PSYCHOLOGICAL TESTINGNORMS M A Part I Semester I

PSYCHOLOGICAL TESTINGNORMS M. A. Part I Semester I By Balaji Niwlikar https: //www. careershodh. com/

Basic Statistical Concepts: � ü Measures of Central tendencies: Mean ü ü Median ü ü ü Middle value Mode ü � Average Most repeated score Measures of Variability: Standard deviation Quartile deviation Range Z Scores https: //www. careershodh. com/

Norms � � Standard/Average performance. Methodology – to understand psy tests and proper interpretation of scores. Norms, Reliability , Validity, Item Analysis and Test Design. Raw Scores - 35 in English. 20 in math ? ? � Expressed in different units –kg, hour , no. of correct/incorrect responses, no. of trails, � So we cant just directly compare it. � It can only interpreted in clearly defined and uniform frame of references. https: //www. careershodh. com/

Norms Definition: “Norms may be defined as the average performance on a particular test made by a standardization sample. ” Standardization sample-true representation population, cross cultural, � Empirically established. � To discover where S/he falls in that situation - we convert Raw scores into Derived Score� � � 1. 2. Difficulty level – all score low 1, 5, 10 /1000. we can compare D scores 2 objectives /purpose/ goals of Derived Score. To indicate the individual’s relative standing in the normative sample and thus permit an evaluation of her/his performance in reference to other persons. To provide comparable measures that permits a direct comparison of the individuals performance on different tests. https: //www. careershodh. com/

Norms � 1. Types if evaluation Formative /concurrent Evaluation � � 2. Summative Evaluation � � 3. To find out which area is strong / weak Useful in Training program, language , math Diagnostic Evaluation � 4. To evaluate learning Not standard and Informal way At end of program Follow up Evaluation � � In Corporate areas Attitude changing Program https: //www. careershodh. com/

Norms � 1. Steps in developing Norms: Defining the target population � 2. Normative group -based on intention of test. Selecting the sample from the target population True representative sample. � Cross sectional � Large sample � Random sampling � 3. Standardizing the conditions � Test administration must be standard, valid https: //www. careershodh. com/

Types of Norms: Derived Score� Expressed in two major ways i. e types 1 st Developmental Level Attended or 2 nd relative position within a specific group � https: //www. careershodh. com/

Types of Norms: Norms Developmental Norms Within Group Norms Percentile Age Grade Standard Scores Sten Ordinal scale https: //www. careershodh. com/ Stenine Deviation IQ

Developmental Norms � � � � A way to attach meaning to scores. To indicate how far an individual has progressed the normal developmental path. Ex – children smile after certain age. Binet proposed early developmental age norms and gave concept of Mental Age. Binet –Simon Items passed by the majority of 8 years old in standardised sample were grouped together and placed in the 8 year level. = Mental Age of 8. 8 year old Sheldon Cupper scored well on intelligence test of 80 year old –means he has Mental Age of 80 year old https: //www. careershodh. com/

Developmental Norms 1. ü ü Age equivalent norms: Criteria – Ave. performance of standard sample at certain age level. Most suitable for trait or ability which increases systematically. e. g. Height, weight, Cog. Abilities, intelligence etc. Limitations – It is not fully standard and uniform unit for measurement for over all period. 2. Some of the traits can not be explained by age norms though they are related to age. Ex- maze learning will not develop after adolescent, IQ will not increased after 16 but vocabulary can. 1. https: //www. careershodh. com/

Developmental Norms 2. Ø Ø Ø Ø Ø Grade Equivalent norms Like age equivalent norms – criterion –Grade/Standard In field of educations. Achievement test & educational test. Ex -4 th grade performance in math , language skill. The average no. of problems solved correctly on a math test by the 4 th Grader in a standardization sample is 23, them raw score of 23 corresponds to grade equivalent of 4 It can be expressed in decimal (4. 5); If we considered months. Limitations – Same students in different subjects not comparables (math with social sciences). Not suitable for higher grades level ( 1 subject for 2 years) Not suitable for subjects which occurs rapidly growth in lower grades ; will be same in higher grade https: //www. careershodh. com/

