Structural Equation Modeling - Science method

I am preparing a protocol for SEM of a biofilm, but do not have easy access to 100% ethanol for the dehydration steps. Can 100% ethanol be substituted for 100% alcohol (95% EtOH, 5% methanol/isopropyl alcohol)? 95% ethanol generally is not anhydrous, and water has enough surface tension to potentially damage the specimen. I am also open to other suggestions if anyone has any. Thank you!!

We were imaging platelets under SEM. We got this structure. Any recommendation on what this structure could be will be of great help. Thank you

Hello,

I'm doing a Master Thesis, and it's my first time dealing with SEM - PLS 4 (with barely any statistical background behind me). I'm analyzing the relationship between negative word of mouth (NWOM) and consumer loyalty, moderated by the initial consumer loyalty towards the brand (pre-NWOM), as well as the type of NWOM (frequency, solicited or not, concordance with belief, strength of expression, performance or value-related).

I'm not sure whether I've collected sufficient data to do SEM, and am struggling with elements such as a pvalue that's NaN (consistent bootstrapping), a non-satisfying discriminant validity when trying to correct construct reliability & validity, or an AVE below 5 despite having removed any indicator with an outer loading below 5. Are those issues related to data-collection?

If anyone could help me out, I would be really very grateful, as my tutor isn't helping out a lot and will give me a very low grade if I do only descriptive statistics. Any help would be immensely appreciated. Here is my data set and screenshot of my model. I'd be happy to provide any extra information.

Thank you!

Dear Fellow researchers

I am looking for a statistician who is familiar with Smart PLS-SEM model analysis and interpretation

Hej, I did SEM in AMOS and CMIN/DF is -465248062004,313.

Is this normal or does this indicate that I am doing something wrong?

Thanks a lot!

Ilia

Dear researchers, I am facing a problem with the shrinkage of RBC during the SEM sample preparation. In brief, blood was collected from the fish, and the smear was prepared on a 10mm circular glass slide and air dried. The dried slide was subjected to fixation in 2.5% glutaraldehyde (In 0.1M PBS). After that, the dehydration was done with ethanol gradient (30% to 100% and even tried 30% to 70% also). Then the slide was dipped in 98% HMDS (an alternative to the critical drying point method). When observed with SEM the RBC was found to be shrinkaged. What may be the reason behind this. The image is also attached here. Kindly suggest the reason behind this.

Thank you.

I am analyzing a Scanning Electron Microscope (SEM) on high plastic silt that has been treated with cement. During the examination, I observed a recurring shape across some of the images, which appears to be a particle with a diameter of nearly 20 microns. Could it be a hydration product, or an aggregation of silt particles, or any other foreign material? Could any researchers/ experts shed light on this? It would be a great help. I'm new to analyzing SEM images, so please bear with me if my question seems silly.

I have three variables (A, B, C) and do a multilevel SEM with R - Lavaan.

I do not understand why the following two models render different regression coefficients:

in the 1st one I use the ready aggregated latent variables from the sheet directly, in the 2nd one I define them within the model, but the data behind is of course the same.

Could anybody please explain why that is and which model would be the right one to use?

1.) "

level: 1

A ~ B + C

level: 2

A ~ B + C

"

2.)"

level: 1

A =~ a1 + a2 + a3

B =~ b1 + b2 + b3 + b4

c =~ c1 + c2 + c3

A ~ B + C

level: 2

A =~ a1 + a2 + a3

B =~ b1 + b2 + b3 + b4

C =~ c1 + c2 + c3

A ~ B + C

"

thanks so much for any help!

I have conducted a CFA(n=313) in SEM through R studio and I have got these results.

Chi squared 265 (p<0.001), RMSEA 0.093 and and CFI 0.867. Can some expert help me to interpret the data. All of the estimate coefficients loadings are significant and positive.

I and new to SEM and I would be happy to receive guidance on the procedure to analysing this path diagram using SEM.

I am using AMOS to run SEM. When the model with no interaction, the x2/df is smaller than 5, but when the interaction is added, the X2/df is much bigger? How to deal with this? 

I need a sample paper that uses second-order SEM reflective models using SmartPLS.

I'm using covariance-based SEM software. Do I need to normalize the data before I run the SEM model? The SEM model is fine and the data is large (744).

