Download to read offline
STATISTICAL TECHNIQUES USING SPSS SOFTWARE
- 1. STATISTICS USING SPSS PRESENTED BY Dr.A. SABERUNNISA ASSISTANT PROFESSOR DEPARTMENT OF STATISTICS THE MADURA COLLEGE MADURAI
- 2. TOPICS COVERED • Introductionand What is SPSS? • What is it used for? • How to Open SPSS? • Basic Structure of SPSS • How to Input Data Manually • Descriptive Statistics: Mean, Standard Deviation, Frequency • How to perform t-test?
- 3. INTRODUCTION • SPSS isa Windows based program that can be used to perform data entry and analysis and to create tables and graphs. • SPSS is capable of handling large amounts of data and can perform all of the analyses covered in the text and much more. • SPSS is commonly used in the Social Sciences and in the business world, so familiarity with this program should serve you well in the future.
- 4. INTRODUCTION • First versionof SPSS was released in 1968, after being developed by Norman H. Nie, Dale H. Bent and Hadlai Hull.. • It was acquired by IBM on July 28, 2009 . • IBM SPSS Statistics 21.0- Released on August 2012 Latest version.
- 5. WHAT IS ITUSED FOR With SPSS we can analyze data in three basic ways: • Describe data using descriptive statistics • Examine relationship between variables • Compare groups to determine if there are significant difference between groups
- 6. Basic Structure ofSPSS There are two different windows in SPSS • Data Editor Window: Here we can create variables, enter data and carry out statistical functions. • Output Viewer Window: Here the results are produced by analyzing the functions.
- 7.
- 8. DATA EDITOR CONSISTSTWO TABS DATA VIEW VARIABLE VIEW
- 9. DATA VIEW Date viewis used to enter data and view data In data view: • Row represent individual cases. • Columns represent particular variable in your data file.
- 11. VARIABLE VIEW Variable viewis used to create and define various variables: In Variable View: • Row represent individual variable or define the variable • Column represent the specific characteristic of variable like Name, Type, Width, Decimals, Label, Missing, Align, Measure.
- 13. VARIABLES A concept whichcan take on different quantitative values is called a variable. What are variables you would consider in buying a second hand bike? Brand Type Age Condition(Excellent, good, poor) Price
- 14. • Dichotomous variables( having two values only) Yes or No Male or Female • Continuous Variables may take on any value within a given range, or in some cases, an infinite set. Income age a test score
- 15. Measurement Scales The processof assigning numbers to objects in such a way that specific properties of the objects are faithfully represented . Types of Scales: Nominal Ordinal Scale Interval Ratio
- 16. Types of Scales NominalScale: is defined as a scale that labels variables into distinct classifications and doesn’t involve a quantitative value or order. This scale is the simplest of the four variable measurement scales. Examples • Gender • Political preferences • Place of residence
- 17. Ordinal Scale is definedas a variable measurement scale used to simply depict the order of variables. Examples • Satisfaction • happiness • a degree of pain
- 18. Interval Scale is definedas a numerical scale where the variables’ order is known and the difference between these variables. Variables that have familiar, constant, and computable differences are classified using the Interval scale. Examples • What is your family income? • What is the temperature in your city?
- 19. Ratio Scale is definedas a variable measurement scale that not only produces the order of variables but also makes the difference between variables known, along with information on the value of true zero. Examples • weight and height • market share • annual sales • the price of an upcoming product
- 22. DESCRIPTIVE STATISTICS Describing thestatistical data in numerical measures is called descriptive statistics. They are classified in two types: • Measure of central tendency(or) location- it describes the central theme of data and summarizes the characteristics of data. (i.e.)., mean, median, mode. • Measure of dispersion- it describes the extent of scatter of the values. (i.e.)., Standard deviation, range, quartile devation and mean deviation.
- 23. Example: Weight of babies(kg)below 6 months taken from a hospital record is given below. Calculate mean, median, mode, standard deviation.
- 27.
- 29.
