Psychological assessments are kind of like movies. To many of us, we only see the finalized product, overlooking the extensive effort and dedication invested in their creation. With a rich history, these assessments remain subjects of ongoing scientific research worldwide. Today, we’re going to explore this history and the scientific properties that define psychoeducational assessments.
What Are Psychoeducational Assessments?
Psychoeducational assessments are a comprehensive, systematic process used to diagnose, set goals, and suggest treatment by collecting information about an individual’s behaviour, learning profile, strengths, and needs (Haywood et al., 1990). Specific areas explored often include one's reasoning, academic abilities, executive functioning, memory, intellectual ability, and attention skills. A psychoeducational assessment
is helpful in gathering a comprehensive picture of one’s overall profile.
Where Did Psychoeducational Assessments Originate?
Modern psychoeducational assessments, like all other types of assessments, trace their origins back to Sir Francis Galton, the founder of psychometrics in the 19th century (Michell, 2022). Galton was interested in scientifically measuring psychological properties and viewed this task as fundamental to the field of science.
One of the first psychological properties that interested Galton and other influential researchers at the time was the Intelligence Quotient (IQ) (Craig, 2017). IQ became a pivotal concept in understanding cognitive abilities and was foundational in the development of psychometric assessments. Galton's work laid the groundwork for later advancements in assessing intellectual capabilities and continues to be influential in the field of psychology. While not the sole component of psychoeducational testing, modern IQ testing remains a cornerstone of psychoeducational assessments today.
Galton faced the challenge of measuring and quantifying something intangible: cognitive functions. Unlike physical phenomena like a fever, which is defined by a specific temperature (38°C or higher), cognitive functions are more abstract. Determining what intelligence consists of and how individuals compare to others in the population posed several significant questions.
Did you know that defining IQ has been a long-standing debate among researchers (Sternberg, 2018)? Back in the day, Francis Galton believed intelligence was mainly about physical abilities—how keen one's senses were and how quick their reaction time was. On the other hand, Alfred Binet and later psychologists like David Wechsler focused more on cognitive abilities—like problem-solving, reasoning, and how well one handles educational challenges. Then came Charles Spearman, who argued that all aspects of IQ boiled down to one major factor: the g factor. Louis Thurstone, however, pushed back against this idea, suggesting that IQ encompasses multiple factors such as verbal and visual skills, among others. These historical debates continue to influence how we measure and understand intelligence in various contexts today. For a deeper exploration of these theories, see Sternberg (2018).
This is where the science of psychometrics comes into play—a branch of psychological study and practice dedicated to assessments aimed at measuring diverse aspects of human behavior and mental attributes (American Psychological Association, 2018). In the upcoming paragraphs, we'll delve into several scientific principles that enable psychologists to accurately measure these complex functions. But, before we do, let’s take one last look at the history of one popular psychometric tool: the IQ test.
When Were The First IQ Tests Created?
The Binet-Simon Intelligence Scale. This was the first IQ test, created by Alfred Binet and Théodore Simon in 1905 (Boake, 2002). These researchers were tasked with identifying children in France who might experience difficulty in school. To address this, they developed the test to identify these children by focusing on practical cognitive functions such as attention, problem-solving, and memory.
Stanford-Binet IQ Test. This was the next IQ test, developed by Lewis Terman in 1916 (Wasserman, 2018). Terman created this IQ test as an adaptation of the Binet-Simon Intelligence Scale. The difference was that Terman determined individuals' scores by dividing their mental age by their chronological age and multiplying by 100, resulting in their IQ score.
Wechsler- Bellevue Intelligence Scale. This was the next intelligence test to appear, developed by David Wechsler in 1939 (Silva, 2008). Over the years, it has undergone numerous revisions and remains in use today as the Wechsler Intelligence Scale – IV (WAIS-IV). It was the first intelligence test standardized using adults and utilized a normal distribution in scoring, represented on a bell curve.
How Do We Know That The Instruments Used Actually Measure What They Are Supposed To?
Great question! Clinicians always ensure the validity of their measures. Validity is the ability of an instrument to measure what it intends to measure (Trochim, 2001). It’s a crucial property because using a tool that doesn't measure the intended construct defeats the purpose. Researchers are interested in several types of validity, including:
Construct Validity. This is the degree to which a test measures the theoretical idea or concept it is supposed to measure (Cronbach, 1955).
Example: Imagine an IQ test. Construct validity asks if this test really measures intelligence as it claims to.
Convergent Validity. This checks if measures that should be related are actually related (Cronbach, 1955).
Example: Let’s say we develop a new test to measure IQ. To check for convergent validity, we compare scores from our new test with scores from a well-established IQ test, like the Weschler Adult Intelligence Scale (WAIS-V). If our new test scores are similar to the WAIS-V scores, it shows that our test is accurately measuring IQ. It’s like making sure your new thermometer shows the same temperature as a trusted thermometer. If both show similar readings, you can trust your new thermometer works well.
Content Validity. This is the extent to which a test represents all parts of a given construct (Kline, 2000).
Example: Suppose with our new IQ test, we want to assess various cognitive abilities such as problem-solving, verbal reasoning, and spatial awareness. To establish content validity, you would ensure that your test includes a balanced representation of questions that tap into each of these cognitive domains. If the test comprehensively covers these aspects, it demonstrates strong content validity for measuring IQ.
