Toddlers Diverged from Honey Badgers

This article provides preliminary, observational data suggesting that toddlers may have diverged from honey badgers. Most evidence is based on behavior characteristics, but some structural similarities were found. Molecular comparisons were not made because of lack of funding and equipment (and a healthy respect for my life). Further evidence is needed to substantiate this claim.

Field Notes

Table 1. Comparison of Honey Badger and Toddler Behavioral and Structural Characteristics

Badger Characteristic	Toddler Shares Characteristic
	Yes	Maybe		No
Behavior
Vocalize by whining	Their vocalizations begin with soft, high, pleasant tones. As the toddler's rights are violated (e.g., changed diapers, broken crackers, unsolicited eye contact) vocalizations become shrill and surprisingly loud. These sounds are typically coupled with flailing arms, dead weight, and evil stares.
Mostly solitary	Interacts occasionally with others of its kind. Interactions may be positive or negative depending on the territory, if cookies are present, and the toys available.
Diurnal & Nocturnal		Individuals within this species tend to vary. Some sleep primarily during the day while other sleep at night. Regardless, getting the toddler to sleep is exceptionally difficult. I’ve observed that rubber chewing objects are great for pacification; however, many toddlers are no longer using them due to threats from dentists. Dentists obviously haven’t seen what I have.
Strong, ferocious, and tough	They will fiercely and savagely defend their territory by attacking any animal (or inanimate object) that displeases them. I witnessed one individual chase down a group of aggressive domestic geese trying to take back his “crackers”.
Omnivore		I believe it has the capacity to eat meat, I just haven’t witnessed it yet. Primary diet is milk, cookies, and gold fish.
Can run quickly for short periods of time	They have a stealth-like run - both quick and quiet. This is an adaptive behavior. The parents stay busy chasing them around the house and are too tired to reproduce; therefore decreasing future competition for resources.
Capable of using tools		Tools are only used when in best interest of the toddler. For example, they are more than willing to use a screw driver, even at risk to their own safety, but they may be unwilling to use a fork when eating.
Intelligent	Caution must be taken while doing any action within eye-sight of a toddler. They easily learn skills without being taught. While some skills, like putting up their laundry, are cute and adorable, other skills, like throwing EVERYTHING away, including a new smart phone, are not.
Structure
Short, fat bodies with short legs	In addition, toddler has an exceptionally large, protruding belly - possibly due to the disproportionate consumption of simple carbohydrates (specifically cookies and gold fish).
Jaw structure adapted for holding prey	They bite their prey (typically parents and other small children) and hold on with the utmost tenacity – withstanding all forms of threats and bribery.
Teeth display signs of irregular development	Teeth can erupt as early as 4 months or as late as 1 year. Teeth are typically snaggled and gnarly in appearance with a large gap between each tooth. Regardless of maturation, teeth tend to erupt at the most inopportune times and usually results in alcohol consumption of the parent.

Based on my field observations, toddlers shared 8 behavioral and 3 structural characteristics with honey badgers, suggesting that toddlers may have diverged from honey badgers. Some might suggest convergent evolution for these similarities, however, significant differences in environments would suggest otherwise.

Graphic from: Divine Secrets of a Domestic Diva

Expectations of Expecting

Sweetest Kid Ever!!!

We have one son. His name is Kallan. Although he is a little too spirited at times, and his will is proving to be stronger than mine (which I didn't think possible), I love him more than I can verbally express.  He is the bean in my burrito, the pecan in my sandie, and I couldn't imagine life without him. (Notice how both my love references revolve around food - ha!)

The unconditional love you feel as a parent is astounding. Before being initiated into parenthood, this love is anticipated, even expected, but it is something that has to be experienced to be fully appreciated. Our unconditional love for K has led us to the decision to have another child.

We are expecting!!!!

As you can imagine, I'm ecstatic. I'm nesting - designing a room full of bright colors and fun toys, planning for child care, and wondering how Kallan is going to react to being a big brother and having to share Mamma. I feel joy and peace about being a mom to another bright-eyed little boy or girl. The waiting and anticipation is hard, but it will all be worth it when we bring another Strovas home!

As much as I would love to give you a due date, I can't. We are adopting.

We are not adopting in the traditional sense of the word; we will be fostering a child with the intention of adoption. As you can imagine, this has the potential to produce immense joy, incredible heartbreak, or an emotional roller coaster of both. Just like traditional adoption, the foster-to-adopt program does not guarantee you a child. Under Texas law, we could foster a child for up to 18 months before being given the option to adopt.