1. Do I need to normalize the data before I run SEM

2. Would the central limit theorem not apply and so I need not normalize the data

3. Normalizing would change the basic characteristics.Would the findings still be valid

Good morning to everyone,

I have a problem with high value of RMSEA in MGCFA with categorical variables with the WLSMV estimation method in Mplus.

I test a model consist of 4 variables on 4-point scale. I compare 28 countries.

Results of testing configural invariance are:

Chi-square: 1884.026

Degrees of Freedom: 57

P-Value: 0.000

RMSEA: 0.130

90 Percent C.I. : 0.125 - 0.135

Probability RMSEA <=.05: 0.000

CFI: 0.991

TLI: 0.972

If I do the same analysis but I set variables as continuous, so results are good (RMSEA 0,08; CFI 0,980; TLI 0,939).

Can anyone please thoroughly suggest me how to overcome this problem of the inadequate (poor) value of RMSEA? 

Thank you very much.

Hello all! I have synthesized hydroxyapatite nanoparticles and have to characterize them with DLS and SEM analyses. Can someone please let me know the sample preparation methodology for DLS and SEM characterization? Thank you!

I want to know how i can use SEM and TEM micrographs to discuss mesoporous and microporous nature of materials

I am preparing bacterial samples treated with drugs for analysis of their morphology and surface using scanning electron microscopy (SEM). I am considering whether platinum or gold would be more suitable for sputter coating.

Hello ResearchGate Community,

I am an accomplished Assistant Professor and Research Associate with a diverse background spanning over 13 years in both industry and academia. Currently completing my PhD in Mathematics, with an anticipated date of July 2024, I am eager to leverage my expertise in technology adoption, statistical and mathematical modeling, and structural equation modeling (SEM) to contribute to cutting-edge research initiatives.

Skills:

Proficient in technology adoption strategies, statistical and mathematical modeling techniques, including Structural Equation Modeling(SEM) and Artificial Neural Networks(ANN). Extensive experience utilizing tools such as SPSS and AMOS to analyze data and derive meaningful insights.

Interests:

Passionate about leveraging mathematics and statistics to drive technological advancement, particularly in the realms of e-learning and online education.

Interested in exploring the dynamics of technology adoption, usage, and acceptance within higher education institutions, with a focus on post-adoption behavior, continuous intention usage, and actual usage patterns.

I am seeking a postdoctoral opportunity where I can collaborate with like-minded researchers to address complex challenges at the intersection of mathematics, statistics, and technology adoption. My goal is to contribute to the development of innovative solutions that enhance the effectiveness of educational technologies and inform strategies for organizational change.

If you are aware of any leads/opportunities or research projects aligned with my expertise and interests, I would welcome the opportunity to connect and explore potential collaborations. Please feel free to reach out to me here on ResearchGate or via email at [[email protected]]

Thank you for your consideration.

Warmly,

Shard

Assistant Professor | Research Associate |

PhD Candidate in Mathematics

Line mappping

Hello everyone,

I am currently undertaking a research project that aims to assess the effectiveness of an intervention program. However, I am encountering difficulties in locating suitable resources for my study.

Specifically, I am in search of papers and tutorials on multivariate multigroup latent change modelling. My research involves evaluating the impact of the intervention program in the absence of a control group, while also investigating the influence of pre-test scores on subsequent changes. Additionally, I am keen to explore how the scores differ across various demographic groups, such as age, gender, and knowledge level (all measured as categorical variables).

Although I have come across several resources on univariate/bivariate latent change modelling with more than three time points, I have been unable to find papers that specifically address my requirements—namely, studies focusing on two time points, multiple latent variables (n >= 3), and multiple indicators for each latent variable (n >= 2).

I would greatly appreciate your assistance and guidance in recommending any relevant papers, tutorials, or alternative resources that pertain to my research objectives.

Best,

V. P.

I have tried HDMS to dry my samples (spiders), and it works perfectly. However, the price for a few milliliters is unaffordable for the quantity of samples I have to prepare. CPD also works well, but in the last few weeks, I have experienced problems such as collapsing of some body parts, or it is hard to manipulate when samples are to small.

I have used Keller's reagent till now but I am not able to see silicon network in SEM. I am also facing problem in etching. Some times I can see the microstructure in Optical Microscope but not able to see silicon network in SEM in same sample.

