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  • I am looking for an app (Android), which can facilitiate a qualitative, digital ethnography study with a main component of diary entries in form of visual, text and audio.
  • I plan to work with 10-20 participants based in Tanzania over a duration of 18 months.
  • I have so far primarily used WhatsApp (chat and call for both self-reporting and interview over video/phone) based on the idea of using a tool participants already are familiar with, but it is hard to structure/organise mixed data and many participants.
  • I have checked out dScout, Indeemo and FlexMR, but I have different kinds of doubt (approach and budget) + this earlier thread from 2014 https://www.researchgate.net/post/Is_there_an_existing_digital_tool_for_diary_studies
Any suggestions for free apps, apps particularly directed at ethnographic research (and not marketing) are very much welcome as well as any research papers on the use of diary studies combined with digital ethnography (preferably in Africa) and/ or the use of whatsapp in qualitative research in Africa (I am familiar with https://edoc.unibas.ch/51923/1/20161216110618_5853bc9abba73.pdf)
Thanks!
Pernille
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Marion Demossier, thanks for your question. For different other reasons I changed to fieldwork among the Tanzanian diaspora in Denmark, thus I did not have to work over the geographical distance with self-reporting. Also, I did not find any app which suited my case. I tested using WhatsApp for self-reporting - but that is another story (it is free, participants were used to it - but it takes
effort to keep connection on track). Perhaps this is useful https://edoc.unibas.ch/51923/1/20161216110618_5853bc9abba73.pdf
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Hello!
Maybe the community can help me with this question: I am preparing an analysis for a daily-diary study and most of the methodological literature as well as papers on study with similar designs apply a person-based centering before computing a MLM. Most of the time, authors specified that the predictors were centered: I was wondering if there is a rational on why only the predictors and not the outcome variable (also continuous) have to be centered in order to analyze changes on the daily level.
Thank you very much in advance,
Matteo
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first, let me post a quote from the Centering section in:
"Keeping in mind Bryk and Raudenbush’s advice “That no single rule covers all cases,” we recommend the following, advice which is consist with that offered by Enders and Tofighi (2007). At level 1 continuous predictors should be entered group-mean centered, which makes the intercept similar to the original intercept (null model) which helps to maintain the stability of covariance matrices. Grand-mean centering at level 1 can be used when an analyst wants to adjust the level 1 intercept for level 2 differences in a predictor. At level 2 continuous predictors should be grand-mean centered. This makes the intercept the expected value for an observation at the mean of the level 2 predictor, which corresponds to the mean of the sample for the outcome and is a value that is easy to understand. Categorical predictors (discussed below) should be entered uncentered. This makes it easier to interpret results, i.e., what coefficients represent. A more detailed discussion of centering can be found in Nezlek (2011; pp. 13-18). "
To answer your question more directly - you could center the outcome but there is no need to, as it will only change the intercept and not the slopes.
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Conducting a weekly quantitative diary study. 
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Hi Nelesh,
This might be a bit late, but hopefully it'll help someone else. I've been having the same question and I came across an article that talks about the statistical power gained by different variations of sample sizes. https://d-nb.info/1097268829/34.
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I am currently trying to figure out how to use Visual TimePAcTS application for analysing activity data emerging from time use diaries. I am having trouble setting up a database and entering my respondents' data. I was wondering if anyone here has had experience using the Visual TimePAcTS application and if so whether they would mind sharing with me their experience? 
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I am also searching for a website where we could download VisualTimePAcTS/energy . Anyone with a link to the page or the software ?
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Hi, I am new in diary study and intends to analyse them qualitatively. From my search, I found many articles that describe quantitative analysis on diary. Would appreciate if you could suggest one. Thanks
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Nurhanis Syazni Roslan- I recommend for you reading the study be Janssens, KA et al 2018. I enclose a copy. Good luck.
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I conducted a diary study. 3 independent judges analysed the data (with thematic analysis) and after deliberation all together now we have 6 categories.
Do I need to do confirmatory factor analysis?
