Chapter 13 Measurement Winston Jackson and Norine Verberg Methods: Doing Social Research, 4e

13-2© 2007 Pearson Education Canada Theoretical, Conceptual, and Operational Levels Measurement is the “process of linking abstract concepts to empirical referents” (Carmines & Zeller) Hence, one moves from the general (theoretical level) to the specific (empirical level) I.e., For each concept, an indicator is identified E.g., What is the best way to measure, or indicate, a person’s social prestige? The concepts we measure are called variables See Figure 13.1 (next slide)

13-3© 2007 Pearson Education Canada Figure 13.1 Levels in Research Design

13-4© 2007 Pearson Education Canada Figure 13.1 Explained Shows movement from the general to the specific – from the theoretical level to the operational level Referred to as operationalization At the theoretical level, concepts are conceptualized (e.g., socioeconomic status, alienation, job satisfaction, conformity, age, gender, poverty, political efficacy) At the operational level, the researcher must create measures (or indicators) for the concept Indicators should reflect the variable’s conceptual definition

13-5© 2007 Pearson Education Canada Assessing Indicators We assess the link between the concepts and the indicators by evaluating the validity and reliability of the indicators 1. Validity The extent to which a measure reflects a concept, reflecting neither more nor less than what is implied by the conceptual definition 2. Reliability The extent to which, on repeated measures, an indicator yields similar readings

13-6© 2007 Pearson Education Canada 1. Validity (in Quantitative Research) Illustration: concept – socioeconomic status Conceptual definition: a “hierarchical continuum of respect and prestige” Operational definition: annual salary Assessment: Low validity (salary might not capture prestige – widows, ministers, nuns – prestige and respect would be higher than income suggests Measure should be congruent with conceptual definition (e.g., use a prestige scale)

13-7© 2007 Pearson Education Canada Types of Validity Face validity: “on the face of it...” Content validity: reflects the dimension(s) implied by the concept Criterion validity: two types Concurrent validity: correlation of one measure with another Predictive validity: predict accurately Construct validity: distinguishes participants who differ on the construct

13-8© 2007 Pearson Education Canada Validity in Experimental Design Internal validity: the extent to which you can demonstrate that the treatment produces changes in dependent variable External validity: the extent to which one can extrapolate from study to the general population In qualitative research, “credibility” is the issue Degree to which the description “rings true” to the subjects of the study, to other readers, or to other researchers

13-9© 2007 Pearson Education Canada 2. Reliability (in Quantitative Methods) A measure should provide similar results when repeated – should be “reliable” measure of the variable Can assess the internal reliability of items used to construct an index (an index combines several items into a single score) Split-half method: randomly split the items in two, construct index, check to see if results correlate highly Internal consistency: statistical procedure done in SPSS (described later in chapter)

13-10© 2007 Pearson Education Canada Measurement Error Researchers assume that the object being measured has two or more values (i.e., is not a constant) and that it has a “true value” True value: the underlying exact quantity of a variable at any given time Researchers also assume that measurement errors will always occur because instruments are imperfect Measurement error is any deviation from “true value”

13-11© 2007 Pearson Education Canada Measurement Error (Cont’d) MEASURE = True Value ± (SE ± RE) SE: Systematic error is non-random error that systematically over- or under-estimates a value (hence, distorts results) E.g., in coding, if researcher assigns the lowest value when respondents does not answer non-response coded as lack of support for x RE: Random error is random fluctuations around the true value Will not distort results

13-12© 2007 Pearson Education Canada Tips for Reducing Random and Systematic Error 1. Take the average of several measures 2. Use several different indicators 3. Use random sampling procedures 4. Use sensitive measures 5. Avoid confusion in wording questions or instructions 6. Error-check data carefully 7. Reduce subject and experimenter expectations

13-13© 2007 Pearson Education Canada Levels of Measurement Introduced in Chapter 8; this chapter stresses the importance of level of measurement for measuring concepts Level of measurement constrains type of statistical procedures one can use Three levels of measurement 1. Nominal 2. Ordinal 3. Ratio

13-14© 2007 Pearson Education Canada Levels of Measurement (cont’d) Nominal: Involves no underlying continuum; assignment of numeric values arbitrary Examples: religious affiliation, gender, etc. Ordinal: Implies an underlying continuum; values are ordered but intervals are not equal Examples: community size, Likert items, etc. Ratio: Involves an underlying continuum; numeric values assigned reflect equal intervals; zero point aligned with true zero Examples: weight, age in years, % minority, indexes

13-15© 2007 Pearson Education Canada The Effects of Reduced Levels of Measurement Best to achieve most precise, and highest, level of measurements possible When lower levels are used, the results under- estimate the relative importance of a variable The greater the reduction in measurement precision, the greater the drop in correlations between variables Precisely measured variables will appear to be more important than poorly measured ones

13-16© 2007 Pearson Education Canada Indexes, Scales, and Special Measurement Procedures While used interchangeably, an index refers to the combination of two or more indicators; a scale refers to a more complex combination of indicators where the pattern of responses is taken into account Indexes are routinely constructed to reflect complex variables Socioeconomic status, job satisfaction, group dynamics, social attitudes toward an issue Produce more valid and reliable measures than single-item measures

13-17© 2007 Pearson Education Canada Item Analysis Items in an index should discriminate well Example of test item development Test graded, students divided into upper and lower quartile Examine performance on each question Select those questions that discriminate best See Table 13.1 (next slide)