With adoption comes other conditions that you cannot control, for example, the state of the mother during pregnancy or the treatment of the child after birth. We may get a baby that is perfectly healthy with no signs of withdraw, abuse or neglect, or we may not. The uncertainty of adoption is unsettling, but we are trusting that God has the right child for us.

Even though the risk is high, so is the payoff - for us as well as the child. Below I have summed-up our reasoning for adoption.

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Why Adopt?

1. We want to extend our family of three to a family of four.

After all, three is a weird, uneven number. Someone always has to ride the roller coaster alone; there is an empty, expectant seat at the restaurant table. We really don't want to deal with these inconveniences for the rest of our lives, so it's only logical to have another child.   ;)

2. We want to give a good home to a child who would otherwise grow up in the foster-care system.

I'm not stating that the foster-care system is bad, in fact, it's quite the opposite. It is designed so that children without a good support system can experience a life as close to "normal" as possible in an environment where they are loved, supported, and encouraged. Granted, this is the idealistic premise of the system, and some foster-care families may not live up to this standard, but most do.

Throughout the last couple of months we've had the honor of meeting a few of Buckner's foster-care families and they are amazing! Giving a good home to children whose parents' are working on getting their lives back together is an admirable calling, and these families put their hearts on the line to help out children in need. Foster-care families are doing a great service for our communities by taking care of these innocent children.

Foster-care is designed to be temporary, not permanent. This is where Shawn and I come in. We can offer a good home to children whose parents are not able to get their lives back together, and therefore cannot lawfully take care of their children.

3. I've always felt that adoption would be in my future, and Shawn was open to the possibility. 

It's funny the things people remember. When I told my brother and my best friends that Shawn and I were going through the process to adopt, they weren't that surprised. They remembered me talking about adoption, even as far back as high school. I always had a feeling that adoption would be in my future, but I didn't realize those closest to me knew it too.

Shawn and I have talked about adoption from the very beginning. We both wanted a biological child, but were open to the possibility of adoption. I wouldn't say that it was an automatic decision to adopt child #2, in fact, we were trying for a few months to have another biologically. But, after a lot of soul searching and praying, we felt that God was drawing us toward adoption. We know that LOVE makes a family and that we could be blessed by a child that was not of my womb.

4. God adopted us.

Now let me start with the fact that I did not come up with this realization on my own. I was reading the websites of various adoption centers in Lubbock and came across Buckner's philosophy on adoption. It's a beautiful picture of us coming into the family of a loving father. If you'd like to learn more about this philosophy, read Buckner's devotional guide.

 

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Some people find it odd that we would choose to adopt a child when we can have children of our own. To be honest, before we started this process I assumed that most people who adopted had unresolved infertility issues. However, that is not the case. There are thousands of families just like ours that are choosing not to have additional children biologically, but to extend their family by adopting children who become wards of the state. In fact, we just went through two months of training with four families who actively decided to adopt through foster-care and had no issues with infertility. Three of those families had biological children.

At this time, we feel like adoption is the best option for extending our family. Even though this process will be difficult, I know that we will be blessed beyond measure by the child who comes into our home. He/she will change our lives forever, just as Kallan has. 

What is Structural Equation Modeling (SEM)?

If any of you are like me, you're excited about the idea of using a structural equation model to analyze your data, but completely overwhelmed with its complexity. I'm hoping that this post will give you a brief introduction along with a few sources to help you develop the skills and knowledge to apply this statistical procedure to your own research. Good luck!


What is Structural Equation Modeling?

Structural equation modeling (SEM) is a statistical technique used to assess whether a proposed model (which is a set of specified causal and noncausal relationships among variables) accounts for the observed relationships we see in empirical data (Savalei & Bentler 2006). This involves the use of factor analysis, path analysis, measurement models, and structural models (Stoelting 2002). Factor analysis deals with constructs (AKA latent variables, factors, concepts) that cannot be directly measured but are related to measurable variables. For example, intelligence is a vavriable that cannot be measured directly but it can be determined through a series of questions or tests. Path analysis is a technique used to identify causal relationships between directly measured variables (Klem 2000). Measurement models deal with relationships between measured variables and latent variables (like a factor analysis). Structural Models deal with relationships between latent variables only (Stoelting 2002). The goal of SEM is two-fold: to obtain estimates of the parameters of the model (i.e. the factor loadings, the variances and covariances of the factor, and the residual error variances of the observed variables), and to assess the fit of the model (i.e. to assess whether the model itself provides a good fit to the data) (Hox & Bechger 2001).