Criterion-Related Validity. This is the extent to which a test’s scores correlate with an external criterion (Kilne, 2000).
Example: Let's revisit our new IQ test. If our test demonstrates strong criterion-related validity, high scores on our IQ test should correspond to high scores on a predictive behaviour, such as academic performance in school (Roth et al., 2015). It should be noted that while IQ scores can predict certain outcomes, such as academic performance, they do not determine an individual's performance in school. Other factors, including motivation, study habits, and educational support, also play significant roles in academic success.
How Do We Know That The Instruments Used Can Be Trusted And Consistent?
Another great question! Clinicians ensure reliability of the measures they use. Reliability refers to the consistency and stability of a measurement tool or instrument (Kilne, 2000). It is important because it ensures that the measurements obtained are dependable and consistent over time and across different conditions. This consistency allows researchers, clinicians, and educators to trust the accuracy of the data and conclusions drawn from the measurements, establishing credibility in their findings.
One of the main types of reliability researchers are interested in is test-retest reliability. This is exploring the extent to which test scores remain consistent across time (Kline, 2000). It’s just like that thermometer we talked about before: if it consistently reads similar temperatures under identical situations, we can be confident it is accurate in its measurements. This reliability is crucial in IQ testing. Ideally, if individuals take IQ tests on two separate occasions and score similarly each time, it suggests that the test is reliably measuring their cognitive abilities without significant fluctuations due to other factors. This consistency allows psychologists and educators to make confident assessments of intellectual strengths and weaknesses over time.
Did you know that cognitive profiles remain stable over time? Researchers investigate this through longitudinal studies that track participants for years at a time. A study conducted in Germany followed children aged 4 through 12 for thirteen years, periodically testing their IQ scores. They found that IQ remained relatively consistent over time, indicating that cognitive profiles tend to maintain stability in the long term (Roth et al., 2015). For a comprehensive review of the literature on this topic, refer to Whitaker (2008).
How Do We Know Where Individuals Compare To Others In The Population?
This question can be answered by exploring the process of norming or standardizing psychometric tests. This involves administering the test to a large, representative sample to establish average scores (norms) (Hubley & Zumbo, 2013). These norms serve as benchmarks against which individual scores are compared. For example, if an IQ test is normed on a population, a score of 100 represents the average intelligence level for that group. Psychologists use these norms to interpret how an individual's test score compares to the average, providing insights into their relative strengths and weaknesses in cognitive abilities. This process helps ensure fair and accurate assessments across diverse populations and allows for psychologists to see where individuals stand in comparison to the rest of the population.
To visualize this a little better, let's take a brief look at the bell curve. This is a common statistical representation that psychologists use to see where an individual's IQ score compares to others in the population. Right in the middle of the bell curve is the average score for the population. As we move to the left or right, it moves away from the average.
Green = Average. This is where we want most scores to fall into.
Yellow + Red = Below Average. These scores indicate greater challenges.
Blue + Purple = Above Average. These scores indicate greater strengths.
What About Factors Such As Age, Ethnicity, And Gender? How Are These Accounted For When A Measure Is Used?
It wouldn’t be fair to judge a marathon runner's performance by the standards set for sprinters, just like it wouldn’t be fair to judge a child’s IQ against an adult. Fortunately, researchers have considered this when developing norm groups for psychometric tests. These tests are typically normed against peers of the same age, and the samples are randomly selected, ensuring that age, ethnicity, and gender are appropriately represented in their scoring and interpretation. This allows for fair comparisons between individuals and the population average.
What Happens When The Standardizing Sample No Longer Reflects The Current Population?
I know what you’re probably thinking: “If the standardizing sample becomes outdated, how can we ensure that tests remain accurate and relevant in our changing world?” You are right, generational shifts do occur. I’m sure you can think of a way or two that you are quite different than your grandparents’ generation. This is a great question and can be answered using the example of the Wechsler Intelligence Scale for Children (WISC).
The WISC was first developed in 1949 as an extension of the adult IQ test at the time, the Wechsler Bellevue (Niolon, 2005). Since then, it has gone through three revisions: the WISC-R in 1974, the WISC-III in 1991, and finally the WISC-IV in 2003, which is still in use today. A significant reason for these revisions is to better reflect the changing demographics and cognitive demands of the population. Each revision has updated the norms to account for population changes in IQ, removed culturally biased items, and added new subtests to better assess various cognitive abilities. For instance, the WISC-IV introduced improvements in assessing fluid reasoning, working memory, and processing speed, ensuring the test remains relevant and accurate in measuring children's intelligence in a modern context. In essence, the population that the WISC was first normed on in 1949 is no longer applicable to today’s day in age. Nowadays, children face different educational and environmental challenges, requiring these updates to maintain the test's validity and reliability for our children today.
There’s So Much More To Learn!
Although this is only a snippet of the history and science behind psychological testing, taking the time to learn about these topics can help us appreciate the work that researchers have done to advance the field of psychology. By understanding the complexities of validity, reliability, and norming, we gain insight into how psychologists measure and understand human behaviour and cognition. There's still so much we need to talk about, such as percentile ranks, standard scores, and more, that we will address in another blog. So, stay tuned to learn more about these crucial elements that shape the world of psychological assessment!
References
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