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Measurement and Scales of Variables

This chapter discusses the different scales of measurement and the psychometric properties of good measurement, such as reliability and validity. It also explores the methods used to collect evidence of validity.

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Measurement and Scales of Variables

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  1. Chapter 5 Measuring Variables & Sampling

  2. Variable and Measurement • Variable – A condition/characteristic that can take on different values or categories • Measurement – the assignment of symbols or numbers to something according to a set of rules

  3. Scales of Measurement • Nominal Scale • use of symbols to classify or categorize • Ordinal Scale • rank-order scale of measurement • Allows to conclude who is 1st, 2nd, but not for how much! • equal distances on scale not necessarily equal on dimension being measured • Difference between 1st & 2nd = 20 secs; difference between 2nd & 3rd = 60 secs. • Laredo #1 Most Diverse City in the US,

  4. Entropy index (E): Index gauges how uniformly members of a population are spread. E = 0 = complete homogeneity or no diversity

  5. Scales of Measurement (cont'd) • Interval Scale • same properties of ordinal plus equal distances between adjacent numbers • e.g., temperature on Fahrenheit scale • Ratio Scale • highest scale of measurement • same properties of other scales plus absolute zero point • e.g., weight, height

  6. Psychometric Properties of Good Measurement • Reliability – Consistency or stability of the scores of your measurement instrument • Temperatures: 98.9, 98.6, 97.6. 97.8, 99.0 RELIABLE? • Temperatures: 98.0,100.3, 95.3, 103.0 RELIABLE? • Validity – Extent to which your measurement procedure is measuring what you think it is measuring and whether you have interpreted your scores correctly • A measure must be reliable in order to be valid but a reliable measure is not necessarily valid

  7. Types of Reliability • Test-Retest Reliability • consistency of individual scores over time • same test administered to individuals two times • correlate scores to determine reliability (high correlation?) • how long to wait between tests? • Equivalent-Forms Reliability • consistency of scores on two versions of test • each version of test given to different groups of individuals

  8. Types of Reliability (cont'd) • Internal Consistency Reliability • consistency with which items on a test measure a single construct • involves comparing individual items within a single test • coefficient alpha or Cronbach’s Alpha (> .70) is common index • eHORMONY CLAIMS that with 5 Questions you can be MATCHED • HIRES Social Psychology students.. Owner a social psychologist! • Interrater Reliability • degree of agreement between two or more observers • interobserver agreement is the percentage of times different raters agree

  9. Validity • Validity refers to the accuracy of the inferences, interpretations, or actions made on the basis of test scores. • The validity of test concerns what the test measures and how well it does so. It tells us what can be inferred from test scores (Anastasia & Urbina, 1997, p. 113) • Involves the measurement of constructs (e.g., intelligence or happiness) • Do operational definitions accurately represent construct we are interested in? • LEADERSHIP. How do you measure leadership? Multiple indicators? • ULTIMATELY: PREVIOUS RESEARCH!

  10. Methods Used to Collect Evidence of Validity • Content-Related Evidence (content validity): JUDGMENT of the degree to which the task(s) adequately represent the construct’s domain • validity assessed by experts • do items appear to measure construct of interest? (FACE VALIDITY: Do questions address issues related to LOVE? • were any important content areas omitted? Romantic, Friendship • were any unnecessary items included?... Animal, classroom love.. • Evidence Based on Internal Structure • how well do individual items relate to the overall test score or other items on the test: Different FACTOR LOADINGS? • factor analysis – statistical procedure used to determine the number of dimensions present in a set of items; Factors… Romantic…

  11. Methods Used to Collect Evidence of Validity (cont'd) • Evidence Based on Relations to Other Variables • criterion-related validity: How good the scores of TEST predict or relate to an existing test… : • predictive validity – using scores obtained at one time to predict the scores on a criterion at a later time • concurrent validity – degree to which scores obtained at one time correctly relate to the scores on a known criterion obtained at the same time • E.G., Administering a brand-new depression task that you created and at the same time ADMINISTERING THE BECK’S DEPRESSION INVENTORY… and the results of both SHOULD BE HIGHLY CORRELATED • EYETRACKING vs. Traditional behavioral measures (RT) • Run the experiment using eye-tracking and at the same time using RT Eye-tracking highly correlated to RT measures..

