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IV and DV have significant correlation (.345**), and so is the case with IV and MV (-.388**)and MV and DV (-.264**). Mediation model is run on IBM SPSS and shows coefficient for direct and total effect as 0.084 and 0.101 respectively, and coefficient for indirect effect is 0.017 with BootLLCI (-0.008) and BootULCI (0.054) having values with different signs.
Please suggest ?
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I'm assuming that in your 3-variable mediation model you have the following regression equations:
MV = intercept1 + a*IV + error1
DV = intercept2 + b*MV + c*IV + error2
The indirect effect then is equal to the product of the path (regression) coefficients a*b. The direct effect IV --> DV is given by the path c. The total effect is equal to the sum c + a*b.
The negative correlations between IV and MV as well as MV and DV may explain why your indirect effect is positive (the product of two negative regression coefficients is a positive number). Since the correlation between IV and DV is positive (and so path c is probably also positive), the total effect c + a*b is also a positive number.
The confidence interval for the indirect effect (0.017) appears to include zero (the interval ranges from -0.008 to 0.054 and so it does include zero), indicating that the indirect effect is not statistically significant at the alpha level that was specified for the confidence interval.
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Dehydroascorbic acid crosses the blood brain barrier and preliminary research shows it has a neuroprotective function.
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Chemical oxidation (e.g. with AAPH) would be one way to go as some ascorbic acid detection methods rely on the oxidation to DHAA so you can find data on the oxidation efficiency etc. (e.g. If you want to avoid adding any chemicals (a preferable way if you want to use the solution for some kind of experiment that may be biased by AAPH or other oxidants added to the solution) I'd try to oxidise it with light or heat. For example, you can oxidise ascorbic acid to dehydroascorbic acid with UV exposure (e.g.
Whatever you decide to do the important thig to have in mind is that oxidation of ascorbic acid to dehydroascorbic acid is usually reversible (so if you expose the solution to UV and quantify the amount of DHAA bear in mind that the result you obtain will change over time). Additionally there are other products of AA oxidation (some being irreversible) so make sure you thoroughly examine the solution after you oxidise it or you may end up blaming the effects of other oxidation products on DHAA (-:
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Hello,
I am writing my thesis and I am a bit confused in reporting my covariate. my
IV are: brain stimulation, and time
DV: rate of percieved exertion (RPE)
I run repeated measures Anova, but want to control for sex so I put it as covariate, and I found significant value for sex which makes it covariate. However, no significant effect of IV on DV. shall I still report the sex effect? or just say: "no significant effect was found of IV on DV while controlling for sex".
I will appreciate your advices and answers.
Thank you very much
#Statistics #neuroscience #Covariance
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As an aside there is no need to control for sex in a repeated measures design. Any between-subject difference is already accounted for (in a sense) in the within-participants variance (often labeled a subject term). Adding sex will simply reduce the subjects term and not affect the test of the IV (though it can show you whether portion of variance accounted for by the covariate is statistically significant).
On the other hand the model David Morse proposes with IV*sex may produce a different result the interaction with the covariate can increase the precision of the estimate of the IV.
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Hi everyone,
I am conducting some research, and I have 1 IV, 1 DV and four covariates; I was initially planning on carrying out an ANCOVA, but it turns out that the DV will have to be binary( yes, no ) answers. Any suggestions on alternative analysis?
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Since your DV is binary, logistic regression could be an option.
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Hello, as I was doing my data analysis, I came across what I read as a suppression effect. One of my IV which is low but positively correlated with my DV seems to have a negative effect during multiple regression analysis. But how can I explain it? What does it say about the relationship? Is the correlation false? What should I do?
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Is the original positive zero-order correlation significant?
Is the negative partial coefficient significant?
Rather than removing the variable from the equation or treating the correlation as "false," you need to explain why controlling for the variable in question has this effect on the direction of the coefficient.
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Hello, I am currently conducting a moderated mediation analysis in AMOS and want to mean centre my IV and moderator. To calculate the mean, do I use the original dataset or the dataset where I have removed some items with low factor loadings.
Thank you.
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Thank you all very much.
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Hello to all,
I do not know how to make mediation and moderation with covariates in SPSS with a categorical IV and DV (both have two categories 0 and 1). I am doing a between-subject design.
Thanks in advance for our help;
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2 category DV means.you need logistic regression not ols. Setting up moderation and mediation is in
the first screenshot. The second attachment covers logistic regression. Best wishes David Booth
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I am trying to understand if the administration route can influence this AAV serotype's expression in the brain.
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Thank you
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We are going to induce intracerebral hemorrhage by using collagenase type VII (Sigma). We used to do this before using collagenase type IV by dissolving it in PBS. However, Sigma emphasized that we must dissolve collagenase type VII in TESCA buffer. Is it possible to dissolve this collagenase in PBS as well?
Thank you and by best regards,
Reza
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WOW, i have the same question just like you, professor. So do you know the answers for it?
Many articles described that they dissolved the collagenase in saline or PBS.
The technician from sigma told me i should use TESCA buffer.
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The BET analysis of my synthesized ZIF-8 at 77K gives a type IV like isotherm. The sample was degassed at 150 degree C for 8 hours. The BET surface area of ZIF-8 I got is around 1800 m2/g, which is almost similar with the previous literature reported. The t-plot data also gives micropore volume of 0.7 cc/g. But the isotherm shows a noticeable hysteresis loop from relative pressure range around 0.4 to 0.8. Is it possible for ZIF-8 to show a type IV like isotherm as in most of the literature ZIF-8 MOF is reported as microporous MOF. Is there something wrong or there may be some reason. Please suggest
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Capillary condensation can proceed reversibly or
irreversibly. The difference is that irreversible capillary condensation is accompanied by hysteresis. In reversible capillary condensation, the adsorption and desorption branches coincide. Capillary condensation depends on the shape of the pores: conical, cylindrical dead end, cylindrical through, slotted and bottle-shaped.
The synthesis of ZIF-8 is very capricious. Each creator can synthesize his ZIF-8 with different pore shapes.
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Our IV has 4 quadrants which all are not significantly correlated with the DV but the hypothesis include moderation, backed by literature.
