Introduction

In this document, I will unpack the most basic “big picture” findings from the first wave of our study, which I have nicknamed “Weird Parents”.

In the first few sections, I will remind the reader of which variables we measured. Sometimes I’ll have a bit to say about the psychometric properties of these questions.

In the last few sections, I will explore some initial trends, but I am hoping that collaborators will think of other, more interesting trends to examine.

Use the sidebar to jump to your preferred section. The R code has been hidden, but you can view it by clicking any of the “Code” buttons. Only the results of the code are shown by default.

Variables

Parenting status

First, I import the cleaned/recoded data file.

dat=read.csv("wave1.coded.csv")

We included people who are planning to be parents (“Future”), who are already parents still raising children (“Current”), and parents whose children have moved out (“Past”). People who aren’t parents and never plan on becoming parents (“Never”), were told to leave the survey.

barplot(table(dat$switchboard),names=c("Past","Current","Future"))

As you can see, most of the participants were current parents. The second most plan on becoming parents in the future. The fewest people in the survey were parents whose children have moved out.

General strictness

We measured general strictness with the following items (rephrased, where appropriate for past and future parents). The R means that this item was reversed scored.

“My children are on a strict schedule”
“There are set rules in our household”
“There is punishment when rules are broken”
R “I treat my children as equals”
“I know where my children are at all times”
R My children and I can talk as equals.

When examining factor analyses, it was apparent that items 1 through 3 were pretty much measuring the same thing, but item 4 was on a different wavelength. Items 5 and 6 were muddled. They seemed to load equeally (and not very much) on separate underlying constructs.

Items 1 - 3 are on the same page. Item 4 is on its own. The rest are muddled.

library(ggplot2)
library(ggpubr)

ggbarplot(dat,x="parent.status",y="strictness",
          add="mean_se", ylab="Strictness",
          xlab="Parenting status",ylim=c(1,7))

There isn’t a very large difference in strictness across the different parenting statuses. However small it might be, it could still be statistically significant.

summary(aov(dat$strictness~dat$parent.status))

##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## dat$parent.status   2   29.0  14.522    14.7 5.14e-07 ***
## Residuals         952  940.3   0.988                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness

According to a one-way ANOVA, there is a significant difference in strictness among the different levels of parenting status.

pairwise.t.test(dat$strictness,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$strictness and dat$parent.status 
## 
##         future  past
## past    0.20    -   
## present 2.3e-07 0.26
## 
## P value adjustment method: bonferroni

Apparently “future parents” intend to be more strict than present parents current strictness, but past parents don’t report being any more strict than either.

Parenting philosophy

ggbarplot(dat,x="parent.status",y="parenting.philosophy",
          add="mean_se", ylab="Parenting philosophy",
          xlab="Parenting status",ylim=c(1,7))

It looks like future parents plan on having a slightly stronger parenting philosophy than either current parents or past parents.

summary(aov(dat$parenting.philosophy~dat$parent.status))

##                    Df Sum Sq Mean Sq F value Pr(>F)  
## dat$parent.status   2   13.5   6.737   4.544 0.0109 *
## Residuals         953 1413.0   1.483                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 1 observation deleted due to missingness

A one-way ANOVA says there is a difference in parenting philosophy among these 3 groups.

pairwise.t.test(dat$parenting.philosophy,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$parenting.philosophy and dat$parent.status 
## 
##         future past 
## past    0.080  -    
## present 0.019  1.000
## 
## P value adjustment method: bonferroni

Future parents don’t quite have a stronger (intention to have a) parenting philosophy compared to past parents (p = .08), but they do apparently think they’ll have a stronger parenting philosophy compared to what current parents say report having. No difference between presnet and past parents on this front.