Thanks
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Yağmur Rumeli, confirmatory factor analyses will not help you unless you have quantitative data to analyze. You also need to have a hypothesized underlying factor structure, which is essentially a measurement model. You don't have a measurement model in these types of studies. Attached in my response is a PDF document that explains the assumptions and process of confirmatory factor analyses.
If you want to increase the validity of a qualitative research project such as this, then two of the most common methods are member checking and inter-rater reliability.
Inter-rater reliability will tell you the extent to which the 3 independent judges agreed on the categories. It helps to determine whether or not your themes can be replicated within the study. I always recommend doing this when you have multiple judges, as it's a fairly easy way to see how the coding scheme worked.
Member checking involves going back to the participants and ensuring that the themes that you pulled and the interpretations that you made accurately reflect what the participants were trying to convey. It's essentially an exit interview from the project.
I recommend that you do inter-rater reliability, as you should have the necessary data at your disposal. If you have the opportunity to do member checking, then that will add even more validity to the study, but that requires that you have the capacity to re-contact the participants.
Here is a link explaining how inter-rater reliability works:
Here is a link to some information about member checking:
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Dear RG community,
I found it a bit confusing on few terms and aspect of study design.
1. From my understanding, longitudinal study is an observational study consist of cohort (prospective or retrospective) and panel study.
How about diary study, is it considered panel study?
How about repeated cross sectional studies that also often referred as longitudinal study?
2. Experimental study consist of before-after (or known as pre-post) or repeated measure interventional study, either randomized or non-randomized. Am i right?
3. For longitudinal study, especially prospective cohort, most references highlight on two group (exposed vs unexposed) with similar baseline and categorical outcome measure.
How about single group cohort with different baseline and outcome continous measure?
4. For longitudinal study especially cohort study, most references talk about months to years of followup duration.
But, for some psychlogical measure such as stress and fatigue, there should be no problem to conduct shorter duration of cohort study e.g. within 8 hours, am i right? For example, we measure baseline stress level prework, and followup the outcome stress messure postwork, i.e. after 8 hours of continous work. Is it appropriate?
.
Thank you for kind assistance.
Your response is highly appreciated.
Regards,
Fadhli
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Need assistance in determining the lagged effects in a time series of four weeks using R. I need to determine multilevel estimates of the models predicting a lagged effect. What is the syntax that would do this?
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Have send you a PM :-)
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I am preparing to conduct a Diary Study as part of my PhD research in the areas of leadership and entrepreneurship.
My desire of course is to start from the strongest foundation possible.
So I turn to our wonderful research community!
Please share with me, the best articles, books, resources you have found.
Much thanks.
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Thanks Nanda. Very helpful!
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I am currently designing a diary study and ponder on a good identification code generated by participants so I can match the cases of different measurement occasions.
Does anyone have a reliable code combination which reduces both wrong code input (e.g., asking for size in cm which might get 180 on one day and 181 another day) and too much overlap between people (e.g., last character in mother's first name which is likely to be a vowel)?
Cheers
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I have a sample of 800 participants who I'm tracking over time. From the lit it seems that accuracy reduces when the questions are about other people so I ended up using a code based on 5 questions:
1. First letter of your first name
2. Day of birth
3. Month of birth (which I then convert into a number)
4. First letter of your middle name (if none, use X)
5. First letter of city/town you were born in
So far, we've done 4 data collection waves from Sept to Jan and the accuracy rate is around 90% with no duplicate codes. Most people who make errors seem to do it on Q4 or 5. So overall, seems pretty successful!
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Looking for association between Mg intake and various psychological factors.
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Hi
i agree with Mehdi
regards
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I noticed the formation of e red pigment on a typical  Arabic cheese, this cheese is prepared by Bedouins from the goat milk and conversed in a hyper-saline water, the pigment is formed only when the cheese is exposed to the air (out of water), is it a microbial metabolites? Is this phenomena known for other types of cheese?
