Multiple linear indicators A better scenario, but one that is more challenging to use, is to work with multiple linear indicators. Example: Attraction

attraction heart ratetalkingphone calls We assume that when someone is attracted to someone else (a latent variable), that person is more likely to have an increased heart rate, talk more, and make more phone calls (all observable variables). let’s assume an interval scale ranging from –4 (not at all attracted) to + 4 (highly attracted)

attraction heart beat x10 attraction talking attraction phone calls We assume that each observed variable has a linear relationship with the latent variable. Note, however, that each observed variable has a different metric (one is heart beats per minute, another is time spent talking). Thus, we need a different metric for the latent variable.

Allow the lowest measured value to represent the lowest value of the latent variable Allow the highest measured value to represent the highest value of the latent variable The line between these points maps the relationship between them Latent Observed

attraction heart beat x 10 attraction talking attraction phone calls Now we can map the observed scores for each measured variable onto the scale for the latent variable. For example, the observed heart rate score of 120 maps onto an attraction score of 2. Ten-minutes of talking maps onto an attraction score of zero. Thirteen phone calls maps to a high attraction score of 3. (Russ on The Bachelorette)

attraction heart beat attraction talking attraction phone calls This mapping process provides us with three estimates of the latent score: 2, 0, and 3. Because we are trying to estimate a single number for attraction, we can simply average these three estimates to obtain our measurement of attraction. In this example: ( )/3 = 5/3 = 1.67 (somewhat attracted)

Multiple linear indicators Advantages –By using multiple indicators, the uniqueness of each indicator gets washed out by what is common to all of the indicators. ( example: heart rate and running up the stairs ) Disadvantages –More complex to use –There is more than one way to scale the latent variable, thus, unless a scientist is very explicit, you might not know exactly what he or she did to obtain the measurements.

Multiple linear indicators: Caution When using multiple indicators, researchers typically sum or average the scores to scale people on the construct Example: (time spent talking + heart rate)/2 = attraction Person A: (2 + 80)/2 = 82/2 = 41 Person B: ( )/2 = 123/2 = 62

Multiple linear indicators: Caution This can lead to several problems if each manifest variable is measured on a different scale. First, the resulting metric for the latent variable doesn’t make much sense. Person A: 2 minutes talking + 80 beats per minute = 41 minutes talking/beats per minute???

Multiple linear indicators: Caution Second, the variables may have different ranges. If this is true, then some indicators will “count” more than others.

Multiple linear indicators: Caution Variables with a large range will influence the latent score more than variable with a small range PersonHeart rate Time spent talking Average A80241 B80342 C D * Moving between lowest to highest scores matters more for one variable than the other * Heart rate has a greater range than time spent talking and, therefore, influences the total score more (i.e., the score on the latent variable)

Mapping the relationship by placing anchors at the highest and lowest values helps to minimize this problem Preview: Standardization and z-scores Latent Observed

Some more examples Let’s work through a detailed example in which we try to scale people on a latent psychological variable For fun, let’s try measuring stress: Some people feel more stressed than others Stress seems to be a continuous, interval- based variable What are some indicators of stress?

Some possible indicators of stress Hours of sleep Number of things that have to be done by Friday

Operationalizing our indicators We can operationally define these indicators as responses to simple questions: –“Compared to a good night, how many hours of sleep did you lose last night?” –“Please list all the things you have to accomplish before Friday—things that you can’t really put off.” Note that each of these questions will give us a quantitative answer. Each question is also explicit, so we can easily convey to other researchers how we measured these variables.

Regarding Exam 1 If you missed the question on the limitations of medical research—a question that supposedly came from the readings—you will need to do the following to get credit for the question: On Wednesday, turn in to me a sheet of paper that includes (a) your name, (b) your alias, and ( c) a mention of the problem (just a sentence). When the end of the semester arrives, if that question “makes or breaks” your grade, you will be given the extra point. If not, we will ignore it. I must receive the paper no later than this Wed.

Latent: Stress Level Observed: Hours of Lost Sleep Operationally defining the latent variable

Latent: Stress Level Observed: Things to do Operationally defining the latent variable

Estimating latent scores PersonIndicator 1 (hours lost sleep) Latent score estimate 1 Indicator 2 (to do list) Latent score estimate 2 Averaged latent score Prof. Fraley Tina Safa d e

Summary Recap of what we did –Determined the metric of the latent variable –Identified two indicators of the latent variable –Mapped the relationship between the latent variable and each observed variable –Using this mapping, estimated the latent scores for each person with each observed variable –Averaged the latent score estimates for each person

Multiple linear indicators By mapping the measured variables explicitly to the latent metric, we can avoid some of the problems that emerge when variables are assessed on very different metrics

Multiple linear indicators When the indicators are on the same metric (e.g., questionnaire items that are rated on a 1 to 7 scale), the process of estimating the latent score is easier, and researchers often use the manifest metric as the latent metric and average the observed scores to obtain a score on the latent variable.