# Statistics & Research Methods-Measures of Variation (CBSE-NET (UGC) Psychology (Paper-II & Paper-III)): Questions 7 - 11 of 14

Get 1 year subscription: Access detailed explanations (illustrated with images and videos) to **4421** questions. Access all new questions we will add tracking exam-pattern and syllabus changes. View Sample Explanation or View Features.

Rs. 700.00 or

## Question number: 7

» Statistics & Research Methods » Measures of Variation

Appeared in Year: 2013

### Question

While evaluating the personnel selection programme, job performance was found to have a multiple correlation of 0.6 with the four predictors in the selection test battery. This means that the four predictors explain (December)

### Choices

Choice (4) | Response | |
---|---|---|

a. | 36 % variance in job performance | |

b. | 40 % variance in job performance | |

c. | 64 % variance in job performance | |

d. | 60 % variance in job performance |

## Question number: 8

» Statistics & Research Methods » Measures of Variation

### Question

If one finds a positive correlation between degree of coffee drinking and the likelihood of heart attacks. One can conclude that:

### Choices

Choice (4) | Response | |
---|---|---|

a. | Coffee drinking causes heart attack | |

b. | Individuals prone to heart attacks are predisposed to drink a lot of coffee | |

c. | An active life style of certain people causes heart attack | |

d. | All of the above |

## Question number: 9

» Statistics & Research Methods » Measures of Variation

### Question

The most widely used measure of correlation is

### Choices

Choice (4) | Response | |
---|---|---|

a. | the Pearson r. | |

b. | Spearman’s rho. | |

c. | the rank-difference correlation coefficient. | |

d. | the Spearman-Brown prophecy formula. |

## Passage

A psychologist wanted to develop a numerical ability test for the student population. She wrote eighty multiple choice items, each item with five alternatives. The item analysis was carried out by finding discrimination index and item-remainder correlation for each item. Sixty items were retained in item analysis.

The new version, with finally retained sixty items was administered to a new sample (N = 400) twice with a time gap of eight weeks. The test-retest correlation was found to be 0.22, significant at. 01 level. The data obtained at the first administration of the sixty item version was also used for computing split-half reliability. The correlation between scores based on odd items and the scores based on even items was found to be 0.60. The split-half reliability coefficient was obtained after applying Spearman-Brown correction.

The scores obtained by the students were correlated with their marks in Mathematics in their annual examination which took place three months after the data collection. The product-moment correlation of 0.6 was obtained between test scores and mathematics. Norms were developed for the test. The normative sample yielded a mean of 40 and standard deviation of 8. The data were found to be normally distributed. As norms, percentile ranks were obtained.

(June 2014)

## Question number: 10 (1 of 1 Based on Passage) Show Passage

» Statistics & Research Methods » Measures of Variation

### Question

Which of the following correlation is suitable while computing item-remainder correlation in the above analysis?

### Choices

Choice (4) | Response | |
---|---|---|

a. | Spearman rank difference correlation | |

b. | Phi-coefficient | |

c. | Multiple correlation | |

d. | Point-biserial correlation |

## Question number: 11

» Statistics & Research Methods » Measures of Variation

Appeared in Year: 2013

### Assertion (Ꭺ)

Total variance of test score is the sum of true variance and error variance. (December)

### Reason (Ꭱ)

True score and error score are independent of each other.

### Choices

Choice (4) | Response | |
---|---|---|

a. | Both Ꭺ and Ꭱ are true and Ꭱ is the correct explanation of Ꭺ | |

b. | Both Ꭺ and Ꭱ are true but Ꭱ is NOT the correct explanation of Ꭺ | |

c. | Ꭺ is false but Ꭱ is true | |

d. | Ꭺ is true but Ꭱ is false |