Do I have an effect? Definitions, Examples, and an Infographic on Causal Language

Causal language is a term that gets thrown around often in peer reviews because causal language irritates many seasoned reviewers. I knew when I started these posts on causal analysis, that I wanted to write something on causal language. I googled causal language and looked at what came up – and I could not find any lists of words that were causal, or recommendations for what to use instead. So, dear reader, this post is intended to help you 1) when you are crafting your papers, and 2) when a reviewer says to you “this paper is trying to make too many causal claims”.

First, I wanted to give you an example of a causal claim, and I thought I would find one in my own work. In looking at a few papers, I found this statement in Kamp Dush & Amato (2005): “the influence relationship happiness on subjective well-being”. Why was my use of “influence” in appropriate? Because subjective well-being could have predicted relationship happiness of course! Any time your DV can reasonably predict your IV, stay clear of causal language.

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How Structural Equation Modeling is Ruining Family Research

photo credit: BrittneyBush via photopin cc

How I feel when I see a structural equation model of cross-sectional data with 15 latent variables predicting 3 different outcomes. photo credit: BrittneyBush via photopin cc

Look out readers! This is my first of a series of posts I am working on related to causal analysis.

About two weeks ago, I attended the National Council on Family Relations (NCFR) annual meeting. It is a long meeting – I usually get there on Tuesday, and don’t leave until Saturday. And, while at the conference, I go from 7 am until 9 pm. In fact, I didn’t even leave the hotel two days! This might explain why I went off the rails during the Q&A at the last session I went to, but what set me off was a theme I saw throughout the conference.

Here is the crux of the problem: early career scholars are so focused on fancy statistics (i.e. structural equation modeling, latent class analysis) that they 1) forget about theory and the justification of their research question, and 2) present papers so complicated that no average person can understand, and even the non-average person who has a PhD and tenure cannot understand it.  But, I do not want to lay all the blame on early career scholars – we senior scholars are the ones creating these monsters!!

In most graduate programs, students are required to take several methods courses. I was required to take at least 6 when I was in grad school, and our students are required to take at least 6 as well. Unfortunately though what is happening is that we are overemphasizing the importance of cutting-edge methods and statistics, and underemphasizing the importance of constructing coherent research questions that have strong theoretical justifications.

Why are these complicated statistics ruining family science? They are ruining family science because they are making conferences boring and incoherent, and leading to the rejection of papers from these family science scholars, and a lack of publications can make it hard for these students to get a job. I talked to several colleagues and students about this issue, and each could give me examples of presentations they went to where they could not even figure out what the research question was, or why they should care.

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