  12. Methods Used to Collect Evidence of Validity (cont'd) • Evidence Based on Relations to Other Variables • convergent validity – extent to which test scores relate to other measures of the same construct TRIANGULATION • Eye-tracking vs. Visual Moving window: Measure your eye movements as you read vs. pressing a word each time as you read the sentence.. • discriminant validity – extent to which your test scores do not relate to other test scores measuring different constructs: Test in Research methods…. Should not relate to therapy. • known groups validity evidence – extent to which groups that are known to different from one another actually differ on the construct being developed

  13. Sampling Methods • Sample – a set of elements selected from a population • Population – the full set of elements from which the sample was selected

  14. Sampling Methods (cont'd) • Sampling – process of drawing elements from population to form a sample • representative sample • equal probability method of selection method (EPSEM) • Statistic – a numerical characteristic of sample data • Parameter – a numerical characteristic of population data • Sampling error (Standard error) – difference between the value of the sample statistic and the value of the population parameter • You want a small sampling error? Use large N

  15. Random Sampling Techniques • Simple Random Sampling • choosing a sample in a manner in which everyone has an equal chance of being selected • sampling “without replacement” is preferred • Random numbers generators simplify the process or =rand()*n • Stratified Random Sampling • random samples drawn from different groups or strata within the population • If gender stratification variable: Separate groups into M F • Choose at random from the two groups! • proportional stratified sampling involves insuring that each subgroup in sample is proportional to the subgroups in the population • If F population = 60%, choose 60% from F

  16. Random number tablehttp://www.stattrek.com/statistics/random-number-generator.aspx

  17. Random Sampling Techniques (cont'd) • Cluster Random Sampling • involves random selection of groups of individuals (clusters) • Cluster: Collective type of unit including multiple elements: neighborhoods, schools, families • e.g., select at random 30 classrooms (from sampling distribution of 50: one-stage Cluster sampling). Include all subjects • Or select at random 10 subjects from the 30 classrooms (two-stage)

  18. Random Sampling Techniques (cont'd) • Systematic Sampling (similar to random sampling) • Involves three steps • (1) determine the sampling interval (symbolized by k) • (2) randomly select a number between 1 and k, and include that person in your sample: CHOOSE 5 • (3) also include each kth element in your sample 5th person + 10th (kth)= 15 So, subjects would follow the pattern: 5, 15, 25, 35, 45, and son on… (son increments of 10!) If in two (Choose 3): 3, 13, 23, 33, 43…

  19. Nonrandom Sampling Techniques • Convenience Sampling – using research participants that are readily available – e.g., college students • Quota Sampling – identifying quotas for individual groups and then using convenience sampling to select participants within each group

  20. Nonrandom Sampling Techniques (cont'd) • Purposive Sampling – involves identifying a group of individuals with specific characteristics – e.g., college freshmen who have been diagnosed with ADHD • Snowball Sampling – technique in which research participants identify other potential participants. • particularly useful in identifying participants from a difficult to find population

  21. Random Selection and Random Assignment • Random selection involves selecting participants for research • purpose is to obtain a representative sample • Random assignment involves how participants are assigned to conditions within the research • purpose is to create equivalent groups to allow for investigation of causality • 20 Participants assigned at random to Condition A • 20 Participants assigned at random to Condition B

  22. Determining Sample Size • If less than 100 use entire population • Larger sample sizes make it easier to detect an effect or relationship in the population • Compare to other research studies in area • Larger sample sizes are needed if population is • heterogeneous

  23. Determining Sample Size (Power) • If N < 100 use entire population • Larger N make it easier to detect an effect/relationship in the population • Compare to other research studies in area • Larger sample sizes are needed if population is • Heterogeneous, you have multiple groups • if you want to increase precision • when you expect a small effect • for some statistical techniques • if you expect a low response rate • ***when you use less efficient methods of sampling

  24. Sampling in Qualitative Research • Qualitative research focuses on in-depth study of one or a few cases. • Several different sampling methods are available. It is common to mix several different methods.

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