“Winging it”

ggbarplot(dat,x="parent.status",y="wing",
          add="mean_se", ylab="Winging it",
          xlab="Parenting status",ylim=c(1,7))

It looks like future parents don’t plan on “winging it” very much compared to past and present parents.

summary(aov(dat$wing~dat$parent.status))

##                    Df Sum Sq Mean Sq F value  Pr(>F)   
## dat$parent.status   2     35  17.519   6.395 0.00174 **
## Residuals         954   2613   2.739                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

There’s a significant difference among “winging it” across the three parenting categories.

pairwise.t.test(dat$wing,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$wing and dat$parent.status 
## 
##         future past  
## past    0.0802 -     
## present 0.0018 1.0000
## 
## P value adjustment method: bonferroni

Future parents plan on “winging it” less than current parents currently report “winging it”. Non-significant difference in how much future parents plan on “winging it” compared to past parents (p = .080). No difference in reports of “winging it” between past and present parents.

Preference for all-natural products

We wanted to see if we could develop a scale that had a good chance of measuring traits typically associated with “crunchy moms”. This was part of it: Preference for all-natural products. This was a novel scale invented for this study.

“I prefer organic products”
“I refuse to use any products with GM (genetically modified) ingredients.”
“My family eats chemical free foods.”
“I only use fluoride-free water.”
“Home births have benefits compared to hospital births.”
“There are natural birth control methods that are superior to contraceptive pills.”
“Natural immunity is superior to vaccine-induced immunity.”
“We use all natural products for our household items.”
“I make sure to buy products with no unnecessary chemicals or ingredients.”
“I prefer all-natural products.”
“There are certain ingredients my child/children cannot have (e.g., red dye).”
“I believe in complementary and alternative medicine (e.g., herbal treatments, chiropractic).”
“Natural remedies are better than drugs produced by the pharmaceutical industry.”
The food at most restaurants is not good for us.
R “We use name brand household items (Lysol, Clorox, etc.)”

Factor analyses revealed that these questions don’t exactly measure one single construct. True, some of these are tapping into a “preference for all natural stuff” spectrum. Others, though, are also measuring something else. When examining the factor loadings, it appeared this other factor was a more radical/anti-establishment dimension of the preference for all-natural stuff.

I had R estimate pure “factor scores” for each participant for (1) the degree to which they generally prefer all-natural products and (2) the degree to which they fall into this partially-separate radical/anti-establishment dimension.

There are a lot of different directions to go in with these two variables, but I started by examining how much people report having a strong parenting philosophy relates to the generic preference for all-natural products.

cor.test(dat$parenting.philosophy,dat$all.natural)

## 
##  Pearson's product-moment correlation
## 
## data:  dat$parenting.philosophy and dat$all.natural
## t = 5.8996, df = 947, p-value = 5.069e-09
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1261557 0.2489370
## sample estimates:
##       cor 
## 0.1882819

There is a statistically significant, positive relationship between the two. In other words, the more you report having a strong parenting philosophy, the more you tend to report preferring all-natural products.

plot(jitter(dat$all.natural)~jitter(dat$parenting.philosophy),ylab="Preference for 'all-natural'",xlab="Strong parenting philosophy")

As for the radical/anti-establishment part, I did a quick test to see if it was related to participant age:

cor.test(dat$radical.all.natural,as.numeric(dat$age))

## 
##  Pearson's product-moment correlation
## 
## data:  dat$radical.all.natural and as.numeric(dat$age)
## t = 2.6359, df = 947, p-value = 0.008528
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02182453 0.14817686
## sample estimates:
##        cor 
## 0.08534381

It is. The older you are, the more you tend to embrace all-natural products specifically out of a radical/anti-establishment reasons.

plot(dat$radical.all.natural~as.numeric(dat$age),ylab="Radical all-natural",xlab="Age")

This isn’t the strongest correlation I’ve ever seen though.