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Most likely a pigment made by an obligate aerobic organism like Pseudomonas. I have investigated a similar problem observed in Mozzarella cheese (usually soaked in Salt saturated water before packing in a plastic film). The film used usually has low oxygen penetration coefficient and the cheese lasts for the expected length of shelf-storage time. However, in that specific case the film supplier ran out of the regular film so he substituted it with a film material that was more permeable to Oxygen. Guess what! Oxygen interned the package and allowed the obligate aerobic organism to grow and produce the red pigment. 
We were able to isolate the organism and show it will not grow or produce the pigment in absence of air (20% Oxygen).  But upon exposure to oxygen the pigment was produced. It is a Phenazine Pigment.
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I am looking to integrate simple, objective measures of stress into an app-based diary study. Are there currently any apps that could replace, for example, the use of saliva cortisol as a measure of stress? I am trying to design a cost-effective study but also want to include some physiological measures.
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Thank you! 
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Maybe someone could share articles about DIARY method... i mean method / strategy / or methodology when participants of the research write experience in the diaries. Thank you
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Dear Renata,
I add some papers which you can find interesting
Best regards,
Luis
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I am planning a diary study and want to collect data on the work engagement and on job characteristics of people in the media sector. The participants should self-report their everyday activities over a certain period of time on a daily basis. Has anyone been using something like a web application to collect data like that? 
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 Hi Thomas,
There are several. Tamlin Conner offers an extensive list here: http://www.otago.ac.nz/psychology/otago047475.pdf
and reviews top research-specific (and not) apps and SMS services for diary studies here: (start at page 17/slide 33): http://www.otago.ac.nz/psychology/esmworkshop2015
I'm with LifeData, and we actually offer a smartphone app in which you can create diary study protocols. lifedatacorp.com/experience-sampling-app/
The research group that helped develop the app used a previous version (iHabit) to ask students questions about their daily activities. (See attached paper.)
Please let me know if you have any more questions about resources for diary study tools, or if our app would be a good fit. 
Thanks so much,
Ellen
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I'm trying to track participants in an individual level over time, in which participants have to report their experience every day.
I'm using Qualtrics for this study. One option is to distribute the survey through email which will allow me to track individual surveys. However, the link could be used only once so I'm not sure how will this work to track a partipant over time.
I would really appreciate your suggestions and help
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You could use the panel function.
Create a panel with your participants.
Give each participant a special id number.
Then you can track them over time.
I have done just this in two studies.
After that qualtrics can automatically send out emails to the participants with an individual link for the survey for that day.
You can even time it so it sends it out at a certain time. Also its real easy to send out reminders to only the participants that has not yet answered.
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Hi,
I have daily level data nested within individuals from a diary study. Let's say I only have hypotheses on between level effects: in this case, is it then always wrong to aggregate the data over the days and use standard OLS type analyses (e.g. correlations)? 
I know there are some concerns with aggregation, e.g. it assumes that within variance is zero, gives people with fewer observations relatively more weight).The latter is less of a problem in my data since people reported about equal numbers of observations and quite a lot of them.
But given that I only have expectations on between person effects, is aggregation such a bad thing?
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Dear Dirk,
    Models are supposed to serve your interests, not the other way around. So don't feel compelled to use a multi-level model. Psychologists, especially, often take repeated measures and derive an average. They then analyse differences between averages. It is particularly suitable if you are actually interested in average performance and no curiosity about the actual distributions involved. 
   Two things to reflect on, though. One is that your between person effects can often be modelled more precisely and accurately using a multi-level model then can be done using an aggregated approach. The second is that the size of the difference between the two approaches can be easily verified if you use a MLM or HLM first... so assuming that the aggregation effects are irrelevant seems a little silly once you become accustomed to MLM/HLMs. The literature on the aggregation bias and ecological fallacy give reasonable pointers on what sorts of mistakes in interpretation are likely to be made by ignoring these points - however, as I wrote, in some fields aggregation is still the standard approach. 
