Howdy, Stranger!

It looks like you're new here. If you want to get involved, click one of these buttons!

Supported by

How to interpret BF10 for interaction effect?

Hello! I recently use JASP to conduct a Bayesian regression analysis to examine my Hypothesis. Attitude is the dependent variable, and the three independent variables are persuasion (dichotomy), label (dichotomy), and self-construal (continuous). My hypothesized there would be a three-way interaction on the dependent variable.
I put the three main effects and all the two-way interactions and the three-way interaction into the model. According to the results JASP produced (in attached file), the BF10 of the full model which contains the three-way interaction is 0.240, while the BF10 of the model which only lacks the three-way interaction is 0.054. Dividing them, I could conclude that inclusion of the three-way interaction is around 4 times better than not. However, comparing 0.240 to the BF10 of the null model, which is 1, it seems that the alternative model which contains the three-way interaction is not good at all. I'm confused about how to explain my result properly. Could you help me out?
Thanks!

Comments

  • Hi,

    To understand this, it's probably best to start with a hypothetical 2-way interaction.

    Say that you have two factors, A and B, and that there is a full A × B cross-over interaction in your data. In that case, there is neither a main effect of A nor B, but there is an A × B interaction. So JASP might tell you that the BF10 for the A + B model is (say) 0.1, reflecting that there are no main effects. But the BF10 for the A + B + A × B model might be (say) 0.5, reflecting that there is an interaction. Specifically, these results would tell you that the data is 5 times more likely under a model with the interaction than under a model without the interaction.

    So if you're only interested in the interaction, then the relevant comparison is between the model with the interaction and the model without the interaction (but with all main effects). The null model is, in this case, of no concern.

    In your case, you're interested in a 3-way interaction, but the logic is the same. The relevant comparison is between the model with the 3-way interaction and the model without the 3-way interaction (but with all 2-way interactions and all main effects). So this:

    Dividing them, I could conclude that inclusion of the three-way interaction is around 4 times better than not.

    … is a sensible conclusion.

    Does that make sense? In recent versions of JASP, you can use the 'Inclusion BF based on matched models' option, which is based on this logic, and should tell you that the 3-way interaction is reasonably well supported by your data.

    You can also take a look at a post that I wrote some time ago about this question:

    Cheers!
    Sebastiaan

  • Hi Sebastiaan,

    Thanks! I've read the post you suggested and it helps me understand better Baws factor and Bayes factor. I think your answer made perfect sense. But I'm still a bit confused about the meaning of the three-way interaction. Compared to the model which only lacks the three-way interaction, I can conclude that the model with the three-way interaction is 4 times better. But the BF10 of this model is way smaller than the pure null model.
    It makes me doubt the real function of this three-way interaction. Because it seems the most powerful statement would be the dependent variable does not depend on anything. In other words, my question is: when should I look into the model and when should I look into the effect? If the evidence for a particular effect is abundant but the evidence for the model with this effect is small compared to the null model, do they seemingly contradict each other?

    Thanks again for your clear answer!
    Shuang

  • If the evidence for a particular effect is abundant but the evidence for the model with this effect is small compared to the null model, do they seemingly contradict each other?

    No they don't :smile:

    Say that you have a model with three factors and all interactions: A + B + C + A×B + A×C + B×C + A×B×C. So there are three main effects, three two-way interactions, and one three-way interaction. And now say that the data does not contain any evidence for the main effects nor for the two way interactions, but does contain evidence for the three-way interaction, then there are 6 effects that drive the BF down relative to the null model, and only 1 effect that drives the BF up relative to the null model.

    That's why you can get a model with a three-way interaction that does poorly compared to the null, even though the three-way interaction is supported by the data. That's all there is to it. Does that make sense?

    Incidentally: If you would compare a model with only the three-way interaction (but no main effects nor two-way interactions) to the null model, you would find that the three-way model does best. But JASP doesn't allow you to construct such models: if you have an interaction in a model, then all the main effects and lower-order interactions must be in the model as well. This is the principle of marginality, which for some reason (unclear to me) statisticians consider a good thing.

    Cheers,
    Sebastiaan

  • Thanks, this is super clear! You really help me out :)

Sign In or Register to comment.

agen judi bola , sportbook, casino, togel, number game, singapore, tangkas, basket, slot, poker, dominoqq, agen bola. Semua permainan bisa dimainkan hanya dengan 1 ID. minimal deposit 50.000 ,- bonus cashback hingga 10% , diskon togel hingga 66% bisa bermain di android dan IOS kapanpun dan dimana pun. poker , bandarq , aduq, domino qq , dominobet. Semua permainan bisa dimainkan hanya dengan 1 ID. minimal deposit 10.000 ,- bonus turnover 0.5% dan bonus referral 20%. Bonus - bonus yang dihadirkan bisa terbilang cukup tinggi dan memuaskan, anda hanya perlu memasang pada situs yang memberikan bursa pasaran terbaik yaitu http://45.77.173.118/ Bola168. Situs penyedia segala jenis permainan poker online kini semakin banyak ditemukan di Internet, salah satunya TahunQQ merupakan situs Agen Judi Domino66 Dan BandarQ Terpercaya yang mampu memberikan banyak provit bagi bettornya. Permainan Yang Di Sediakan Dewi365 Juga sangat banyak Dan menarik dan Peluang untuk memenangkan Taruhan Judi online ini juga sangat mudah . Mainkan Segera Taruhan Sportbook anda bersama Agen Judi Bola Bersama Dewi365 Kemenangan Anda Berapa pun akan Terbayarkan. Tersedia 9 macam permainan seru yang bisa kamu mainkan hanya di dalam 1 ID saja. Permainan seru yang tersedia seperti Poker, Domino QQ Dan juga BandarQ Online. Semuanya tersedia lengkap hanya di ABGQQ. Situs ABGQQ sangat mudah dimenangkan, kamu juga akan mendapatkan mega bonus dan setiap pemain berhak mendapatkan cashback mingguan. ABGQQ juga telah diakui sebagai Bandar Domino Online yang menjamin sistem FAIR PLAY disetiap permainan yang bisa dimainkan dengan deposit minimal hanya Rp.25.000. DEWI365 adalah Bandar Judi Bola Terpercaya & resmi dan terpercaya di indonesia. Situs judi bola ini menyediakan fasilitas bagi anda untuk dapat bermain memainkan permainan judi bola. Didalam situs ini memiliki berbagai permainan taruhan bola terlengkap seperti Sbobet, yang membuat DEWI365 menjadi situs judi bola terbaik dan terpercaya di Indonesia. Tentunya sebagai situs yang bertugas sebagai Bandar Poker Online pastinya akan berusaha untuk menjaga semua informasi dan keamanan yang terdapat di POKERQQ13. Kotakqq adalah situs Judi Poker Online Terpercayayang menyediakan 9 jenis permainan sakong online, dominoqq, domino99, bandarq, bandar ceme, aduq, poker online, bandar poker, balak66, perang baccarat, dan capsa susun. Dengan minimal deposit withdraw 15.000 Anda sudah bisa memainkan semua permaina pkv games di situs kami. Jackpot besar,Win rate tinggi, Fair play, PKV Games