1. Chapter 7 Measuring of data Reliability of measuring instruments The reliability* of instrument is the consistency with which it measures the target attribute. Example If a scale weighed a person at 80 kg one minute and 85 kg the next, we would consider it unreliable. The less variation an instrument produces in repeated measurements, the higher its reliability. Thus, reliability can be equated with a measure’s stability, consistency, or dependability. Reliability also concerns a measure Dr. Areefa

2. VALIDITY The second important criterion for evaluating a quantitative instrument is its validity. Validity is the degree to which an instrument measures what it is supposed to measure. When researchers develop an instrument to measure hopelessness, how can they be sure that resulting scores validly reflect this construct and not something else, like depression? Reliability and validity are not independent qualities of an instrument. A measuring device that is unreliable cannot possibly be valid.

3. Types Face validity refers to the degree to which an assessment or test subjectively appears to measure the variable or construct that it is supposed to measure. In other words, face validity is when an assessment or test appears to do what it claims to do example Lila is a researcher who has just developed a new assessment that is meant to measure mathematical ability in college students. She selects a sample of 300 college students from three local universities and has them take the test. After the students complete the test, Lila asks all 300 participants to complete a follow-up questionnaire. In the questionnaire, Lila asks the participants what they think the purpose of the test is, what construct they believe is being measured, and whether or not they feel the assessment was an adequate measure of their mathematical ability. After analyzing the follow-up results, Lila finds that most of the participants agree that Lila's assessment accurately measures their mathematical ability. Lila's has just demonstrated that her assessment has face validity.

4. Content Validity Content validity concerns the degree to which an instrument has an appropriate sample of items for the construct being measured. Content validity is relevant for both affective measures. content validity (also known as logical validity). For example, a depression scale may lack content validity if it only assesses the affective dimension of depression but fails to take into account the behavioral dimension. An element of subjectivity exists in relation to determining content validity, which requires a degree of agreement about what a particular personality trait such as extraversion represents. A disagreement about a personality trait will prevent the gain of a high content validitydepression affectivebehavioralpersonality trait extraversion

5. Construct Validity Validating an instrument in terms of construct Validity is a challenging task. The key construct validity questions are: What is this instrument really measuring? Does it adequately measure the abstract concept of interest? involves logical analysis and tests predicted by theoretical considerations.

6. Analyzing Quantitative Data: Descriptive Statistics Statistical analysis helps researchers make sense of quantitative information. Without statistics, quantitative data would be useless mass of numbers. Statistical procedures enable researchers to summarize, organize, evaluate, interpret, and communicate numeric information.

7. Statistics are either descriptive or inferential. Descriptive statistics are used to describe and synthesize data. Averages and percentages are examples of descriptive statistics. Actually, when such indexes are calculated on data from a population, they are called parameters.

8. A descriptive index from a sample is called a statistic. Research questions are about parameters, but researchers calculate sample statistics to estimate them, using inferential statistics to make inferences about the population.

9. LEVELS OF MEASUREMENT Scientists have developed a system for categorizing measures. This system is important because the analyses that can be performed on data depend on their measurement level. The four major classes, or levels, of measurement are nominal, ordinal, interval, and ratio.

10. Nominal Measurement The lowest level of measurement is nominal measurement, which involves assigning numbers to classify characteristics into categories. Examples of variables amenable to nominal measurement include gender, blood type, and marital status.

11. The numeric codes assigned in nominal measurement do not convey quantitative information. If we classify males as 1 and females as 2, the numbers have no inherent meaning. The number 2 clearly does not mean “more than” 1. It would be perfectly acceptable to reverse the code and use 1 for females and 2 for males. The numbers are merely symbols رمز that represent two different values of the gender attribute. Indeed, instead of numeric codes, we could have used alphabetical symbols, such as M and F.

12. Interval measurement occurs when researchers can specify the rank- ordering of objects on an attribute and can assume equivalent distance between them. Most psychological and educational tests are based on interval scales.

14. Ratio Measurement they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied. At the risk of repeating myself, everything above about interval data applies to ratio scales + ratio scales have a clear definition of zero. Good examples of ratio variables include height and weight.descriptive and inferential statistics

17. What type of measure? Test your self

18. What type of measure? Wt. ht. ratio Temperature, date interval

19. Constructing Frequency Distributions Frequency distributions are a method of organizing numeric data. A frequency distribution is a systematic arrangement of values from lowest to highest, together with a count of the number of times each value was obtained.

23. The Mode The mode is the most frequently occurring score value in a distribution. The mode is simple to determine; it is not computed but rather is established by inspecting a frequency distribution. In the following distribution of numbers, we can readily see that the mode is 53: 50 51 51 52 53 53 53 53 54 55 56

24. The Median The median is the point in a distribution above which and below which 50% of cases fall. As an example, consider the following set of values: 2 2 3 3 4 5 6 7 8 9 The value that divides the cases exactly in half is 4.5, which is the median for this set of numbers. The point that has 50% of the cases above and below it is halfway between 4 and 5. An important characteristic of the median is that it does not take into account the quantitative values of scores. The median is an index of average position in a distribution. It is insensitive to extreme values.

25. The Mean The mean is equal to the sum of all scores divided by the total number of scores. The mean is the index usually referred to as an average. The computational