13-18© 2007 Pearson Education Canada Discrimination of Items TABLE 13.1 DISCRIMINATION ABILITY OF 100 ITEMS: PERCENTAGE CORRECT FOR EACH ITEM, BY QUARTILE PERCENT CORRECT EACH ITEM QUESTION #BOTTOM 25%TOP 25% ………

13-19© 2007 Pearson Education Canada Selecting Index Items 1. Review conceptual definition Note if the concept has different dimension 2. Develop measures for each dimension Developed items for each dimension of the concept 3. Pre-test index Complete the index yourself, then pre-test it with target-group members 4. Pilot test index Use SPSS to assess internal consistency

13-20© 2007 Pearson Education Canada The Rationale for Using Several Items in an Index Illustration: goal – to measure people’s attitudes toward abortion Would it be better to have one question or several questions in our measure? Answer: use several items Why? Attitude toward abortion would be complex; a valid measure should reflect complexity (e.g., their view may differ if mother’s life is threatened, or was result of rape)

13-21© 2007 Pearson Education Canada Rationale (cont’d) Single item questions (e.g. Are you in favour of abortion? yes/no) are more prone to measurement error (less reliable and valid) Such measures often lack precision; e.g., do not state conditions influencing attitudes Do not measure degree of support; thus, may not represent people’s opinion Have a limited range of values (limits type of statistical analysis)

13-22© 2007 Pearson Education Canada Likert-Based Indexes Idea of constructing indexes based on related questions introduced by Rensis Likert Original measure: asks respondent to note agreement with list of statements using a five- point scale: (1) strongly disagree, (2) disagree, (3) undecided or neutral, (4) agree, (5) strongly agree To improve reliability – increased number of response options from 5 to 9 Example shown on next slide

13-23© 2007 Pearson Education Canada Likert-Index Example: Job Satisfaction of Nurses In the following items, circle a number to indicate the extent to which you agree or disagree with each statement. 16. I enjoy working with the types of patients I am presently working with. Strongly Disagree Strongly Agree 29. I would be satisfied if my child followed the same type of career as I have. Strongly Disagree Strongly Agree 30. I would quit my present job if I won $1,000,000 in a lottery. Strongly Disagree Strongly Agree 31. This it the best job that I have had. Strongly Disagree Strongly Agree 32. I would like to continue the kind of work I am doing until I retire. Strongly Disagree Strongly Agree Source: Clare McCabe (1991). “Job Satisfaction: A Study of St. Martha Regional Nurses.” St Francis Xavier University, Sociology 300 Project. Cited with permission.

13-24© 2007 Pearson Education Canada Likert-Based Indexes (cont’d) Likert-based indexes are widely used Popularity due to a variety of factors: They are easy to construct There are well-developed techniques for assessing the validity of potential items They are relatively easy for respondents to complete improves response rate on surveys One can assess the reliability of the measure

13-25© 2007 Pearson Education Canada Tips: Constructing Likert-Based Index 1. Avoid the word “and” in one statement E.g., I get along with my mother and father Remove the “and”; create two statements 2. Place “Strongly Agree” on right hand side, with 9 indicating strong agreement Varying which side has “Strongly Agree” causes confusion To avoid response set, word some statements positively, and others negatively

13-26© 2007 Pearson Education Canada Tips (cont’d) 3. Avoid confusing negative statements E.g., I don’t think the university administration is doing a bad job 4. Vary strength of wording to produce variation in response 5. Provide a brief explanation of how respondents are to indicate their answers E.g., “In the following section, please circle a number to indicate the extent to which you agree or disagree with each statement.”

13-27© 2007 Pearson Education Canada Evaluation of Likert-Based Indexes We assume that the summation score of a set of Likert-type responses reflects the true underlying value of the variable Assess this by examining the correlation among the items

13-28© 2007 Pearson Education Canada Using the Internal Consistency Approach to Selecting Index Items Internal consistency (or homogeneity) refers to the ability of the items in an instrument to measure the same variable The greater the intercorrelation among the items, the greater the internal consistency Most commonly used method for evaluating internal consistency is Cronbach’s alpha Easy to calculate with computer software (Reliability procedure in SPSS)

13-29© 2007 Pearson Education Canada Internal Consistency (cont’d) Cronbach’s alpha value ranges from 0 to 1 1 = perfect consistency (items measure same variable) 0 = no internal consistency (items do not measure same variable) Want an inter-item correlation of above 30 Value of alpha influenced by number of items Alpha of.70 is reasonable if there are 5 items in the scale, but not if there are 14 items

13-30© 2007 Pearson Education Canada Semantic Differential Procedures In this measure, a series of adjectives indicating two extremes are placed at the margins of the page. Respondent is asked to indicate where on the continuum he or she would place the group, individual, or object being evaluated Originally developed to measure subjective feelings toward objects or persons E.g., how respondents view out-groups

13-31© 2007 Pearson Education Canada Semantic Differential (cont’d) 62. Circle a number to indicate where you think you fit on a continuum between the two opposites. 621Shy Outgoing 622Passive Dominant 623Cautious Daring 624Bookworm Social Butterfly 625Quiet Loud 626Serious Humorous 627Conformist Leader 628Cooperative Stubborn Source: Winston Jackson ( ). Research Methods: Rules for Survey Design and Analysis. Scarborough: Prentice-Hall Canada Inc., p. 99.

13-32© 2007 Pearson Education Canada Magnitude Estimation Procedures Respondents use numbers or line lengths to compare the magnitude of a series of stimuli to some fixed standard Useful when comparative judgments are required (See Box 13.4 and 13.5 in text) E.g., comparing liking of teachers; seriousness of crimes; liking of one community compared to another one, etc. Yields ratio level measures Researcher must be present to explain instructions