- 31. CHARTS IN SPSS Astatistical graph or chart is defined as the pictorial representation of statistical data in graphical form. The statistical graphs are used to represent a set of data to make it easier to understand and interpret statistical information. Types of Graphs in Statistics • Bar charts- is for discrete variables, both qualitative and quantitative. It compares the mean of group observations or simple frequency of qualitative variable. • Clustered bar charts- it shows two or more categories of variables in the same graph. • Pie charts-It is a circular diagram in which the frequency of different classes is equal to the angle of different sectors of a circle. It is used to display the relative frequencies of the same set of data. • Scatter plot - The relationship between two quantitative variables can be represented in the form of scatter plot. • Line Charts - Line graphs are drawn for two variables or more than two variables. Line graphs can be drawn with just one line or more than one line in the graph.
- 32. Formulate a frequencytable and draw a bar diagram for the following data on the blood group of 45 students in a class. AB B O A O O A O B AB B A B A B O B AB A O O A O AB O O A A B A A AB O A A O A O A A O A O O B
- 36. CLUSTERED BAR CHART •Draw a clustered and stacked bar diagram for the following data on the blood group of 90 students in a class. O B AB B A B A O B AB B A B A A O AB O O A A B A A AB O A A O A O A A O A O O B O O B A B AB A O O O B A B A A A A A AB AB B O B AB O B O B A A A AB O B A A A O O B AB B A A AB O A B A MALE FEMALE
- 40.
- 43. SCATTER PLOT • Drawa scatter plot for the following data on age (years) versus systolic blood pressure (mm Hg. AGE 56 42 6 0 50 5 4 49 3 9 62 65 70 40 53 35 38 BP 16 0 130 1 2 5 13 5 1 4 5 115 1 4 0 120 140 160 126 145 118 120
- 47.
- 51. PARAMETRIC TESTS ANDNON-PARAMETRIC TESTS • Parametric tests are statistical tests that make assumptions about the distribution of a population from which a sample is drawn. • Non-parametric tests are distribution-free tests that do not require a distribution to meet certain assumptions
- 52. STUDENTS t-TEST • Thet-test enables us to test the significance of difference between two sample means or significance of a single mean. These procedures are called two-sample test and one sample test. • Independent sample t-test : compares means for two groups of cases. Eg., A sample of 100 individuals is drawn from the population and randomly divided into two groups and one group is subjected to some experimental conditions and the rest to control conditions.
- 53.
- 60.
- 61. INTERPRETATION • Output 1:gives the mean haemoglobin level in the individuals fed with Diet A is 10.82 with a standard deviation of 1.136 (gm %) and for Diet B, the mean level is 11.357 with a standard deviation of 1.15 (gm %). • Output 2: gives the t-value, degrees of freedom, significance level and 95% confidence interval for the mean. The t-value of –1.239 for 26 (14+14 – 2 as each group has 14 values) degrees of freedom (df) is not significant as significance value (for two-tailed test) is 0.226 which is >0.05. Therefore, we accept the null hypothesis, i.e., diet B is not superior to diet A in increasing the hemoglobin level.
- 62. PAIRED SAMPLE t-TEST Aninvestigator wants to evaluate the effect of a particular supplementary diet in increasing the level of haemoglobin in man. He selected a group of 15 individuals, the level of haemoglobin in these persons were estimated and then these individuals were fed on the supplementary diet. After feeding for a sufficient period of time, the level of haemoglobin in these persons were estimated. The data obtained in this study is given in the form of a table. Evaluate the efficiency of the supplementary diet in increasing haemoglobin (gm %) level. Null hypothesis The supplementary diet is not effective in increasing haemoglobin level in human.
- 66. INTERPRETATION The paired-sample t-testshowed a significant improvement in hemoglobin levels after the intervention (t(14) = -5.815, p < 0.001). Mean hemoglobin increased from 10.65 g/dL before to 11.23 g/dL after the intervention. This indicates that the intervention had a statistically significant positive effect on hemoglobin levels.