Many books and articles on SEM discuss its ability to determine causal relationships; however, the data used to determine these relationships is correlational. Remember learning in your basic stats class that correlation does not equal causation? Well, that idea also applies with SEM. Testing a single model does not validate causality, it must be compared to competing models and used in multiple samples. Just because a single model fits the data does not mean that it has been proven "true" (Hox & Bechger 2001).

How to read a model created through SEM (Klem 2000, Hox & Bechger 2001, Stoelting 2002, Savalei & Bentler 2006)

• Variables that are incased in rectangles represent variables that can be measured directly and are called indicators.
• Variables that are incased in circles represent latent variables that cannot be measured directly and therefore are the product of some kind of instrument or measurement tool. These latent variables are abstract concepts and refered to as factors or constructs.
• Single headed arrows represent regression coefficients and indicate a hypothesized pathway (causal relationship) between two variables. The variable at the tail of the arrow causes the variable at the point. 
• Double-headed arrows represent covariances and indicate the relationship between two variables or error terms. These relationships are non-directional and therefore not causal.
• Error terms represent the variance within a single variable. These are depicted by circles (since they are not directly measured) or simply represented by an arrow pointing toward the variable.

We can further break down unmeasurable (latent) variables into two subcategories: exogenous and endogenous. Exogenous factors are those that the model does not try to explain, and arrows will point away from these factors. Endogenous factors are completely opposite, they are affected by one or more of the latent variables and arrows will be pointing towards these variables. In SEM, you can also have error variables which are exogenous variables within the model that include the effects of omitted variables along with the effects of measurement error (Klem 2000).

There are 8 types of parameters that can be estimated using structural equation modeling. Four of those parameters are direct effects and these effects are analogous to the coefficientts used inn multiple regression (beta values) (Klem 2000):

• the effect of an exogenous factor on a measure variable (e.g. effect of locus of control on plan unhappy)
• the effect of an endogenous factor on a measured variable (e.g. the effect of self esteem on worth)
• the effect of an exogenous factor on an endogenous factor (e.g. the effect of locus of control on self esteem)
• the effect of an endogenous factor on an exogenous factor (e.g. the effect of self esteem on overall satisfaction)

The other four parameters distinguished by SEM are all variances or covariances (Klem 2000):

• the variance and covariance of unmeasured variables (e.g. the curved double headed arrow linking locus of control and loneliness)
• the variance and covariance of unmeasured variables which represents error - this is interesting because it is unexplained variance (e.g. the variance of the unmeasured variable in the top left corner - the oval - its the variance of plan unhappy that is not explained by locus of control)
• variance/covariance of errors in measured dependent variables
• variance/covariance of errors in measured independent variables

This diagram should help you understand both the variables and parameters used in SEM

Two assumptions for SEM

1. the variables on which the matrix coefficients are based are intervally scaled
2. the variables have a multivariate distribution

These assumptions can be hard to meet within social science research but maximum likelihood, the estimation method most commonly used in SEM, is robust to violations of normality. Currentt SEM software provides possible remedies for unmet assumtions (Klem 2000).


Sample Size and SEM

Sample size is important to consider when conducting a SEM. The necessary sample size for reliable results depends upon the complexity of the model, the magnitude of the coefficients, the number of measured variables associated with the factors, and the multivariate normality of the variable distributions - more cases are needed for complex models, models with weak relationships,\ models with few measured variables per factor, and nonnomral distributions. The input matrix should be based on at least 150 cases, and at least 5-10 cases per parameter estimated. It is recommended to have 10 cased per parameter if the variables do not have a multivariate normal distributionm (Klem 2000).

How to estimate parameters in SEM (Klem 2000)

1. Create a model based on the literature. If there are competing models as indicated by the literature, then they should be specified. This can be done by completing a simple diagram and indicating factors, indicators, and all relationships.
2. Obtain parameter estimates for the model - the eight parameters discussed above that involve coefficients of direct effects and variances or covariances of unmeasured variables. For this step, you must use statistical software to calculate the relationships

How to evaluate results (Klem 2000).

1. Make sure the results fit statistical and theoretical criteria. Any model tested by SEM should be based on theory. After the parameters have been estimated by the statistical program, each paramete should be assessed from a theoretical perspective, for example, the signs and magnitudes of thee coefficients should be consistent with what is know from the literature and previous research. Results should be theorettically sensible.
2. Determine the identification status of the model. IF the model is considered "identified" then there is a unique solution for each parameter in the model.
3. Check if parameters are reasonable. A model that is misspecified can results in improper results such as negative variances and correlations greater than one.
4. Check to see if the data fits the model.

References:
*Please note that not all of the references listed below are the best resources. I simply used information that was presented in a simple and easy to understand format. If you plan on publishing your research, I would suggest finding better sources.

