
Dr. Jennifer Roback Morse joins the show to unpack a study on children raised in same sex households that she says was buried because its findings did not match the conclusions some researchers wanted. The conversation focuses on how earlier claims that "the kids are fine" were built on weak samples, and why newer, more representative data has produced very different results. We also discuss Mark Regnerus's work, how ideology can shape the use of social science data, and why statistical methods like multiverse analysis matter when researchers choose what to include, exclude, or redefine. Key topics In this episode, Dr. Jennifer Roback Morse explains why she believes the latest video from the Ruth Institute exposes a suppressed study on gay parenting. She says the original "kids are fine" narrative relied heavily on small, nonrandom samples, including early studies based on 26 couples and other convenience samples. Morse contrasts those older studies with newer, larger, more representative datasets that she says show more serious emotional difficulties among children of same sex parents. She discusses Father Paul Sullens's role in reexamining a 2016 study and pushing researchers to revisit their conclusions. The conversation revisits Mark Regnerus's 2012 research on adult children from same sex households and the criticism he faced before his findings were later vindicated. Morse explains the difference between asking parents how their children are doing and asking adult children about their own outcomes after leaving the household. She argues that once government and census-style datasets became available, the earlier consensus about "no differences" became much less secure. The discussion turns to ideological bias in academia, with Morse warning that claims like "the science is settled" can be used to shut down questions rather than answer them. She connects the broader problem of relativism and ideological capture to medicine, law, and the public interpretation of data. Morse closes by describing how multiverse analysis can test whether a result holds up across many possible analytic choices, and why that kind of checking matters for controversial social science.
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