Kindly guide me please.

can we see the grains and grain boundaries without using any etching procedure by SEM?

I am trying to synthesize MXenes (Ti3C2Tx), using the MILD etching method (in-situ formation of HF by reaction of LiF + HCl). I am taking equal amounts of LiF powder (Sigma Aldrich, >99.99% trace metals basis) and Ti3C2Tx powder with 10ml of 9M HCl. LiF and HCl are stirred for around 30 minutes, followed by a slow addition of MAX precursor. The etching is done at 35 deg Celsius at 300 rpm for 24 hrs. The product is then centrifuged 10 times (10 minutes each). The supernatant after the last centrifugation is collected, and its SEM micrographs (also EDS mapping) show very high traces of fluoride particles (~1 um) and a much lesser trace of Titanium. Why did fluoride particles remain undissolved or not removed in the decant of the first few centrifuges? Also, is it possible that Titanium is also getting etched out?

Note: 1) Traces of Aluminium are also present, even more than Titanium.

2) The mixture of LiF and HCl remained fuzzy even after 30 minutes of stirring at 300 rpm.

3) All these are done in ambient environment

Hi,

I'm searching to find as many articles, mostly in form of dissertations and thesises of M.Sc. / M.A. and Phd,

about classic reviews on SEM, and recent reviews on different methods of SEM (Structural Equations Modeling).

Any guidelines and suggestions would be greatly appreciated.

Suppose that we have three variables (X, Y, Z). According to past literature Y mediates the relationship between X & Z while X mediates the relationship between Y & Z. Can I analyze these interrelationships in a single SEM using a duplicate variable for either X (i.e., Xiv & X Ddv) or Y (Yiv or Ydv)?

Hi.

I am a sport psychology researcher and I usually analyze structural equation model (SEM) using AMOS program.

But this time, my data reported unusual results.

It revealed that variables A and B had a negative correlation (in SPSS, Pearson's r), while A had a positive effect on B in SEM.

I wonder why this happened and what is the solution in this case (There is no issue of multicollinearity).

I'm suspicious about "perfect fit" in my SEM path analysis, with fit indices of CFI = 1 and RMSEA = 0 which I would expect from an just-identified model. However, since I have 3 degrees of freedom, the model is over-identified. What might this indicate?

Hello,

I'm looking for journal recommendations for my study on teacher satisfaction with online teaching. Using Structural Equation Modelling, we applied Herzberg’s two-factor theory to identify intrinsic and extrinsic satisfaction drivers among teachers. Findings highlight differences in satisfaction across STEM and non-STEM teachers and various age groups.

Could anyone suggest Q2 / Q3 journals focused on education technology, or teacher psychology?

Thanks for your help!

Hello, I have collected 300 responses on employee outcomes like satisfaction and engagement in relation to work from home. Many items have skewness outside the range of -1 and + 1. However, kurtosis for these items is in the range of -3 and + 3. All the variables are continuous. Can I use this data to conduct analysis in SPSS and IBM AMOS (using CB SEM). Also, can I use demographic data that is non-normal as control variable? Thank you in advance.

For an experimental design ( one experiment and one control group), at least or minimum, how many subjects need to be included in the groups in SEM?

I'm excited to speak at this FREE conference for anyone interested in statistics in clinical research. 👇🏼👇🏼 The Effective Statistician conference features a lineup of scholars and practitioners who will speak about professional & technical issues affecting statisticians in the workplace. I'll be giving a gentle introduction to structural equation modeling! I hope to see you there. Sign up here:

Dear colleagues,

the images show fibrous material after 1h contact with whole blood. Samples where washed with PBS and prepared for SEM observations: fixing with PFA, dehydration with graded ethanol solutions, drying in HMDS, sputtering with gold.

The idea was to analyse quantity and morphology of surface-adhered platelets. There are samples where I observe many small objects (pointed with arrows) adhered to fibers. I wonder what it can be? Any ideas?

How to test for common method bias in CB-SEM?

Hi,

I was wondering if someone could help me combine the results of 2 different ChIP-qPCR biological replicates.

I used the %input method in my analysis, but the results between the 2 biological replicates are different and when I calculate the average between the 2, the SEM is huge making the differences to be non significant. How can I relativize my results independently of the samples?