Can we still proceed to moderation analysis?
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Maryam Hussain as Rainer Duesing suggests, there is no need for your variables to be > 0.05 or <0.05 to be included in your model. As was suggested, it is better to have a rationale/theory driven question based on which you collect samples which fit to the hypothesis. Why would you bias your study to only showing p<0.05? Wasn't this the hypothesis?
Additionally, path analysis are more suited when there is a specific rationale behind the question. One can always find "the best" model making some statement on the correlation >0.6 and <0.05. But without a rationale and added meaning it is simply a correlation.
It is perfectly fine to keep your study exploratory (hypothesis generating) and make this clear from the start. This next study (hypothesis addressing) might bring stronger and more convincing evidence to the table as you formed a clear rationale before and gathered new data not and addressed the hypothesis less biased by any selection procedure.
I hope this helps in clarifying your study aims
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Hi all!
I've been looking into the effects of pre-order promotion design on pre-order sales of videogames.
My IV"s are binary, 1 moderator (AAApublisher, referring to firm size) is binary, and my second moderator (Sentiment, low is negative, high is positive) is continuous. Sentiment has some missing values. My DV is number of pre-order sales, which I've had to log-transform due to its distribution.
I've attached my conceptual model.
When I make a simple linear regression model with only my IV's, the results suggest that they've got decent explanatory power of the DV, but when I add my moderators, these main effects disappear. As I understand, these moderators (and not my IV's) are actually the cause of the changes in my DV.
Looking forward to hearing your thoughts on these results. Thanks!
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Sorry- hard for me to help without understanding what you are researching.
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Family member with diabetic retinopathy and who has poor vision from blood being trapped in the eyes stayed at a hospital on two separate occasions and only received a sodium chloride saline IV bag and was able to see much better because the blood had mostly receded from the eyes. He also had a potassium sodium imbalance, high potassium of 5.4 and low sodium of 121. Is there a way to replicate this treatment at home to test and see or something similar to offer relief for this condition? He stayed at the hospital the third time but did not receive the IV solution and did not have an improvement what would be the reason for the blood to almost recede completely? Looking forward to your responses. Thank you and have a blessed day
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Thank you, he has been on a whole food plant based diet over 4 months now a1c is down to 7.9 sodium ses steady at 138 for a month now but potassium still at 6.0. he would be able to recover much quicker with temporary measure of elevating the blood from the eyes as he is not able to drive work or participate in sports anymore. I'm just not sure why the blood was almost gone from they're on 2 seperate hospital visits with only an IV saleen solution administered.
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how do we categorize SES based on the Kuppuswamy scale then make it into three categories like Low, Medium, and High?
Since scores for kuppuswamy sale are.
Socioeconomic class Total score I Upper 26‑29 II Upper middle 16‑25 III Lower middle 11‑15 IV Upper lower 5‑10 V Lower below 5
this can be recoded into three? any reference?
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no answer
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I got set of data that includes:
Gender: categorical (classified as IV in jasp)
Ethnicity: categorical (classified as IV in jasp)
Congruent: continuous data (classified as DV in jasp)
Incongruent: continuous data (classified as DV in jasp)
I have been asked the following questions:
Is there a significant interaction between ethnicity and implicit association?
I am struggling to choose the correct test; I am trying ANOVA but actually I don’t know what I should measure to answer the question!
Is it the interaction between Ethnicity and gender? What about congruency data?
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Chi square test for association of categorical variables.
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i have multiple dv's and m ultiple IV's ,research paper and articles which i have read have suggested me to use multivariate multiple regression. but the syntax in spss of multivariate multiple regression and MACOVA seems to be same. Analyze>Genral linear>multivariate.
i am putting Dependent variable under the dependent variables category and independent variables under the Covariates category as variables needed to be in nominal scale ,if they have to be put under the Fixed Factor category.
please help and suggest my all the DV's and All the IV's are measured on 5 point likert scale
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You need multivariate ordinal regression. See the attached Google search. You might have to build scales first if your variables involve multiple questions from a survey. See @David L Morgan advice on that issue available here on RG. Best wishes David Booth
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Currently, I am working on project of "CHROMIUM (VI) REMOVAL FROM WASTEWATER USING ACTIVATED CARBON DERIVED FROM ALGAE". If anyone working on the same project, can you please share details of what you had done in this regard. Besides this, if you had used/tried other activated carbon including but not limited to tea waste, tangerine peel, bovine gut, sunflower seeds, can you please share details regarding the drawbacks of those activated carbon that you had used in your project?
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one of the disadvantages is that the activated carbon is not regenerated at the end of the adsorption process.
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How do I check the Endogenity in AMOS if i have one IV (Knowledge sharing) and one DV (Performance). And how do I check the robustness if I have Knowledge sharing as IV and Performance as DV and Gender as a moderator.
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there are various methods to do this in AMOS or SPSS
check this link:
Also this video explains the concept very well which might be helpful for you:
test whether coefficient on your regression is significant. If it is, conclude that X and error term are indeed correlated; there is endogeneity.
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My framework IV on DV through Mediation. Is any requirement that must measure direct effect of IV on DV? Because I wanted to prove that Mediation makes a positive significan and never claim that direct effect is significant.
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It is necessary to measure and report the direct effect between your IV and DV. This is because, you would want to present to your audience, the type of mediation established in your study (i.e., Partial or Full). Here is the overview of the conditions and types of mediation effects among constructs. For instance, Baron and Kenny (1986) proposed that several conditions should be met when testing a mediating effect through a three-separate regression estimate:
regressing the mediator on the independent variable (Model 1);
regressing the dependent variable on the independent variable (Model 2); and
regressing the dependent variable on both the independent variable and on the mediator (Model 3).
The four conditions of mediation under this method are:
the independent variable (IV) must significantly predict the mediator in Model 1;
the independent variable must significantly predict the dependent variable (DV) in Model 2; the mediator must significantly predict the DV in Model 3, and
the IV must predict the DV less strongly in Model 3 than in Model 1 (i.e., partial mediation).
However, full mediation occurs when the IV is insignificant in the third model (Model 3). I hope you find this submission helpful. Regards!