Parenting Self-Efficacy (BPSES)

Parenting self-efficacy measures how confident parents are that they can execute this whole parenting thing. We used a scale published by Wooglar et al. (2023). It is called the BPSES:

Even though I may not always manage it, I know what I need to do with my child/children
I am able to do the things that will improve my child/children’s behavior
I can make an important difference to my child/children
In most situations I know what I should do to ensure my child/children behaves
The things I do make a difference in my child/children’s behavior

ggbarplot(dat,x="parent.status",y="bpses",
          add="mean_se", ylab="Parental self-efficacy (BPSES)",
          xlab="Parenting status",ylim=c(1,7))

Would you look at that? For once the future parents have some humility. The present parents appear to have the most parenting self-efficacy. Past parents appear to have lost some of their self-efficacy.

summary(aov(dat$bpses~dat$parent.status))

##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## dat$parent.status   2   11.9   5.934   11.69 9.73e-06 ***
## Residuals         932  473.3   0.508                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 22 observations deleted due to missingness

A one-way ANOVA says there is at least one signficant difference among these group means.

pairwise.t.test(dat$bpses,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$bpses and dat$parent.status 
## 
##         future  past  
## past    1.0000  -     
## present 3.8e-05 0.0053
## 
## P value adjustment method: bonferroni

Present parents have more self-efficacy compared to future parents. Past parents have less self-efficacy compared to current parents. There is no difference in self-efficacy between future and past parents. I guess you feel like you “got this” when you’re in the thick of it? I absolutely cannot relate.

General vaccine attitudes

To measure general vaccine attitudes, we used the following 5 items. I adapted these mostly from a paper by Jolley and Douglas. I’ve used it and different versions for quite a few studies of mine. Like in previous studies, the items very much appear to measure a single underlying construct:

Academics (scientists, researchers) have covered up or downplayed the dangers of vaccines.
People have been deceived about vaccine safety.
Vaccines are harmful, and this fact is covered up or downplayed.
I feel uncertain about the motives of those involved with vaccines (government, pharmaceutical companies, etc.)
The side-effects of vaccines are unforeseeable.

(Note: higher scores on this measure correspond to more negative vaccine attitudes.)

Again, there are a lot more interesting tests to run, but for starters, I examined whether vaccine attitudes relate to age:

cor.test(dat$gen.vax,as.numeric(dat$age))

## 
##  Pearson's product-moment correlation
## 
## data:  dat$gen.vax and as.numeric(dat$age)
## t = 3.9113, df = 938, p-value = 9.843e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06324954 0.18908932
## sample estimates:
##       cor 
## 0.1266791

Interestingly, the older you are, the more you tend to have negative attitudes towards vaccines. I’m a bit surprised by this, given that vaccine hesitancy is sometimes associated with “zoomers” and very “online” young people who are into conspiracy theories. But studies have also shown that older people tend to get victimized by misinformation more often than younger people. So maybe that’s why?

plot(jitter(dat$gen.vax)~as.numeric(dat$age),ylab="Negative vaccine attitude",xlab="Age")

Children will be (or were) fully vaccinated

We asked everyone if (one or more of) their kids were fully vaccinated (or planned to be).

ggbarplot(dat,x="parent.status",y="children.vaxxed",
          add="mean_se", ylab="Children are (will be) fully vaccinated",
          xlab="Parenting status",ylim=c(1,7))

“Past parents” agreed with this the most. This isn’t too surprising. There’s always been anti-vaxx movements, but the latest upsurge was in the late 1990s, has generally increased sense then, and intensified a lot more in recent years.

summary(aov(dat$children.vaxxed~dat$parent.status))

##                    Df Sum Sq Mean Sq F value  Pr(>F)   
## dat$parent.status   2   23.6  11.820   5.083 0.00637 **
## Residuals         941 2188.1   2.325                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 13 observations deleted due to missingness

There’s a statistically significant difference between at least two of these groups.

pairwise.t.test(dat$children.vaxxed,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$children.vaxxed and dat$parent.status 
## 
##         future past  
## past    0.0506 -     
## present 0.9240 0.0045
## 
## P value adjustment method: bonferroni

There was a statistically significant difference between past and present parents. The difference between past and future parents wasn’t technically significant (p is above .05 by some decimal dust). There is no significant difference between present and future parents.