Thanks,

Sofia.

Hi everyone, I am doing Meta-analysis of mediation using the structural equation model in R (the package I will use is “metasem”). May I ask if anybody has experience in doing this type of analysis? I have found a guide to follow but I do not know how to import data with the correct format to do such an analysis.... I would highly appreciate it if you could give me any advice!

here is the link to the guide: https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/mediation.html

I'm still learning and confuse, which method should i use for my study. What is the method should i use if 3IVs, 1mediate and 1dv. Some say mediate analysis, some say Smart PLS SEM more easier bcs can get results direct and indirect at the same time.

I wanted to do this SEM test on my samples but there is no this device here in Cameroon. if anyone can help me because I would like to evaluate the stability of my samples according to the NFV08-408 standard

I faced difficulties in choosing a natural tooth bonding procedure for SEM observation. I am looking for a fixation procedure that does not affect the

I would like to conduct the systematic review for causality. I wanna select the studies with SEM and path analysis. Pls suggestions to me.

I want to prepare a termite sample for SEM observation and i know it is dehydrated with a graded series of ethanol. but my sample has already been fixed in 75% ethanol for more than one month. So how to treat these samples to prepare for Scanning electron microscope? kindly suggest.

Are there specific things i must mention when describing these images/

Liposome XRD-SEM

In the modification suggestions the largest one is e20 and e21 (90.00)

When I do this all of my problems are solved. Some researcher say I can and some doesnt agree. I couldnt find any references.

Prof. Mike crowson does the same in youtube but he didnt share any references. I am sharing the figures of what I am planning to do.

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!

Less coating on non-conductive material will distort the SEM images due to charging. But what if we use too much of coating?

I'm using very fine zeolite particles which has protruding shapes and need to get SEM images to study the surface morphology. I observed some sudden bright areas of my images and shaded areas. Since zeolite is non-conductive, it obviously needs coating for SEM images.

What is the recommended coating for these type of powder like non-conductive samples?

Hello everyone.I used commercially available a 12.5mM TAE (Mg2+) buffer to mix my DNA sample and took scanning electron microscope (SEM) images of DNA GG and TT mismatches. Since SEM operators and I don’t have any idea whether its DNA or something else your expertise will be highly appreciated. If anybody is working on it, please share your experiences. The ring-like structure represents the figure from the GG mismatch, and the subsequent one represents the TT mismatch.Thank you.

How can i know if it microporous, mesoporous or macroporous or moixture by mere looking

is there a way to analyse before further using physisorption?

All the p-values in the structural component of my model are high (not significant)

The p-value in the measurement component are low (significant)

Goodness of fit at this point is at acceptable level. I have not used the modification indices yet.

Is my still model usable?

Hello,

My dissertation uses a mediator variable to explore the relationship between three latent insecure attachment styles (preoccupied, fearful, and dismissive) and social media addiction. My survey used complete scales to measure attachment (RSQ Scale with 30 items, but only 4-5 items measure each attachment style), social media addiction (BFAS with 18 items that measure six dimensions of social media addiction), and the mediator has 17 items.

My questions include the following:

1. Can pre-existing scales be reduced to a few indicators to run the SEM analysis? It's my understanding that latent variables should have 3-4 indicators. The scale for the mediator variable has 17 items, which seems quite large to run the CFA.

2. What specific steps would I take to reduce my data before running the SEM?

Any help or guidance regarding where I might find more resources on this topic would be greatly appreciated!

trying to prepare stable nano capsules by polycaprolactone so I need to observe the daily by simple way >

In my studies I have 7 IV as factors that influences 1 DV. Therefore I have 7 adapted scales and one newly established scale ( for DV)

They were then translated to different language.

Can I run EFA after pilot study and CFA in actual study because the data will be analysed in SEM PLS?

Do I run EFA seperately for each factor ?

Pls advice. Tq...

In his very helpful online book on structural equation modeling, Jon Lefcheck writes the following concerning d-separation tests for SEMs:

"Once the model is fit, statistical independence is assessed with a t-, F-, or other test. If the resulting P-value is >0.05, then we fail to reject the null hypothesis that the two variables are conditionally independent. In this case, a high P-value is a good thing: it indicates that we were justified in excluding that relationship from our path diagram in the first place, because the data don’t support a strong linkage between those variables within some tolerance for error."

