypothesis for the qualitative analysis in the introduction (while in this case it is exploratory right?). According to her I should exclude this initial hypothesis from the paper, although this was part of the initial plan.
2. Is the qualitative part not based on a exploratory research design and am I therefore not obliged to formulate a hypothesis?
- The purpose of the evaluation questions were to evaluate the part of the intervention. I did not construct specific questions for the specific skills of Psychological Flexibility (as in an interview with themes & coding etc.). According to my supervisor there should be specific hypothesis for it formulated in the introduction, since I can't say that the study is based on a mixed-method design otherwise (is this true? As long as I report which analyses I conduct in the data-analysis even when modified, I can still say that it is based on a mixed-method design right?) IMPORTANT NOTE: I already did a quantitative analysis before the intervention procedure, so therefore I thought that the combination of quantitative and qualitative design can be seen as mixed-method design.
I hope this explanation is clear for you to give me some advice on how to approach this. If not, ask me some questions and I will try to elaborate on it.
Thank you in advance!
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Good discussion question and explanation. Thank you for sharing.
All the best,
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I read some articles about statistical robustness of SmartPLS. However, I am not sure about the appropriateness of SmartPLS in the case of survey study involving a representative sample with adequate sample size. Any suggestions?
Thank you!
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It's my understanding that PLS works better with smaller samples. But, let's hear it from the experts.
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If I use SmartPLS to test the structural model then how I can measure the Goodness of Fit Index (GFI). What are the indices I need to observe for validating the research model?
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I was given a role play as a financial analyst and the task is to perform a presentation on how to estimate the growth rate of a company by doing quantitative analysis using the company's financial statements.
Hence, which variables from the financial statement should I use to be able to estimate and calculate the projected growth rate?
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Dear Anh H, I agree with Fatemeh Khozaei. To estimate the company growth rate, the income statement is the most essential variable, which with other variables should include Gross profit and Gross sales of the company.
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I need an all in one software that could handle quantitative analysis aside R and also easy to operate
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Hi
In my experience, XLSTAT is a good option, as it installs within MS Excel so the data can be directly analysed within the excel file. Plenty of tutorials are available, GUI based, quite versatile and easy to learn. Student pricing is also available. I used XLSTAT in my PhD.
Hope it helps
Sudhir
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There is a problem in my research with quantitative analysis of XRD patterns of glass-crystalline materials (including glass-ceramics and geopolymers).
Thanks to the discussion (https://www.researchgate.net/post/Does-anyone-know-how-to-quantify-C-S-H-in-cementitious-materials-using-XRD) I've found RieCalc program which calculate of rescaled phase fractions (including amorphous phases).
Unfortunately, I've faced two difficulties:
1. This program "could not be found" at http://www.geoscienze.unipd.it.
2. I'm not sure about its suitability for the analysis of geopolymers and glass-based materials.
Are there any other options to find a program for automatic quantitative analysis of crystalline and amorphous phases, and where can I find them?
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Dear Boris,
ceramics like grès porcelain after cooking sintering so they have high Temperature crystalline minerals and very little amount of glass. You can use XRF analysis to check these minerals like mullite, Cristobalite and other feldspar just to say the most common you can find.
Geopolymer materials instead aren’t crystalline so XRF isn’t the best technology to study.
But normally reversal engineering cannot be easy to do with Geopolymers because what was the mineral amorphous allumino silicate, used as precursor, after geopolymerization process becomes new mineral thanks the combination with partially reactive aggregates and will have typically the mineralogy depending on the type of reagent used (alkaline or acidic medium) and the quality of the aggregates. Newborn minerals at early stage of geopolymerization are still amorphous structures then passing time will crystallize about feldspar like minerals.
Remember that it’s possible to create a ceramic-like geopolymer material using specific raw materials to increase at best the Flexural strength even at quite low temperature of curing (alkaline GP or acidic phosphate GP from room temperature to below 200°C). But isn’t possible to use the ceramic powder (micronizing grès tiles) as precursor for Geopolymerization because it isn’t amorphous and so enough reactive.
Maybe using ceramic filler you can obtain an alkaline activated material (AAM) using very corrosive pH, but will be very difficult to stabilize the material because its own over saturation of cations, then durability (high instability under water immersion and the high amount of efflorescence and leaching) cannot to be obtained . In this case XRF can also be used because hardened AAM are compose by hydrated, close to Portland like material.
Best
Alex Reggiani
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I want to do quantitative analysis for vitamin A acetate raw material using HPLC method, but my sample cannot dissolve in many organic solvent such as methanol,ethanol,chloroform and hexane.
is there any recommended solvent that i can use for dissolve it ?
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Some raw material fo vitamins can not dissolve directly like other APIs because these are added some other components for stability. You need to investigate how this raw material is and in base on that create a method for a selective extraction of the vitamin. Good luck
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I have added 4 control variables namely firm size, board size, industry and firm age. do i have to collect data for the control variables? my research topic is impact of gender diversity on firm performance.
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Hi Nabeel,
If you have added the control variables in the equations, the data needs to be collected for that. This would help you see if the relationship between your variable of interest and dependent variable still holds in the presence of the controls.
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