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During a process of polyester staple fiber production, while the IV (intrinsic viscosity) of PET granules is within 0.63-0.64 dl/g, the IV of as-spun fiber output from spinneret is around 0.66-0.67 dl/g.
What are the reasons for such growth in IV?
Does IV rise have any negative effect on polyester fiber quality?
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The main impacts are the increase in melting and glass transition temperature.
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Hi all,
For some time now, I cannot figure out what is going on with a moderation model (model 1) with a covariate that I'm running.
IV = categorical, two groups, dummy coded (lockdown (T2) or no lockdown condition (T1))
DV = continuous (mental illness)
M = continuous (resilience)
Covariate = gender (dummy coded)
I set the conditioning values at 16th, 50th and 84th percentiles. Further, I mean centred the continuous variables.
The overall model is significant.
When I run the data for visualizing the conditional effect of the focal predictor, I get a graph that I do not think is right. Resilience is pictured on the x-axis, mental illness on the y-axis, and the lockdown or no lockdown condition is noted as two variables (0 and 1) on the upper right side of the graph. I always thought that that is where the moderator should be, not the IV. That's why I'm confused.
Can somebody explain to me what is going on or what I perhaps should do differently in the analysis? A big thank you in advance :)
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Andrew Seidman I watched the video and found where I was looking for, as it was mentioned in the comments: The simple slopes are often separated by a categorical moderator rather than by the standard deviation (for a categorical moderator and continuous IV). That would explain why the graph was set up differently. I presume it was correct after all.
Thank you very much for your help. I wish you a great day.
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Hello. there's something that has been confusing me.
I wanted to conduct a tensile test for Polycarbonate materials printed with FDM 3D Printer. In the ASTM D638 standard, there's five types of specimen, I to V. the standard recommends the use of type I specimen whenever possible. However, when I look up what other researcher had use in their respective research. Some, if not, the majority of papers that I read uses the type IV specimen. Despite it being stated that the type IV specimen is used only when we want to compare different rigidity, or to be used for non-rigid materials. Most of the papers that I read tested rigid materials such as PLA, ABS, or even PC itself. Is there any reason for that? And is it still valid? Because I also would like to use the Type IV if its allowed to save cost and time.
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As per the guidelines of ASTM, Type I should be the preferred specimen. Although, mostly Type IV is used because it is recommended to be used when a direct comparison is required for different rigidity cases (non-rigid or semi-rigid).
But there is a caveat with using mechanical testing with 3D Printed specimens. If you dig a little deeper, you will find that the suggested method for fabrication of specimens is die-cutting or any method which ensures homogeneity and is isotropic. Whereas, specimens produced using additive manufacturing technologies (you mentioned FDM) are hardly ever homogeneous because of the layer-by-layer fabrication process. Nevertheless, if you use the same machine, printing technology and settings (at least the infill and layer deposition rate) and orientation for several material samples of interest, it should produce fair results for comparison. Just be sure not to call them material properties, if the load applied is perpendicular to the layers, as they are not.
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Hi all,
Please let me know If you know any things about my question. Recently I have conducted a mediation analysis using PROCESS model 4 macro software and with the bootstrapping method (Hayes, 2017). As you know, in these conditions, we have three paths, A, B, C
path A is between IV and M(mediator), path B is between Mand DV, and path C is between IV and DV when M also is in the model. Please let me know when we can say the indirect effect is significant? should all three paths be significant before? for example when path C isn't significant, and or two paths C and A aren't significant, we can say the indirect effect is significant (I know when zero isn't in the confidence interval (BootLLCI and BootULCI), we have the significant indirect effect). what about when we have two and or three mediators in the model?
All the best,
Esmaeel,
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Stefan Poier Yes, a single-step test of the indirect effect with bootstrap CIs is what I would also recommend. MacKinnon et al. showed that this provides for the most powerful test. That's why I think path analysis with simultaneous estimation of all effects in a single model is the way to go. Nonetheless, I find the logic of the Baron and Kenny approach to still be worthwhile for beginners to think about in order to better understand what mediation actually is.
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83-104.
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99-128.
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I applied Ordinal Logistic Regression as my main statistical model, because my response variable is 7 Point-Likert Scale data.
After testing for Goodness of Fit using AIC, i got my best fit model, including 4 independent variables (3 explanatory and 1 factor variable).
However, I encounter 1 negative coefficient value (0.44 odds) of 1 explanatory variable (all explanatory variables are also 7 point Likert-scale).
My theoretical assumption is simple: the more frequency of explanatory variables (engage in activities) happen, the higher impact score on response variable (mutual understanding)
That's why I am confused when 1 independent variable has negative coefficient.
In this case, how should I interpret this IV?
Thank you very much,
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Just like any other regression..If you want examples simply Google your question. Best wishes David Booth
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I have a query regarding which type of regression analysis to use for my study. I have used a scale (dependent variable) that contains 9-items and each item is marked on a 5-point Likert scale. Scores of each item are summed and ranges from 9 to 45. Higher score indicates the respondent has more characteristics of that construct.
Similarly, there are two independent variable. One IV has 20-items and each item is marked on a 4-point Likert scale. Score ranges from 20 to 80. Second IV has 7-items and each item is marked on a 5-point Likert scale. Score ranges from 7 to 28.
The reviewer has suggested me to use non-parametric tests since my data is ordinal. However, previous studies have used Multiple Linear Regression using similar types of constructs.
Which type of regression analysis is appropriate in this case - ordinal regression or multiple linear regression? Any literature explaining this would be highly useful.
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I'd normalize the DV (that is, rescale it so that the value will be with 0...1) and use a beta-model or a quasi-binomial (regression) model.
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Please mention name of the regression analysis suggested.
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Multiple linear regression is used to model the relationship between a continuous response variable and continuous or categorical explanatory variables. Recall that simple linear regression can be used to predict the value of a response based on the value of one continuous predictor variable.
https://www.jmp.com › en_us › wha...
Multiple Linear Regression | Introduction to Statistics | JMP
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Dear researchers,
I am currently conducting a study for my master's thesis about the effect of empathic concern and personal distress on purchase intentions (two of the subscales of the IRI by Davis as IV's) and WTP for sustainable apparel, with attitudes towards sustainable apparel as a mediator. I have two questions regarding the analysis. Besides the two IV's, the mediator and the two DV's I also include 6 different control variables.