Developmental Norms 3. Ordinal scales – � Not like statistics (providing rank order to individual without knowledge about amount of differences between them ) � Designed to identify the stage reached by the child in the development of specific behavior functions � Originated from research of child psychology � Based on Model of Guttman Scale or simplex(1944)- successful performance at one level implies success at all lower level. � Success in functions of locomotion , concept formation, etc. � Gesell Developmental Schedule (1947)–child has attained a certain level in –motor, adaptive , language , & personal-social. � Development theory of Piaget – schema , object permanence https: //www. careershodh. com/

Within-Group Norms � � Almost all psychological test provides it. Used most near comparable standard group ex - same chronological age /same school grade. Within group scores / norms have uniform and clearly defined quantitative meaning. Used in most of statistical analysis. https: //www. careershodh. com/

four levels or scales of measurement. (noir) � Nominal scales – involve classification or categorization based on one or more distinguishing characteristics, � Ex - “men, ” “ 1, ” “B, ” or “women, ” “ 2, ” or “A. ” � � Ordinal scales� � � permit classification and rank ordering on some characteristic Ex- merit list of SP College. Interval scales contain equal intervals between numbers. But like ordinal scales, interval scales contain no absolute zero point � Ex -IQs of 80 and 100 � � � Ratio Scales In addition to all the properties of nominal, ordinal, and interval measurement, � It scale has a true zero point. � https: //www. careershodh. com/

Within-Group Norms 1. ü ü ü ü ü Percentiles: Most common and popular Percentile -% of persons (standard sample) fall below a given point. Percentile and Percentile Rank are two different concepts. ex – if the 30% of the person obtain fewer than 18 problems correct on math then raw score of 18 corresponds to 30 th percentile (P 30) i. e. percentile rank is 30 and percentile score is 18 Lower the percentile the poorer the persons standing. PR 50 –median. PR 25 and PR 75 are called 1 st n 3 rd quartile points. . Different from percentage ( %) – raw score where percentile is derived score. PR 0 & PR 100? a raw score lower/more than obtained in in the standard sample. https: //www. careershodh. com/

Percentiles: ü ü ü ü Simple to understand. Familiar to population. simple for computation. Percentiles are placed on an Ordinal Scale means it regarded as rank in group of 100 Limitation – inequality of unites The distance between the extreme PRs is larger than the PRs in the middle of the NDC. Percentiles can be converted into large number of other norms. https: //www. careershodh. com/

Percentile https: //www. careershodh. com/

Within-Group Norms: � ü ü � � ü ü Standard scores : Increasing trend. most satisfactory derived score. a SS is a raw score that has been converted from one scale to another scale, where the latter scale has some arbitrarily set Mean and SD. Raw scores may be converted to standard scores to easily interpret. With a standard score, the position of a test taker’s performance relative to other test takers is readily apparent. SS can obtained by Linearly & Non linearly transformed Ex -z scores, T scores, stanines, and some other standard scores. https: //www. careershodh. com/

Standard scores : ü Linearly transformed score – They retain exact numerical relation of the original raw score. Standard score duplicate all the properties of raw score thus all results are distortion less. Units of the scale are equal so that they convey the same meaning throughout the whole range of the scale. They removes the problem of inequality. � Simply known A. ü ü ü z scores https: //www. careershodh. com/

Linearly transformed standard score Standard score/ z scores – It express the persons distance from the mean in the terms of SD of the distribution. zero plus or minus one scale. This is so because it has a mean set at 0 and a SD set at 1. z=(X-M)/SD Limitation of Linearly transformed standard score If one distribution is skewed and other is normally distributed then two standard scores cant be compared Lay people may uncomfortable with z-scores. 1. don't like negative numbers � uncomfortable with a z-score of 0 being average. � Ex- Swapnil got z-score of 0. � https: //www. careershodh. com/

Non Linearly transformed score A. Non Linearly transformed score – � when the data under consideration are not normally distributed yet need compare with normal distributions. � Here , the resulting SS does not necessarily have a direct numerical relationship to the original, raw score. � Examples 1. mental age, 2. percentile score, 3. Normalized standard score https: //www. careershodh. com/