   WRT to what you wrote I am not so sure that these points should dominate your decision. Aggregation doesn't assume that within level/person variance is zero. It is done precisely because people are deciding that the variance is large enough to matter and that they think it is irrelevant to what they wish to test. There is no compelling reason to worry about the weights given to participants as a function of the number of measurements if the pattern of missingness is not informative (MCAR) and you are disinterested in that level of variation. But participant averages can be weighted by their number of constituent observations - and Bayesians would likely insist on this - without much effort on your part.
   So, to summarize, it is not always wrong and not always a problem; in your field aggregation may even make more sense ( I can picture how sources of variation like temperature, feed humidity, sunlight might be less interesting than the average milk output). The things to watch for are biases in interpretation and a rather unnecessary loss of information.
Hope this helps
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I am analyzing data from a diary study (multilevel modeling) and based on previous research I want to person-mean center my predictor variables. One of my predictors is work-family conflict (WFC) which was measured on five days. I think I need to calculate the mean of WFC over the five days (for each person) and then subtract the mean from each individual score. This produces new centred scores. But then when I want to create the scale scores each person has a score of zero which I don't understand. If everyone has a score of zero then there is no variance. Am I understanding this correctly?
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For anyone looking to person-center predictor variables in SPSS coming here as I did, I thought I'd share the steps as George did for R above... 
It's a two step process in SPSS:
1. Create an aggregate variable: the mean score of one person’s observations for the variable. Do this via;
DATA>AGGREGATE in “break variable” move over the participant ID. In “aggregate variable” move over the predictor variable you want to centre (VAR1).
2. Compute the person centred variable from the original variable (VAR1) minus the new aggregate variable (VAR1_mean):
TRANSFORM>COMPUTE VARIABLE: VAR1-VAR1_mean
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The syntax is:
AGGREGATE
  /OUTFILE=* MODE=ADDVARIABLES
  /BREAK=PID
  /VAR1_mean=MEAN(VAR1).
COMPUTE pmVAR1=VAR1-VAR1_mean.
VARIABLE LABELS  pmVAR1 'VAR1 person-centred mean'.
EXECUTE.
-
Hope that can help someone.
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Researching GE's AKTA Pure HPLC unit for the detection of trace proteins in bovine milk. I am looking at getting a result every 5-10 minutes over a duration of 16 hours per day.
With that in mind, is there a better instrument for this purpose?  
Thanks in advance for any advice.
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Hello Mr. Rajendran!
Please try to find some ELISA method for minor proteins in milk ..if you are thinking of assessment of Ig or lactoferin I think that this is nice instrument ...here in Macedonia we have problems with lactoferin examinations and also HPLC procedure is very expensive, so you should check on internet or to ask in some laboratories about this detection.....good luck in your work!
Vesna
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In all the Commonwealth of Independent States (CIS) since years is used the Russian Feed Unit for the animal feeding. How to convert it in French Milk and Meat Unit or in TDN? Thank you 
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Thank you Neeraj! I appreciate it.
Ylli
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Do you have any research experience or reference in asking respondents to keep a diary / journal with their thoughts and reflections about a particular everyday practice ? (mobile music listening in my case)
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My coauthors and I had great success with this method in our study of women's experiences and knowledge of their clitoris over time- diary methods are far superior than interviews on super-sensitive topics like that!  We replicated a similar methodology in study of how people make meaning of odors and manage smells. Both of these studies are available full text if it sounds helpful to you
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I am using an exercise diary to evaluate the effectiveness of my intervention. Now, I am looking for information on how valid this method is, and on how I can check validity of the information given. We know that people cheat (positively to please the researcher) or negatively (they might forget to fill up the diary). I would be glad to receive some references as well.