My understanding is that this is a standard procedure in SEM and more broadly in DAG-data consistency checks, not a quirk of Lefcheck's workflow. My question is, doesn't this amount to an attempt to use p-values to control type II error rate?

Setting aside the inherent limitations of p-values even when used correctly, the fundamental problem is that p-values simply don't control type II error rate --- they control type I error rate.

The language in the documentation of the R package bnlearn, a suite of Bayesian causal discovery tools, is even more explicit in equating the failure to reject the null hypothesis of zero correlation with the acceptance of the null hypothesis, i.e. the conclusion that correlation is "in fact" zero:

"Now, let’s check whether the global Markov property is satisfied in this example. We will use the survey data to check the Null hypothesis that S is independent of T given O and R. In R, we can use ci.test function from bnlearn to find conditional dependence. [...] As you can see, the p-value is greater than 0.05 threshold, hence we do not reject the Null hypothesis and conclude that in fact, S is independent of T given O and R."

As a convert to causal inference and Bayesian logic from the cult of cause-blind null-hypothesis statistical testing, I find it frustrating that when it comes time to validate our causal graphs --- after all our effort to construct principled causal models and estimate parameters informatively --- we fall back on p-values, and a dubious use of p-values at that.

Am I missing something? I hope to be corrected.

After several washings of shale samples in search of microfossils, each time we obtained the same results, a species of foraminifera and radiolarians, we followed the standard method, with water, hydrogen peroxide , soap, vinegar... but in the end it's the same

I am looking for where I can carry out the following instrumentation analyses.

SEM with EDX (5um, 10um and 20um) for powdered samples

SEM with EDX (5um, 10um and 20um) for polished surface samples

XRD (Rietveld Mineralogical analyses) for powdered samples

Any credible suggestions or recommendations?

Thanks

I am facing a weird reversal of two positively related constructs (positively correlated, path positive when no other variables are present in the model) within the full structural model calculation in AMOS:

When inserted into the full structural model and adding directive paths between the constructs, a negative significant path loading appears between the two constructs that correlate (and should correlate!) POSITIVELY. The negative path is oppositve everything that makes sense from a theoretical perspective.

This means, that when other IVs and the measurement models come into play, the relationship that exists is somehow reversed. Obviously, this is an artifact produced by SEM.

It is to mention that some of the 6 IVs we have in our model have VIFs higher than 3, one is even around 7. This means, that multicollinearity between different IVs could be the root of the problem.

Are there other possible explanations, and what are potential fixes?

If anyone has experience with this, or literature to recommend, I would really appreciate it.

Thanks in advance! Charlotte Baar

Hi all,

I've been using a blend of chitosan and PEO (dissolved in aqueous acetic acid) to produce electrospun nanofibers. Whilst I am able to produce bead-free nanofibers, I often see the formation of droplets on the nanofiber mats. Using SEM, you can see areas where the droplets have dissolved the nanofibers. I have tried different polymer concentrations, polymer ratios, solvent concentrations, tip-to-collector distances, voltages and flow rates, but nothing seems to stop the droplets from forming. Is this because of the humidity of the environment, or could other factors be involved? Thanks!

Its about synthesized materials characterizations

I took SEM pictures of my clean 0.2um pore size PES membrane filter. The first picture is coated with 5nm gold, and the second is with 20 nm carbon. Both pictures are 2500X magnification, and the scale is 4um. I wonder why the two pictures look significantly different. The picture I coated with gold doesn't look like the pore size is 0.2um.

Thanks,

I would like to know which is the best for simple moderation analysis with 1 independent variable, 1 dependent variable and 1 moderating variable.

Thank you.

There are many software for analysis but which one is the best?

We have a Jeol JSM 3690 SEM. The filament burned out and I replaced it and checked the objective aperture for damage. Cleaned everything well and reassembled. No amount of alignment, filament heating/saturation, settings in the software will produce an image. Any suggestions are welcome, just can't afford the absurd amount of $$ necessary to get a Jeol tech out to service it...

How do I run a mediation with two independent variables and one dependent variable in PLS SEM? Please suggest some paper as well its interpretation ?

Sample size requirements for Structural Equation Models (SEM)?