1. Does anyone know if the subscales of the IRI are reflective or formative?
2. A researcher argued that as the IVs are formative (not sure if that's the case though?) and the model is complex, PLS needs to be used. Would you agree with that? Or do you think OLS is sufficient? Would it make a difference if the IVs were reflective and not formative?
Kind regards and thanks a lot in advance.
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Thanks a lot, this is very helpful!
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What synthesis approach would be more appropriate to obtain palladium oxide nanoparticles using potassium hexabromopalladate (IV) (K2PdBr6 ) as precursor material?
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Several points:
  • Palladium salts are usually easily reduced to the metal rather than to the oxide
  • Notwithstanding the above statement, all surfaces in the presence of air and water will be fully oxidized. That is, on the important surface Pd will be found as Pd2+ (oxide) and not as Pd0 (metal)
  • There are many suitable reducing agents. I used 5 or 10% hydrazine hydrate in my Ph.D as the only decomposition products are water and N2. I would avoid borohydride reduction (and clichés) like the plague as intractable B is produced in the metal and this cannot be removed by washing. It acts as a catalyst poison in some reactions. Other utilized reduction agents include citrate (Turkevitch route). I'd prefer to use flowing H2 as this will also remove HBr in the gas stream
  • IMHO, you'll need the essential characterization techniques of BET physisorption, some form of chemisorption, and ESCA/XPS. Without these essential techniques you'll not be able t get a handle on the surface (and this is where the particles interact with their environment
Some background webinar material (registration required):
Metal colloids - their preparation, application and characterization
Silver colloids and invisible ink
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There have 3 independent variables which are knowledge, perception, and awareness. My dependent variable is buying decisions. The question in my questionnaire only has demographic and IV questions. But the IV questions represent their buying decision. So I still need to create dependent variable question? My hypothesis is looking for the different status (worker and student) of their buying decision.
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Yes! You need to create a question for the dependent variable in your questionnaire. Your questionnaire must capture both the dependent variable and the independent variables.
Kindly consider the valuable inputs earlier made in response to your question.
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all interval/ratio data
DV are
Temperature
Distribution
Price
IV is
Sales Volume
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Dear Jevohna England,
The relationship between the IV and DV should be termed as "the effect of a variable (IV) on the other variable (DV)". In this context, your IVs are Temperature, Distribution and Price and the DV is Sales Volume. Accordingly, the relationship between the IVs and DV can be termed as "The Effect of Temperature, Distribution and Price (IVs) on the Sales Volume (DV)" as indicated above by Piyush Sharma.
Hence, you have to initially (first) run the correlation analysis to confirm the significant relationship between the variables. If the correlation between an IV and DV can confirm the effects of IV on DV and between IVs can confirm the existence of a level of multicollinearity. Once you confirmed all these, then it is appropriate to run the Multivariate Regression as indicated above by Marcelo Linardi.
Hope this will help you. If you have a question, feel free to communicate.
Good luck with your progress.
Regards,
S. Senthilnathan.
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I need the exactly protocol with volumes of reagents
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Here is the link to a paper that describes all the mitochondrial assays
Best,
Alpana
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Dear community,
I ran a logistic regression with continious IV in SPSS. In the table "variables in the equation" one variable is missing (despite using entry method) and without any message from SPSS . When browsing through the web I understood that this might happen due to collinearity. However Collinearity diagnostics did not return a clear sign for it. Highest VIF-values are 6.1 and the highest Conditionindex is 21.1.
So my question are:
1. Is my regression model still valid despite SPSS dropping one variable?
2. Are there other reasons than collinearity why the IV is missing in the model.
Thanks Ilka
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Try stat package in R programming
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I have 3 DVs and 5 predictor variables (technically, only 1 IV and the other 4 are controls).
I ran it on Stata and all seems to have worked (since mathematically, all predictor variables are treated the same way), but technically I only identify one of my predictor variables as the IV of interest. I would rather stay away from SEM. Thank you so much!
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Thank you very much and hope you are staying warm, Xingyu Zhou
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In IV Curve of single langmuir probe, we can see that there is current even at zero potential, why ?
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Because your probe is grounded somewhere and when you don't have any potential on it, the ground potential will be different from the plasma potential. Hence, you get a potential difference between ground and plasma and a current must flow to close the electric circuit.
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Say we have a Multidimensional variable (having 4 dimensions) that acts as an Independent variable (IV) and want to measure its impact on a uni-dimensional variable (dependent variable).
Would it be okay if we take a composite score of IV (= sum of scores on all the 4 dimensions of it) and run the analysis while the DV is a single dimensional construct?
In other words, one variable (IV) has second order factor structure while another variable (DV) just has first order factor structure...
Thank you in advance!
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Well Done !!! interesting...
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I am working on Perovskite solar cell. We have a KEITHLEY 2450 SMU at our lab. I want to measure IV characteristics of solar device with FTO as front electrode and Ag as back electrode material using this SMU. My question is; which type of clip (for example aligator clip etc.) needed to make good contacts with electrodes in my thin film solar device.
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Kindly see also the following useful link: https://www.preprints.org/manuscript/201912.0364/v1/download
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Scenario : You are shifting your patient & the epidural catheter got broken at the filter end. You troubleshoot but to no avail it is working. The surgery and patient characteristics are : ASA 1, obese male & he underwent open radical nephrectomy with a loin incision with only one functioning kidney.
What would be your alternatives to manage pain in a resource limited setting? Will IV tramadol, with IV PCM (mulitmodal) produce similar pain scores in a patient receiviing epidural morphine alone?
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local infiltration
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I am trying to improve the solubility of a BCS class IV molecule by applylying co-crystalization. But I want to know the mechanism of how co-crystal enhance solubility.
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Dear all, please have a look at the attached file. My Regards
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Does Beta Value found out using AMOS Software shows the influence of one Independent Variable (IV) on Dependent Variable (DV)
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Hello Mohammad,
Yes, the estimated path coefficient linking one variable to another can inform you as to whether there is a non-zero "influence" or, more neutrally, relationship (of course, you would refer to the test of significance for the coefficient, or to the confidence interval estimate).
As well, if the coefficient reported is standardized (relative to all others in the model), you can interpret the relative impact of that relationship, in comparison to others. Note that AMOS does not, by default, issue standardized coefficients--you have to ask for them explicitly.
Of course, gauging the strength of the relationship of some IV on some DV also must be tempered by the other aspects of the model (e.g., are you evaluating direct vs. indirect effects, or of moderation, or of a set of IVs on a DV--in which case collinearity can cause you to misrepresent absolute relationship strength). So, model complexity matters.
Good luck with your work.
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If I have the dose of some drugs taken intraperitoneal , how can I convert it to an intravenous dose both are given to rats ? would it be given with the same dosing frequency ? the only available data in literature is the oral bio-availability (F=49%)
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Hi,
There is no simple solution. A very good paper describing the problem is here in the full version:
If you ask about frequency important is to know that after IP you will have the same half-life as after IV. If difference will be clinically meaningful then has an impact on frequency and steady-state. Second, think you can always expect lower Cmax because it's the route that covers the "absorption" phase. Of course, it's not absorption in the same meaning as from GIT. Finally its distribution from the injection site - 'compartment' into the bloodstream but in the effect, you can measure tmax which is not observed in the case of IV of synthetic small molecules. In many cases, bioavailability after IP could be lower than after IV. Moreover, there are huge differences between synthetic drugs and biologics. The mentioned paper describes the differences in detail.
Click 'next' on the slides.
Best regards
Tomasz
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I'm currently designing the study for my dissertation. Originally I was just going to design a plain phenomenological study where I select participants and conduct a structured interview with all of them and find the pattern and then discuss the final result and say how does IV impact DV based on the pattern of the qualitative data.
After talking to many professors from other psychology departments, they gave me cool suggestions saying it would be more convincing if I could conduct a comparative study where one group of participants are selected with the IV and another group of participants selected without the IV and ask these two groups the same set of interview questions and cross-compare the patterns of these two groups' interview results.
My questions are :1. Is there a specific method name for this research design? 2. How should I frame it in writing the methodology chapter if it doesn't have an already existed term?
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I would reconsider if you are to use qualitative, you should use a semi- or unstructured interview as your chosen research method and design, a structured interview would not be applicable as structured interviews are quantitative. It would make sense then if you are to use the quantitative method using two groups IV and non IV- could then be used in a comparative study to explain the DV. Whereas if you wish to use
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First of all, I really appreciate that you take time to help me out.
Info about my conceptual model:
Moderator = Social Capital
IV = entrepreneurial intention
DV = entrepreneurial behavior
Hypothesis 1: There is a positive relationship between entrepreneurial intentions and entrepreneurial behaviour.
Hypothesis 2: An increase in social capital positively affects entrepreneurial intentions.
Hypothesis 3: Social capital positively moderates the relationship between entrepreneurial intentions and entrepreneurial behaviour
My question is how to test H2 because I find many tutorials on how I test H1 and H3 with Structural equation modeling (SEM) but I can't find anything about H2. Could you help me out?
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Hello Boris,
If all three variables are measured or observed (vs. latent), then ordinary path analysis or regression could be used to evaluate each hypothesis.
H1: Testing path EI -> EB (for difference from zero, and positive sign)
H2: Testing path SC -> EI (for difference from zero, and positive sign)
H3: Testing path of SC*EI -> EB (note: SC*EI is a product term, which you may wish to form by mean centering each variable before multiplying). For H3, you'd want to include direct link of EI -> EB and possibly that of SC -> EB so as to get the relative contribution of the interaction term compared to the direct influence of the constituent elements on outcome, EB).
This could all be specified in a single path model.
Good luck with your work.
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I'm currently writing on a dissertation, c.40 pages, and I'm left with no academic support due to the holidays now.
My topic in international relations is very specific and something about which there exists only scant literature. In fact, most of that literature redefines the phenomenon (very contentious that it does exist), but I want to explain how it actually emerges, the reasons why it happened. The work will be done comparatively with two case studies and process tracing. The phenomenon logically is the dependent variable.
From studying the two cases I have gained a good understanding of the drivers behind the phenomenon, and those drivers explain the emergence of the phenomenon in both cases. So the drivers are the independent variables. What I know so far helps me to bridge the gap between IV and DV; that is, explaining the mechanism that led to the occurrence of the DV.
However, what stuck me know from reading guides on how to write dissertations (or Bachelor/Master thesis) is that they all presuppose a 'theoretical framework', or a 'theory' that drives the research question. Apart from conceptualising how to think and measure the DV, I don't really know what else I should add. There is no literature/theory that would explain the DV. Why can't I just proceed as I planned?
Would appreciate any help!
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When it comes to provide theoretical support, I slightly take a different view. We need to come up with an explanation that sounds cogent. Available theories are handy in this regard. Yet, their absence must not be discouraging for us as we can provide reasoning on the basis of our own understanding supported by relevant examples.
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Good Day!
I am now conducting a quantitative study utilizing a descriptive correlational methodology. I am also employing an Independent Variable and Dependent Variable with a Moderating Variable as the third variable. Can you assist me to determine what statistical treatment is essential to investigate the correlation of the IV towards the DV with the intervention of the MV?
Thank you very much!
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As mentioned by David L Morgan , the moderated effect can be tested in a regression model by including a product term (moderator*IV) in the regression equation. This is called moderated regression analysis.
Mathematically, a moderated regression equation for one IV and one moderator variable looks like this:
Y = b0 + b1*X + b2*Z + b3*X*Z + error
where Y = dependent variable, X = independent variable, Z = moderator variable, and b0, b1, b2, and b3 indicate constant regression coefficients. In order to test for moderation, you multiply the X and Z variables to obtain a product term (X*Z), for example, by using the COMPUTE command in SPSS. The product term X*Z gets added to your model as an additional "predictor." The moderator Z can be binary (a 0-1 coded dummy variable) or continuous (metrical/interval scale). In case of continuous X and Z, it is often useful to center X and Z before computing the product term to facilitate the interpretation of the results (in particular when the uncentered X and Z have no meaningful zero points).
The coefficient b3 for the interaction (product) term allows you to examine whether there is an interaction (moderator) effect. The null hypothesis H0 for the interaction effect is H0: b3 = 0. That is, when b3 = 0 in the population, there is no moderation (interaction) effect. When b3 is significantly different from zero, this null hypothesis can be rejected.
For details, see Aiken and West (1991):
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage Publications, Inc.
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Hi everyone,
I have a question:
What statistical analysis should I do when I have:
- IV: Sustainable Campagains
- DV: Customer choice in purchasing sustainable t-shirts
- Mediator : Social Pressure
- Moderator: Quality perception
Can I do ANOVA? Or I have to do regression as well?
Many thanks,
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Structural Equation Modeling using AMOS, Lisrel, Mplus, R and EQS among others.
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Hi all,
I am trying to find a way to take into account local buckling in Class IV sections in Abaqus. This can be done in SAFIR via recalculating the material plasticity under compression, like Franssen et al. in "Effective stress method to be used in beam finite elements to take local instabilities into account" but I cannot find a way to implement a similar method in Abaqus.
I would appreciate very much if someone can give me a hint about this matter or point me in the right direction with some publication.
Thanks in advanced,
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Dear Jigneshkumar, sorry, it does not mention anything about Class IV sections
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I am writing a final year project where my independent variable is employee well-being and my dependent variable is happiness. the factors of my IV are social well-being, physical well-being, and psychological well-being.
Are there other factors that I could add?
Also, I would love to gain more advice and insights.
Thank you.
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If your IV are social well-being, physical well-being, and psychological well-being
use all 3 in a MANOVA or regression analysis the you see what % of happiness you get out of a certain type of well-being.
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Hello All,
Please consider 3 variables:
- DV - dependent
- IV - independent
- MV - mediator
Am using PLS-SEM for analysis. And this is my problem
IV --> DV is a negative, insignificant relationship (acceptable according to theory)
But on introducing a mediator:
IV --> MV --> DV, I get the following:
1) IV --> MV: significant negative (expected according to theory)
2) MV --> DV: significant positive (expected according to theory)
3) IV --> DV: significant positive (certainly not expected)
Variance inflation factors have been checked and are not an issue.
I have been reading about suppression, but I am not sure that is the case here.
Would be most grateful for any suggestions.
Thank you,
Navya
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سؤال جيد كنت اتمنى الإجابة
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I am working in perovskite photovoltaic lab. We are fabricating carbon based monolithic perovskite solar cell. The structure of our cell is FTO/c-TiO2/m-TiO2/ZrO2/C and perovskite infiltration. We have fabricated the solar but we are not getting ideal IV curve which also results in low efficiency. How to improve the fill factor and get better IV curve?
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Georgi Hristov Yordanov Yes, your assumptions are correct. We have some good results also but we get curve similar to this.
I agree there is severe shunting, how can I avoid this thing in order to have ideal IV Curve? Am i doing anything wrong while fabricating the device?
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My moderator is Social Capital. A questionnaire (5 points Likert scale) of 16 questions is used to measure it. (IV = entrepreneurial intentions, DV= entrepreneurial behaviour, M= Social Capital)
My research focuses on if respondents acquired social capital through a (former) membership at a student association. So after the 16 SC questions, the last question was: Have you gained social capital through (former) membership of a student association? (also a 5 point Likert scale)
Therefore, my question is, how do I compute/combine these 2 different aspects into one value for each respondent so I can use it to test my hypothesis.
Thank you in advance!!
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Alternatively, run a factor analysis using the 16 questions, as well as the last question, and see how they are grouped together! It may reveal multiple components. This would be the statistically correct thing to do!
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In a mediation analysis, the direct effect between DV and IV is non-significant. However, the indirect effect becomes significant when the mediating variable comes into play. I often read in sources that for mediation analysis there must be a relationship between DV and IV. According to my logic, if a variable makes a normally non-significant relationship significant, it should be possible to talk about a significant mediating effect there. How would you interpret this? And would such a situation pose a problem? Thank you in advance for your answers along with your source suggestion on the subject.
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The indirect effect quantifies the effect of X on Y through M. Evidence that ab (indirect effect) is different from 0 is consistent with mediation. Evidence that path c is different from 0 is not a requirement now. Correlation between X and Y is neither sufficient nor necessary to claim that X affects Y.
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I am using a questionnaire of Likert scale as a Quantitative method based on analyzing data intervals collected from a survey of a scale (strongly agree, agree, neutral, disagree, strongly disagree).
I have classified my independent variable as follow, but finding difficulty in terms of identifying my dependent variable
IV: Using smart system to measure its effectiveness if it was to be implemented
DV:?
Now my question is, what should be a proper dependent variable? Can it be individual's opinions gathered from the survey?
Thank you.
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Depending on the subject, what he wants to test, if the subject is testing the differences between two groups, the dependent variable will be the subject of the difference between the two groups.
For example, if the subject tests the difference in convictions or cultural backgrounds between those who received the vaccine and those who did not receive the vaccine, the dependent variable is the vaccination
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Looking to start utilising ADIVA scores as part of a unit wide approach go identify patients for whom gaining IV access is "problematic" in an attempt preferentially move high DIVA score patients towards initial attempts via ultrasound guided cannulation. Has anyone had significant experience with this ? are the DIVA scoring methods effective or have you had to develop your own ?
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Thanks all. This project has now expanded and become part of a wider project seeking to identify the infection rate difference between 48mm and 64mm IVs when inserted with a sterile touch technique under ultrasound guidance.
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I am working with two sets of colleagues on research in which we have conducted multiple regression analyses. In one project, there are two independent variables (IVs), one of which is categorical (it's gender) and the other IV is continuous (age). In the other project, there are 11 IVs (they are all significant after backward elimination of several other IVs), most of which are categorical.
I am not sure whether we should be reporting Cohen's f-square as an indication of effect size or something else when describing our results. My main concern is whether the presence of categorical variables would render f-square inappropriate.
Can anyone help, please?