Hox, J.J. & Bechger, T.M. (2001). An introduction to structural equation modeling. Family Science Review. 11:354-373.

Hoyle 1995 Structural Equation Modeling

Klem, L. (2000). Structural equation modeling. Grimm, Laurence G. (Ed); Yarnold, Paul R. (Ed), (2000). Reading and understanding MORE multivariate statistics., (pp. 227-260). Washington, DC, US: American Psychological Association.

Savalei, V. & Bentler, P.M. (2006). Structural Equation Modeling. In: The Handbook of Market Research: Uses, Misuses, and Future Advances. Edited by R. Grover & M.Vriens. Sage Publications

Stoelting, R.  (2002). Webpage retrieved April 15, 2012 from: http://userwww.sfsu.edu/~efc/classes/biol710/path/SEMwebpage.htm

Reliability and Validity - Developing a Good Instrument.

There are a lot of factors that go into developing a good instrument, like the choice of items and measurement scales, using the appropriate sample for pilot testing, implementing statistical techniques that fit the characteristics of your data, etc. Testing your instrument for reliability and validity can help to assess if your instrument is "good" or "bad" and ultimately help you to know if the interpretation of your data is accurate or misleading.

Testing for reliability and/or validity is not a simple process, and can take years of implementation in different samples to determine. Below, I will list the different types of reliability/validity and how they are assessed.

It's important to remember that reliability is a necessary but NOT sufficient condition of validity. A necessary condition of a statement must be satisfied for the statement to be true and a sufficient condition is one that, if satisfied, assures the statement's truth. In other words you can have an instrument that is reliable but not valid. However, if your instrument is valid, then it HAS to be reliable. It reminds me of the old adage that states: "all poodles are dogs, but not all dogs are poodles". It's the same thing in this case: all valid instruments are reliable, but not all reliable instruments are valid. 


 Definitions:

Reliability - the degree to which an instrument consistently measures whatever it intends to measure. In other words, it's the statistical measure of the reproducibility or stability of the data gathered by your survey.

Validity - the degree to which an instrument measures what it is suppose to measure. If your instrument is valid then you can feel confident in the interpretation of the data. Going back to the adage above, a valid instrument must also be reliable, but what does that mean? Here is another way to interpret it:

Precision (reliability) + Accuracy = Validity

If you spent any time studying the sciences, you were sure to come across precision and accuracy. Although the differences of these terms are clear to me now, they seemed very ambiguous when I was in Chemistry 101, so I'll use the bulls-eye analogy to explain. Precision, AKA reliability, is when all your "hits" are clustered in the same area (the degree to which repeated measures under unchanged conditions show the same result). Accuracy is when all your "hits" are close to the bulls-eye (how close the measurements are to the actual value). With both of these properties together, you have a validity (your "hits" are clustered together around the bulls-eye).
 

Image from: Precise Positioning and Targeting

Types of Reliability:
 
Test-Retest (stability) - the degree to which scores on the same test are consistent over time, in other words, the questions are worded in such a way that cause the respondents to consistently answer the same way. To test this, you would implement the survey to a sample twice and then calculate the correlation coefficients (r) to compare the two sets of responses. Correlation coefficients are considered good if they are 0.70 or above, indicating that the responses are reasonably consistent from one point in time to the other. The trick is determining the amount of time to wait in between implementation. Two weeks is suggested as a good amount because it's long enough for the respondents to forget their answers, but short enough so that they don't gain knowledge or change behaviors before the second survey.

• Intraobserver (intrajudge) - measures the stability of responses from the same person. This is a type of test-retest reliability because it looks at a single individual's score over a period of time. It is also measured using correlation coefficient.

Alternate Form (equivalent-form) - the degree to which two similar forms of a test produce similar scores from a single sample. The two instruments will have the same structure, number of items, reading level, difficulty level, etc; however, each item is not the same. Items differ in wording, but still measure the same idea. You can do this by implementing the survey in two separate samples of the same population, or implement the survey twice in the same sample (as a pre and post-test). Correlation coefficients (r) are compared and high values indicate good alternate-form reliability.

Internal Consistency  - indicates how well different items measure the same issue. It is applied to a group of items that are thought to measure different aspects of the same concept. This is important when measuring the reliability of latent constructs because a single item will not be able to assess concepts such as knowledge, behavior, and attitude. Below are two commonly used methods to assess internal consistency.