Non Linearly transformed score � Normalized standard score SS which are expressed in the terms of normal distribution � Maeshall & Hales (1972) ‘’Normalized standard score � which have been adjusted to produce a normal frequency distribution and convert to a standard � � � base with pre assign Mean & SD’’. NSS can expressed in same form of linearly transformed SS i. e. with Mean= 0 and SD =1. Examples T scores , stanines, sten , C scores , Deviation IQ https: //www. careershodh. com/

Normalized standard score 1. � � T Scores called a fifty plus or minus ten scale; i. e, a scale with a mean set at 50 and a standard deviation set at 10. Devised by W. A. Mc. Call (1922, 1939) and named a T score in honor of his professor E. L. Thorndike, This system is composed of a scale that ranges from 5 SD below the mean to 5 SD above the mean. T score = 50+/-10 z https: //www. careershodh. com/

Normalized standard score 3. Stanines Stenines= 5+1. 96 Zn § Standard nine. Distribute entire scores into 9 units It has mean @ 5 and SD at 1. 96 If researcher knows PR scores corresponding Stanines value can be calculated. Stanines= 5+1. 96 Zn § Zn –Normalized z scores (we already calculated PR. ) +v –reasonably easy to understand. § Useful to counselor, educational psychologist , selection & § § § recruitment process. ad https: //www. careershodh. com/

stanine https: //www. careershodh. com/

Normalized standard score � ü ü ü Sten scores: They are also called as Standard Ten. After proposing 16 PF Raymond Cattle proposed the concept of Sten scores. It distributes entire score range into 10 units. It has mean of 5. 5 and SD of 2. If researcher knows PR scores corresponding Stanines value can be calculated. Sten scores= 5. 5+2 Zn https: //www. careershodh. com/

Sten Scores https: //www. careershodh. com/

Normalized standard score 5. � � � C Scores G. P. Guilford 11 standard units Ranger from 0 to 10 https: //www. careershodh. com/

Normalized standard score � � � Deviation IQ IQ Not comparable for different age group IQ of 115 @ Age 10 and IQ of 125 @Age of 12 Deviation IQ is a Normalized standard score has M=100 & SD 16 for Stanford Binet Scale Deviation IQ is a Normalized standard score has M=100 & SD 15 for Wheschlers intelligence test. https: //www. careershodh. com/

Relativity of Norms The theory talks about how norms are interchangeable. � It refers to the concept that if researcher knows one type of norm he can predict about the other. e. g. If researcher knows about percentile score of a subject that score can be converted into a Stanine or Sten score. � But in the case of linear norms relativity experiences limitations. � For conversion of the score one should know the shape of the distribution too. � https: //www. careershodh. com/

Relativity of Norms � 1. 2. 3. Three principle reasons of test score variation – Content –verbal/ numerical /spatial Scale unit-different SDs-16/15 Standardization of samples- slow/Ave/better will matter � Normative Sample- large, representative, selective factors, defined population � National Anchor Norms –solution for the lack of comparability – equipercentile method –scores are considered equivalent when they have equal percentiles for different test. � Specific Norms –standardize test on more narrowly defined population( ex 1 st FYBA students ) – local norms � Fixed reference group- college board SAT -1 st � Item Response Theory- used for difficulty. To established uniform ‘sample free’ scale of measurement ie applicable to person/group https: //www. careershodh. com/

Computers & Interpretation of Test Scores � � � Computers play an important role in generating data analysis. It helps in conduction of experiments. It influences the process of test construction. Calculation of item total correlation, item analysis is possible with the help of computers. It is useful in the method of factor analysis too. Following calculations became popular as well as possible due to computers. https: //www. careershodh. com/

Computers & Interpretation of Test Scores Exploratory factor analysis Confirmatory factory analysis Online test conduction Computers helps in Cross cultural studies New methods of reliability Easier validity computation https: //www. careershodh. com/

Computers & Interpretation of Test Scores � � Computer scoring Interactive computer system � System � 1. 2. for Interactive Guideline Information (SIGI) Major concern To score comparability Narration interpretation scoring https: //www. careershodh. com/

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