Alternate vaccine schedule

We asked people whether at least one of their children were put on an alternate vaccine schedule (or planned on doing this).

ggbarplot(dat,x="parent.status",y="alternate.schedule",
          add="mean_se", ylab="Alternative vaccine schedule (will be) used",
          xlab="Parenting status",ylim=c(1,7))

Again, we see evidence of a generation effect. Parents whose kids have moved out endorsed this statement less compared to people who are currently parents or plan on becoming parents in the near future.

summary(aov(dat$alternate.schedule~dat$parent.status))

##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## dat$parent.status   2   48.5  24.247   7.641 0.000511 ***
## Residuals         951 3018.0   3.173                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness

There is a statistically significant one-way ANOVA, indicating that at least one pair of group means are different from one another.

pairwise.t.test(dat$alternate.schedule,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$alternate.schedule and dat$parent.status 
## 
##         future  past   
## past    0.00104 -      
## present 1.00000 0.00051
## 
## P value adjustment method: bonferroni

Interestingly, there’s no difference between present and future parents, but both of those groups differ from past parents.

Online behavior for the child/children

We created a small cluster of questions meant to measure the degree to which parents were restrictive about their children’s online behavior (or plan on being restrictive). We used the following 5 items:

“The internet is not for children”
“My child/children have limited screen time”
“My child/children’s internet access is monitored”
R “My child/children have social media”
“My child/children have to ask permission before posting content online.”

The factor analyses gave no reason to suspect that there are any issues.

ggbarplot(dat,x="parent.status",y="online.child",
          add="mean_se", ylab="Restrict child's online behavior",
          xlab="Parenting status",ylim=c(1,7))

Looks like future parents plan on being more restrictive compared to how restrictive current parents plan on being. It might be that past parents are even less restrictive than either of the other two groups. I’m also thinking, though, that screen time wasn’t as much of a concern for many of these parents. Some of them are in their 60s or thereabouts, so when their kids were young, there might not have been as much “screen time” anxiety, at least not in the way we asked the questions.

summary(aov(dat$online.child~dat$parent.status))

##                    Df Sum Sq Mean Sq F value   Pr(>F)    
## dat$parent.status   2   52.8  26.399   17.86 2.43e-08 ***
## Residuals         948 1401.1   1.478                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 6 observations deleted due to missingness

The one-way ANOVA is statistically signficiant.

pairwise.t.test(dat$online.child,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$online.child and dat$parent.status 
## 
##         future  past   
## past    3.5e-08 -      
## present 0.00062 0.00141
## 
## P value adjustment method: bonferroni

And there is a statistically significant difference between each pair of group means.

Online medical information seeking

We included a lot of questions about people’s tendency to use the internet when interacting with healthcare providers. We adapted the qeustions from Seckin et al. (2021). We reworded some of the questions to make it more about parents and children, less about individuals seeking medical information for themselves. In some cases we dropped items altogether from the original set:

I seek information on the internet to diagnose my child/children
I use information from the internet to identify appropriate treatments for my child/children
I request medication from healthcare providers based on information gleaned from the internet
I purchase products recommended on the internet based on information I received on the internet
I often seek health or medical information on the internet for my child/children.
I consult the internet before seeing a healthcare provider
Information on the internet helps me to communicate more effectively with health providers during appointments
Information on the internet helps me to ask more informed questions to health providers
Information on the internet helps me to better understand what my health provider is telling me during appointments

There was nothing in the psychometric results to raise concerns.

ggbarplot(dat,x="parent.status",y="online.health.info",
          add="mean_se", ylab="Seeks online health information",
          xlab="Parenting status",ylim=c(1,7))

Looks like future parents might now plan on relying on the internet for guidance as much compared to people are are currently raising kids. Parents whose kids have moved out might use report using the internet less for their kids health info as well, but they might also be thinking of a time where the internet was less prevalent.

summary(aov(dat$online.health.info~dat$parent.status))