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We tend to see 'objective' measures as a gold standard at the moment but we also need to be aware of the pitfalls in all measurement techniques. Bear in mind that if you attempt to 'validate' a self-report measure with an 'objective' measure such as accelerometry or pedometry that such validation will never be perfect as all these measures do not assess physical activity in exactly the same way, they each measure a different aspect of physical activity. So accelerometers would not be able to assess the type of physical activity or anything contextual, as a diary would, and would miss out certain forms of activity depending on the placement of the device on the body. So for example an accelerometer placed on the hip would miss any upper body motion, such as lifting weights or carrying shopping bags. Many accelerometers would have to be taken off to swim. A pedometer merely counts steps but says nothing about the frequency, intensity or duration of physical activity. Like self-report measures, these devices rely on the participant engaging with them appropriately. Also, be aware that physical activity guidelines (e.g. 5 x 30 mins per week) were developed on the basis of self-reported physical activity; guidelines based on accelerometry, when they emerge, may well require less of people. These are a few things I discovered when I undertook a rapid scoping review of literature on physical activity measurement in the spring of this year - please let me know if you want references for these points.
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I am analyzing results from a daily diary study in R using the nlme package. I am using hierarchical linear modelling and am not sure which code to use to examine whether the latter model fits better than the previous model.
I tried the code anova (model1,model2) but it is giving me the following error message: Error in anova.lme(Model1.SigControls.IIP, Null.Model.IIP) :
all fitted objects must use the same number of observations
In addition: Warning message:
In anova.lme(Model1.SigControls.IIP, Null.Model.IIP) :
fitted objects with different fixed effects. REML comparisons are not meaningful.
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As Kelvin noted, you need to compare models that differ only in the fixed part using ML estimation rather than REML. One solution is to refit the models with lme4. The anova command there uses profile likelihoods (rather than REML) to compare models and this avoids the problem with using REML fits to compare fixed effects.
The other error message refers to having different n (presumably because of missingness on one or more predictors). This is a common issue with use of any likelihood based statistic. The simple solution is to run the models on exactly the same data (e.g., through some kind of imputation approach or - less satisfactorily - by omitting cases).
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I am exploring the phenomena of coaching, and I am currently looking at this from an experiential/conversational learning point of view.
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Hello Melissa,
I have used it with university undergraduates to see the relationship between emotional intelligence, work-life balance and eating habits. Though I'm in the process of analysing them if you are interested how I did just contact me:-)
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I am currently writing up the protocol for a diary study examining trait-behavioural consistency and its relationship with mood. I would like to present 15 items, 3 times a day for 2 weeks. Ideally, I would also like the items to be rotated at random from a slightly larger pool.
I am specifically looking at the best software to use to enable people to fill in the survey from their smartphones, either via email or the use of an app. Does anyone with experience in implementing similar designs have any suggestions?
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Hi Tom,
me again :)
you can take a look at the compilation provided by Tamlin Conner:
We have done some studies on mood and other stuff using movisens. It is very easy to set up, has good support and works fine with android mobiles. However, it gets rather expensive very quickly depending on how many items you have per signal.
The cheaper alternatives are using an online survey provider that offers mobile-friendly layouts and another one for the sampling. You can look at http://www.surveysignal.com/
this one manages the sampling, the survey is prepared with another software. I like the way how fluidsurveys adapt to the mobiles, but you can also look at soscisurvey (no costs for scientific research). The adavantage is that you are not limited to androids, but these solutions are clearly not that comfortable as movisens.
Best, Dorota
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I must admit I'm not entirely sure whether I want to measure broader aspects of mood/well-being, i.e. negative and positive affect or specific moods, but in the context of a diary study which measures do you think might be most appropriate?
At the moment my choices are PANAS, the UWIST checklist or The Brief mood inspection scale (or perhaps a scale I have missed). Well-being/mood isn't really my area so I'm not sure how to differentiate between the two. In my thoughts I see mood as a more proximal measure and well-being as a distal measure, but the literature I have looked at thus far doesn't seem to support this.
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You might look at the experience sampling literature. One place to start:
Differentiating Emotions Across Contexts: Comparing Adults With and Without Social Anxiety Disorder Using Random, Social Interaction, and Daily Experience Sampling.
Kashdan, Todd B.; Farmer, Antonina S.
Emotion, Feb 10 , 2014