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G'day Robert
I can tell you both from my own experience as well as from other studies that I have never used or seen Cohen's f-square in connection with multiple regressions. To the best of my knowledge, either the unstandardised regression coefficient (in case of binary IV - b) or standardised regressions coefficients (in case of quasi-metric/metric IV - ß) is reported alongside its p-value and the explained variance (R²) for every step (and total).
As you know, I am not a statistician, so I would not go amiss to have a second opinion on the matter.
Cheers!
Marcel
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Hello
I am using Collagen IV for coating cell culture plate for differentiation of embryonic stem cells into hematopoietic progenitors. Is there a way to check if the plates are fully coated or if the collagen is not denatured?
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may be you can check the plate surface under stereomicroscope and compare with non-coated plate, that may be useful. Second you may put some ink on the surface of coated vs non-coated surface and wash both of them very well, you may watch both under stereo microscope. Ink should not show up in non-coated plate.
Good luck
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Hey there! I'm currently writing my BSc thesis, and I really need some help with SPSS and its interpretation.
I'm doing research into the effects of gamification (dichotomy) on learning, with motivation as a mediator, and gender; interest, and experience with gamification as moderators.
I was advised to apply PROCESS Model 5 to the data (a moderated mediation model), and run separate analyses for each moderator whilst including the others as covariates. Now, I thus have three different analyses, with three different models.
With regards to mediation - the effect of gamification on motivation remains the same in all three models, regardless of which moderator is applied. However, I cannot report the effect of motivation on learning, since in each model it is different - e.g. b = 0.074, t(57) = 2.29, p=0.202 OR e.g. b = 0.078, t(57) = 2.44, p = 0.018.
With regards to moderation, I assume I must only report the moderator and not the covariates..?
One last thing... How does it affect the data when my IV is dichotomous, and can I avoid this? The IV dichotomy was first coded as 0 - gamification and 1 - traditional education, and now I swapped them around. If I compare the data, some coefficients only gained a minus (-) sign in front of it, whereas e.g. 'constant' and moderators change numbers.
I would be so grateful if you could help me out, even if it's only an answer to one question!
Thanks, Alexandra
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I have
b = -0.42
Sy = 0.95
S x = 0.99
I need Beta. But the predictor (x) is already standardized. May I nevertheless use this formula?
beta = b * ( S x / Sy)
Thanks for help
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The formula should still work fine even with standardized X because S_X is simply 1. Assuming you have only one predictor, the easiest way to find beta (the standardized regression coefficient) would be to simply calculate the Pearson product-moment correlation (r) between X and Y, which is equal to beta in the case of a bivariate regression.
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I have a 1970s publication citing a section in this 1950 version of Ham on the saphenous vein. I have tried to obtain a photocopy of the relevant section via the publisher and various libraries (including my own at UCL and the British Library) but without success. I need to confirm that reference to a particular part of the text is true. Any help is much appreciated.
Thanks,
Mick Dashwood
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Dear Gry, thanks for the offer. It was important that I get a copy of that particular edition in order to check a referece regarding the vasa vasorum was correct. There was a delay due to covid but I eventually had areply from the British Library and this confirmed that the reference was in fact incorrect. This happens all too often nowadays as authors tend to recycle citations from reviews without checking the original. Regards,
Mick
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For RNA sequencing purpose, which enzyme would you prefer? NEB Protoscirpt II vs Invitrogen Superscript III vs Superscipt IV?
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The only one I have used is SuperScript III, but it worked well.
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I am looking at whether stress levels reduce from time point 1 to time point 2 when engaging in recommended resources.
DV's - stress levels from time point 1 and time point 2
IV's - engaged in resource 1 and resource 2
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Yep, would agree, and recommend to use residualised change scores.
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I have 2 IV:
- Scale data of skills achievement by our University alumni (They rate from 1 to 7 their acquired skills at University)
- Gender (Already converted it to a dummy variable)
And I have 1 DV:
- Job finding difficulty that has 5 options: (Very Easy, Easy, Neither Easy nor Difficult, Difficult, Very Difficult).
I have 2 questions that I need your assistance with:
1. Can I use a dummy variable with 0, 1, 2, 3, 4, 5 values for my DV?
2. My first IV is not normally distributed, can I still run this regression with it?
I am trying to see the contribution of gender or any specific skills on job finding difficulty.
Please answer as clear and as short as possible.
Thank you!
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You're correct in your comment. However you need to us ordinal logistic regression since your DV is ordinal . Google for further into. Best wishes, David Booth
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Hi to all,
I am using simultaneous equations for my study and I have to solve the possible endogeneity problem.
In our study, we are trying to analyze factors that influence farmers' decisions to purchase seeds from different sources. We suspect the existence of endogeneity between seed source and seed variety purchase. And we don't have suitable instruments to solve this problem.
Hence, I am looking for an econometric tool to solve this problem when the dependent variable is binary (seed source) and continuous endogenous variable (varietal age) and suitable IV's are not availability.
STATA code, ivreg2h is not suitable to my condition seems as my dependent variable is binary
Hoping someone could help me to solve this issue.
Thanks
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Use 2SLS anyway: the estimated coefficient would be consistent. The problem is that if you do not find an IV the parameter is not identified. Theoretically, you could still estiamte the model using maximum likelihood (the functional form would make that there is no perfect collinearity), but, in practise, it is rarely accepted.
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I have incorporated a single impurity in 1*5*1 supercell of GaN. For 4 different atoms from group IV I have found the same lattice constant. Is it normal?
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Dear Abir,
Generally, no! But in your particular case of Si dopant (diamond structure) and GaN host, which is usually hexagonal wurtzite with lattice constant 1/ √2 of zincblende lattice constant, it is Ok. Note that a hexagonal wurtzite type, has nearly the same tetrahedral nearest-neighbor atomic coordination as cubic zincblende (ZB) and diamond structures.
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I am very new to SPSS and I am trying to understand how and what type of statistical test that I should use for moderation analysis. For the first part of analysis, I will be doing Pearson correlation analysis to assess the relationship between the IV and DV. However, Im not too sure about the next step to analyse the effect of the moderator variable in the relationship between IV and DV. Please find the attached conceptual framework of my thesis.Can someone guide me on the exact next step to be taken ?