##                    Df Sum Sq Mean Sq F value Pr(>F)  
## dat$parent.status   2   11.7   5.837   3.883 0.0209 *
## Residuals         949 1426.5   1.503                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 5 observations deleted due to missingness

The one-way ANOVA is statistically significant.

pairwise.t.test(dat$online.health.info,dat$parent.status,p.adjust.method="bonf")

## 
##  Pairwise comparisons using t tests with pooled SD 
## 
## data:  dat$online.health.info and dat$parent.status 
## 
##         future past 
## past    1.000  -    
## present 0.041  0.174
## 
## P value adjustment method: bonferroni

The past and future parents are not different from each other, neither are the past and present parents. Present parents do, however, tend to report higher levels of internet health seeking behaviors for their kids compared to past parents. This, I think, is really going to come down to age differences and how the internet wasn’t as prevalent during certain time periods.

Distrust in healthcare system

The following questions (from Shea et al., 2008) were used to measure levels of distrust in the health care system.

R The health care system does its best to make patients’ health better
The health care system covers up its mistakes
R Patients receive high quality medical care from the Health Care System
The health care system makes too many mistakes
The health care system puts making money above patients’ needs
R The health care system gives excellent medical care
R Patients get the same medical treatment from the health care system, no matter what the patient’s race or ethnicity
The health care system lies to make money
The health care system experiments on patients without them knowing

The psychometrics looked good, suggesting that each of these questions are consistently measuring a single underlying construct.

Distrust in the healthcare system will probably have all kinds of interesting relationships with other variables. For now, though, I just tested whether it’s correlated with age.

cor.test(as.numeric(dat$age),dat$distrust.hc)

## 
##  Pearson's product-moment correlation
## 
## data:  as.numeric(dat$age) and dat$distrust.hc
## t = -3.774, df = 952, p-value = 0.0001706
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.18346860 -0.05839089
## sample estimates:
##        cor 
## -0.1214117

It is. As people get older, they tend to have less distrust in the healthcare system.

plot(jitter(dat$distrust.hc)~as.numeric(dat$age),ylab=
       "Distrust in healthcare system",xlab="Age")

Reactance

“Reactance” might be new to some readers. It’s very hot in my neck of the woods, where we study ideologies, weird beliefs, conspiracy ideation, etc. Reactance is the degree to which you dislike having your actions constrained. You don’t like being told what to do. (“Don’t tread on me!”) It’s usually measured with the following items (I don’t have the reference on me at the moment).

I become angry when my freedom of choice is restricted.
I become frustrated when I am unable to make free and independent decisions.
I am contented only when I am acting of my own free will.
The thought of being dependent on others aggravates me.
When something is prohibited, I usually think “that’s exactly what I am going to do.”
Regulations trigger a sense of resistance in me.
I find contradicting others stimulating.
It disappoints me to see others submitting to society’s standards and rules.
When someone forces me to do something, I feel like doing the opposite.
I resist the attempts of others to influence me.
It makes me angry when another person is held up as a role model for me to follow.
I consider advice from others to be an intrusion.
Advice and recommendations usually induce me to do just the opposite.
It irritates me when someone points out things which are obvious to me.

Naturally, since reactance is your tendency to not want to be “tread on”, I wanted to see if it correlated with political ideology:

cor.test(dat$reactance,dat$pols)

## 
##  Pearson's product-moment correlation
## 
## data:  dat$reactance and dat$pols
## t = 7.0433, df = 937, p-value = 3.633e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1625910 0.2841343
## sample estimates:
##       cor 
## 0.2242344

It is. The more conservative someone describes themselves as, the higher in reactance they are. (Note: “Political affiliation” below ranges from “Very liberal” on the left side of the x-axis to “Very conservative” on the right side.)

plot(jitter(dat$reactance)~jitter(dat$pols),ylab="Reactance",xlab="Political affiliation")

Other variables

There are a host of other variables, mostly demographics, included in the survey. Contact me for the data set and/or survey questions if you’re interested.

Weird Parents: A preliminary report