• Split-Half - measures internal consistency by comparing two parts of a single instrument. Divide the instrument (or construct items) into two halves, compute each respondent's score on the two halves, correlate both scores. High correlation coefficients indicates high internal consistency.
• Cronbach's Alpha -indicates how well the other items complement each other according to a single construct. This can be used for dichotomous items or longer measurement scales like the Likert scale. High CA values indicate high internal consistency. If your instrument only involves dichotomous responses (e.g. yes or no) then the Kuder-Richardson 20 or KR-20 is another option for this statistic.

Interobserver (interjudge/interrater) - measures how well two or more evaluators agree in their assessment of a variable. It refers to the consistency of two or more independent observers and is usually reported as a correlation coefficient. This type of reliability is used in qualitative studies like interviews, focus groups, or open ended surveys.


Types of Validity:

Face - involves the feedback of untrained reviewers. If you were to categorize validity testing into stages, this would be the first one. You're looking for incorrect spelling and grammar, ambiguous items, confusing layouts, etc. Untrained reviewers will focus on the overall aesthetics of the survey and not the content.


Content - the measure of how appropriate items or scales seem to a set of trained reviewers. Like the term suggests, you're looking at the content of the survey. The more people you can have to look over the survey, the better because each person will point out something different. This should be conducted after you check for face validity and can therefore be referred to as the second stage.

Criterion - the measure of how well one instrument compares to another. This is determined by relating the performance of your instrument to another instrument (the criterion against which the validity of your instrument is judged)

• Concurrent - the comparison of your instrument again another that is considered to be the gold standard for the variable in question. It is calculated using correlation coefficients and high values indicate good concurrent validity.
• Predictive- the degree to which a test can predict how well an individual will do in a future situation. For example, the GRE is suppose to be a good predictor of how well we will do in graduate school. However, I think that many of us will disagree with the GRE's predictive potential, but that's another subject for another time. :) Correlation coefficients are used to compare the initial score with the secondary outcome.

Construct - the degree to which an instrument measures a construct. This is the most important form of validity because it answers the question: is this instrument measuring what is was intended to measure? However, it is also the most difficult form of validity to understand, to measure, and to report.

• Convergent - implies that several different methods for obtaining the same information will provide similar results. Assessing convergent validity is similar to alternate form reliability but is more theoretical. This requires a great amount of work over a long period of time to determine.
• Divergent - measures the ability of an instrument to estimate the underlying truth in a given area. This is also very theoretical and requires a lot of time and work to determine.

References:

Litwin, M.S. (2003). How to assess and interpret survey psychometrics: the survey kit 2. Sage Publications: Thousand Oaks, CA.

Gay, L.R., Mills, G.E., Airasian, P. (2006). Educational research: competencies for analysis and applications. 8th Edition. Pearson Merrill Prentice Hall: Columbus, Ohio.

Definitions

I'm working on compiling definitions of all key terms that I will be using in my research:
environmental education
environmental literacy
experiential education
experiential environmental education

I will probably be changing these definitions ever so slightly as I analyze my data, but here is what I'm using so far along with their references.


Environmental Education
A method for increasing environmental literacy through learner-centered, hands-on experiences that challenge students’ analytical and problem solving skills concerning natural systems and humans’ role in those systems. It incorporates multiple disciplines (math, science, history, economics, political science, English, sociology, etc) along with feelings, attitudes, and perceptions in order to develop responsible citizens and environmental stewards (derived from the North American Association for Environmental Education 2004).


Environmental Literacy
The capacity of an individual to incorporate a broad understanding of how people and societies relate to each other and to natural systems in their daily lives. This requires sufficient awareness, knowledge, skills and attitude, along with the ability to incorporate appropriate environmental considerations into peoples’ lifestyle, career, and community (derived from Elder 2003).


Experiential Education
A philosophy and methodology in which educators purposefully engage with learners in direct experience and focused reflection in order to increase knowledge, develop skills and clarify values (Association of Experiential Education).


Experiential Environmental Education
Environmental education that is purposefully conducted in an outdoor environment (whether natural or man-made) in which students actively participate in a process of exploration and discover of environmental topics, processes, and issues. (This is a working definition that I derived from a qualitative study that I am currently working on. It may change as I analyze further data, but the major constructs are active participation, exploration and discovery, and outdoor environment.)


References:

Association for Experiential Education (AEE). Retrieved March 20, 2010 from: http://www.aee.org/.

Elder, James L. (2003). A field guide to environmental literacy: making strategic investments in environmental education. Environmental Education Coalition and North American Association of Environmental Education: Rock Spring, GA.

North American Association for Environmental Education (NAAEE). (2004). Environmental Education Materials: Guidelines for Excellence. Retrieved March 20, 2010 from: http://naaee.org/npeee/materials_guidelines/guidelines.pdf