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@Villasini Looking at your model, it seems that your independent variable (effective project management practices) and dependent variable (project success) both are second order constructs. While your Moderator (a first order latent construct) has 24 observed items which too many and would definitely lead to identification problems while running structural model. I suggest you to reduce the number observed variables for the moderator and use Smart-PLS for testing the model because your model is based on second order latent constructs and AMOS won't work with latent constructs while testing moderation. Wishing you best with your work.
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I determined the surface area of CeO2 modified with MgO as 1 m2/g. The isotherms showed significant hysteresis loop (type IV), indicating mesoporous structure, which is strange to me. Can anyone please explain why I am getting hysteresis at such small BET surface area of 1 m2/g?
Also, the pore size is significantly large, about 15 nm.
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Dear Dailami,
It is difficult to discuss about your results in a proper way without having the sorption isotherm. However, as the specific surface area is so small, to get a proper estimation it would be better to use argon or krypton instead of nitrogen.
About the hysteresis loop, it can originate from different considerations, the first one being the limitations of the measurements, due to the very low SSA. If the measurements are ok, the hysteresis loop could be due to some porosity, either in the material particles, or between these particles. Again, to decide between these options, the sorption isotherm would help.
It must be emphasized that, because of the very small SSA, the extent of the porosity should be very limited, and the sorption likely takes place in interparticular voids of your material.
I could tell you more if you could show me the sorption isotherm.
I hope it helps,
Take care,
Philippe
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Is there any standardized tool available or what areas can be considered important to measure the forgiveness as IV, DV and Moderating variables to see the sih ificant difference among children's?
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There are tools to measure tolerance that were used in previous studies that dealt with the issue of tolerance, whether in children or adults. Look for it in published studies.
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My research model is based on the SOR model on the attachment. In my experiment, I display two conditions: red and blue environmental stimuli, to each respondent. Then, I ask each respondent to rate their emotions and responses to both conditions. I know how to use SPSS PROCESS model 4 to test the mediating effect if IV is continuous, but I don't know to test the mediating effect of emotions when the IV is the dichotomous variable, namely, red and blue environment.
Thank you in advance!
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Please see this link. It has good cases and explanations.
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I want to set each of a survey's phrase (question) in a way that reflects independent variable and dependent variable together .
for example : I have CSRD as a IV , and Competitive advantage as a DV , so i want to write phrases (questions) that combined the two variables at once not separately (i.e not phrases(questions) related to CSRD alone and phrases related to Competitive advantage alone).
so is this method correct ?,if it is true, what kind of statistic test may i conduct?
thank you .
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thank you very much David L Morgan
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I have couple of questions and will really appreciate it if you can help me with them:
1) I want to test if different age groups (IV) will score differently to punitive and rehabilitation attitudes (DV's).
2) Same with gender and ethnicity (IV's)
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I think you could run a repeated measures ANOVA.
Dependent variables: Means of 4 Punishment and Rehabilitation questions
Independent variables: Gender x Age Category x Ethnicity
You would predict an interaction between Gender and Age Categories
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I am setting up mitochondrial complex specific assays performed with a spectrophotometer. I am interested in setting up the assay to look at complex IV activity using the electron donor TMPD. Upon metabolism of TMPD by mitochondrial complex IV the amount of oxidized product of this chemical should increase and therefore the absorbance of the absorbance maxima of the oxidized TMPD. At what wavelength does the absorbance of reduced and oxidized TMPD differ maximally so that I can correlate amount of oxidized TMPD with complex IV activity?
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A module contains large number of cells connected in different strings. We can extract the IV curve of entire module using flash IV simulator but I am interested particularly finding or extracting IV curve/characteristic of a single cell.
Is there any way of doing this?
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Hi I want to conduct a SUR analysis on two regression models with the same IV but different DV to compare the contribution of a same IV to the two DVs, but the two DVs are different in terms of order of magnitudes. I wonder how I can standardize coefficients when conducting SUR analysis using the xtsur conmand in Stata, shall I just zscore everything before doing SUR analysis?
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Thanks for the update Bruce. I thought that about Austin and Tu but was mistaken. Best wishes for a wonderful day. . David
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Hi everyone,
I am currently writing my master thesis about customer acceptance of service robots. I use SPSS Process model 9 (moderated mediation).
My IV is robot's voice type (human vs. robotic), mediator is robot's perceived credibility and DV is customer's service encounter evaluation. The a-path from IV to M is moderated by robot's appearance (humanlike vs. machinelike) and customer's extraversion.
In my model, I take different control variables/ covariates into account which are interesting in robotic research. I look at participant's age and gender and if they have had previous interaction with a robot to check if those variables have an influence on my DV. Age and gender seem to have no influence, but robot interaction does.
However, I also collected data on the participant's nationality/country of origin. Taking this variable into the model I have some interesting results, such as that German participants rated the service a lot lower than other nationalities.
Now my problem is, that my Independent variable voice type becomes slightly insignificant when I add nationality/country as a covariate (of course dummy coded). Before adding country the voice style’s p-value was 0,0598 and with the nationality/country, it is at p=0,1007 ( I am using a p-value of <0.1).
My second concern is, that my nationalities are not evenly distributed. I have 501 participants in total and 259 germans (51,7%) and the second-highest is the USA (49 people, 9,8%), Taiwan (8%), the Netherlands (6,4%), UK (4%), Franke (3,2%) and other countries (17%).
So I am wondering if my results can be representative if more than half of the respondent’s nationalities are germans? Therefore, I was wondering if it would be recommend to take nationality into account as a control variable or if I should leave it out, because then also the c' path from voice type to service encounter evaluation would remain significant at p= 0,0598.
Thank you for your help! Best, Nina
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funny I had read the tweet two. One problem with a drawing a complete DAG however is that you have to come up with a complex structure among the surrounding variables (i.e., the control candidate). I find this rather challenging and the library probably won't tell you. That's why I thought creating a kind of flowchart of critical decisions would be of some help. I did not find an example of a structure where the steps lead to an error.
The most obvious goal is to prevent controlling for mediators, colliders/selection factors, and instrument (or not recognizing the potential of a variable as an instrument.
What do you think?
All the best
--Holger