I built a model in SEM (only direct paths between 4 predictors and 1 criterion) and then MGCFA for 4 groups of respondents. It performs well. However, I wanted to test if the model works for subgroups (each group can be divided into two subgroups) but they were too small for additional MGCFA (some of them are under 100 cases). I would like to do multiple regression on factor scores obtained from SEM for each subgroup. The problem is, due to the treatment of the measurement error in multiple regression, that I'll get biased results. Is there an alternative or correction for such situation?

We need work where the structure of this material is affected, as well as the composition and sem analysis of this material. Thank you!

CK, OK, NAK MEANING IN SEM EDAX GRAPH

Greetings,

I am currently in the process of conducting a Confirmatory Factor Analysis (CFA) on a dataset consisting of 658 observations, using a 4-point Likert scale. As I delve into this analysis, I have encountered an interesting dilemma related to the choice of estimation method.

Upon examining my data, I observed a slight negative kurtosis of approximately -0.0492 and a slight negative skewness of approximately -0.243 (please refer to the attached file for details). Considering these properties, I initially leaned towards utilizing the Diagonally Weighted Least Squares (DWLS) estimation method, as existing literature suggests that it takes into account the non-normal distribution of observed variables and is less sensitive to outliers.

However, to my surprise, when I applied the Unweighted Least Squares (ULS) estimation method, it yielded significantly better fit indices for all three factor solutions I am testing. In fact, it even produced a solution that seemed to align with the feedback provided by the respondents. In contrast, DWLS showed no acceptable fit for this specific solution, leaving me to question whether the assumptions of ULS are being violated.

In my quest for guidance, I came across a paper authored by Forero et al. (2009; DOI: 10.1080/10705510903203573), which suggests that if ULS provides a better fit, it may be a valid choice. However, I remain uncertain about the potential violations of assumptions associated with ULS.

I would greatly appreciate your insights, opinions, and suggestions regarding this predicament, as well as any relevant literature or references that can shed light on the suitability of ULS in this context.

Thank you in advance for your valuable contributions to this discussion.

Best regards, Matyas

I have a longitudinal model and the stability coefficients for one construct change dramatically from the first and second time point (.04) to the second and third time point (.89). I have offered a theoretical explanation for why this occurs, but have been asked about potential model bias.

Why would this indicate model bias? (A link to research would be helpful).

How can I determine whether the model is biased or not? (A link to research would be helpful).

Thanks!

i am very new to experimental physics and could you please suggest me resources to understanding the concepts of UV, FTIR, XRD, SEM, Raman and Judd Ofeld theory and i am struggling with these, are doing ?i also studying separately. if u suggest it ll be very helpful to me.

Hello everyone,

Whats up??

So, I was doing some tests with genetic (PCA scores), environmental (PCA scores), past climatic variation (PCA scores), and geographical (latitude and longitude) data using SEM analysis and some weird results came across. The determinantion coeficients (Std.all) were higher than 1.0 (100%) and p values very significant (0.000). I looked for explanations in lavaan package and other SEM documentation and videos but I didn't find nothing.

Did someone here already used SEM and get this kind of problem?

Model created and analysis summary are attached

I have already collected the data ,during analysis one factor total variance value became greater than 50%. How can I continue?

Dear All,

I want to study advanced research methods(beyond descriptive and inferential analysis such as factorial analysis EFA, CFA, SEM,and regressions, etc.) for conducting research in languages or linguistics. Please suggest some books on how to use correct tests accurately. Regards, Saleem

Can anyone share the tissue processing protocol for SEM/TEM of tissue slides of mice?

I want to determine the elements of the pyroxene and plagioclase minerals so that I can measure the change in composition and temperature-pressure? Is SEM analysis suitable for this study?Thank you

Since this question has been asked but not in quite a few years, and software changes frequently - what alternative do people recommend to AMOS for SEM? I'm a doctoral student who will be using SEM for my dissertation. Many thanks!

I use different types of CL to characterize minerals and materials, like CL color imaging on a light microscope or hyperspectral CL on SEM/EPMA.

Is there any solution to convert a CL spectrum (wavelength vs intensity) to coordinates in a RGB color space to determine the perceived color, in order to be able to correlate and analyze the information coming from the different modes of observation ?

Thank you for your help