| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CNG2201 | Statistics | Fall | 2 | 0 | 2 | 4 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assoc. Prof. BERNA GÜLOĞLU |
| Course Objectives: | The aim of the study is to help students to comprehend data collection and data analysis, to be able to make statistical inferences and predictions, to interpret statistical results accurately, to utilize basic level SPSS, to recognize basic level correlational and comparative statistical techniques. |
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The students who have succeeded in this course; The students who have succeeded in this course; At the the end of the course students are expected of 1. organizing data matrices on Excell and SPSS worksheets, 2. computing and interpreting central tendency measures, 3. computing measures of dispersion, 4. formulating correlational hypotheses between variables at various levels of measurement, 5. identifying appropriate techniques to test the correlational hypothesis, 6. discriminating the appropriate method to test the comparatiive hypotheses. |
| Descriptive statistics * Measures of central tendency * Measures of dispersion Inferential statistics * Correlational methods * Comparative methods |
| Week | Subject | Related Preparation | |
| 1) | Variables and data * Continuous variables (Ratio, interval) * Categorical variables (Ordinal, Nominal) | To manufacture artificial data matrices (N X K) * Scores * Categories | |
| 2) | Descriptive statistics * Measures of central tendency * Measures of dispersion | Manufacturing large amount of data for a variety of variables at diffent levels. Computational exercisizes with Excel and SPSS * Mean, median, mode * Standard deviation, variance, maximum, minimum, range Interpretation and evaluation of findings and proposals for applications... | |
| 3) | Application of descriptive statistics: Conversion of raw scores into standard scores and assignment of grades to total scores | Obtain artificial raw scores for a group of students on different dimensions. Obtain T-scores Assign marks (A, B, ... F) | |
| 4) | Correlational hypotheses | Crosstabulation of correlational methods for two variables at different levels (Continuous, Ordinal, Polytomous, dchotomous). | |
| 5) | Pearson and Spearman Correlation coefficients | Create data for five different continuous variable measured on a large sample of subjects. Demonstrate the computational procedure on Excel by applying the Perason formula. Transport the data to SPSS and compute Pearson and Spearman coefficients. Compare and contrast the findings. | |
| 6) | Concept of significance | Obtain a binomial distribution for a T/F test of 10 items. Discuss the meaning of getting the scores all by chance. Emphasize the probability of getting 10 points by chance. Discuss the probability of being wrong by assigning "Pass" grade for different amounts of scores. Demonstrate the same procedure for K=100 multiple choice items with a=5 alternatives. Obtain the chance average and chance standard deviation. Emphasize how the probability of chance decreases as the scores increase. Point out that the significance increases as the probability of chance success decreases. | |
| 7) | Dependency between discrete (dichotomous and polytomous) variables. | Simulate data for two dichotomous and two polytomous variables. Obtain crostabulation between all possible paired combinations of variables. Compute Phi coefficient for 2 X 2 tables, Compute Chisquare for 2 X k tables. Revise and manipulate the data in such a way to display perfect dependency and perfect independency. | |
| 8) | Correlations between continuoaus and disrete variables | Generate data for a sample of N>100 subjects. Match a dichotomous an a polytomous data to teh subjects. Compute point-biserial correlation between the continuous and the dichotomous data, Compute Eta correlation between the continuous and the polytomous data, | |
| 9) | Analysis of test response data | Simulate a multiple choice (or Likert) response data matrix on Excel. Obtain item scores and scores of subjects. Compute split halves, KR-20 and 21, and Cronbach Alpha correlations. Compute item-remaider correlations. | |
| 10) | Inferential comparative hypotheses | Simulate data for a pretest, posttest and retention test data for an eperimental and control group research design. Match also a dichotomous data set and a polytomous data set to the subjects. * Compare the means of experimental and control group scores on pretest, posttest, and retention tests separately usin t-test for independent samples. * Compare the means repeated measures (pretest, posttest and retention test) scores of experimental and control groups by using t-test for dependent samples. | |
| 11) | One way ANOVA for independent samples One way ANOVA for repeated (dependent) measures | Prepare a data matrix for a continuous variable measured on a sample of subjects Match a polytomous variable to the continuous variable. Compare the means of the sub-samples by using one way ANOVA for independent samples. Discriminate the homogeneous sb-samples by using appropriate posthoc methods. … Prepare a new data matrix for a continues variable which repeatedly measured more than 2 times. Compare the means of different repeated trials by using one way ANOVA for dependent samples. Identify the homogeneous repetitions by using the appropriate techniques. | |
| 12) | Two way ANOVA without any repeated measures | Prepare a data matrix which comprises one continuous variable and two different discrete (two or more categorical) variables measured on a sample N200. Test the significance of difference between the means of sub-groups emerging from the crosstabulation of factors (categorical variables) by using the two way ANOVA without any repeted measures on any factor. Determine the homogeneous subgroups by an appropriate posthoc method. | |
| 13) | Two way ANOVA repeted measures on one factor only | Prepare a data matrix which displays a continuous variable measured at least twice on a large group of subjects (N>200). Also match another discrete variable which categorizes the subjects in to two or more sub-groups. Formulate hypotheses relevant to the means continuous variable across sub-groups (e.g. pre-post X experimental control). Apply two way ANOVA for repeated measures on one factor only. | |
| 14) | Two way ANOVA repeated measures on both factors. | There are times that some observations are to be compared with respect to two different time axis. For instance the body temperatures of the patients might be measured. 5uccessive days and thre times a day. Prepare a a data matrix for a continuous variable which is measured three days a week and twice a day. Emphasize that the scores are to be written next to each other for each subject. The means of the continuous variable between the sub-categories within the crosstabulation of time factors acan be compared by using two way ANOVA for repeated measures on both factors. Plot the values of the means on the graph to illıustrate the effects of intercativity of time factors. | |
| Course Notes: | 1. Cohen J., R. ve Swerdlik E., M. (2002). Psychological testing and assesment (5th. Ed.). New York: McGraw-Hill Book Co |
| References: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 10 |
| Laboratory | % 0 | |
| Application | 10 | % 15 |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | 10 | % 15 |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | % 0 | |
| Preliminary Jury | % 0 | |
| Final | 1 | % 60 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 2 | 28 |
| Laboratory | 0 | 0 | 0 |
| Application | 10 | 2 | 20 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework Assignments | 10 | 2 | 20 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Paper Submission | 0 | 0 | 0 |
| Jury | 0 | 0 | 0 |
| Final | 1 | 20 | 20 |
| Total Workload | 88 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |