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CHAPTER 1 Introduction to statistics

CHAPTER 1 Introduction to statistics. What is Statistics?. •Statistics is the term for a collection of mathematical methods of organizing, summarizing , analyzing, and interpreting information gathered in a study .

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CHAPTER 1 Introduction to statistics

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  1. CHAPTER 1 Introduction to statistics

  2. What is Statistics? •Statistics is the term for a collection of mathematical methods of organizing, summarizing, analyzing, and interpreting information gathered in a study

  3. Data and Data Analysis We have two types of research study •In quantitative research, data are usually quantitative (numbers) and subjected to statistical analysis. Mainly the data is collected by close ended questions •Qualitative research, data are usually narrative and collected by open ended questions

  4. Example of close ended question (Likert scale) to measure attitude toward mental illness SA= Strongly agree A = Agree D = Disagree SD = Strongly disagree ?? = Uncertain Dr. Yousef Aljeesh

  5. Dr. Yousef Aljeesh

  6. Example of open ended question • What is the perception of you organization towered female holding high managerial positions? ………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

  7. Where Do Data Come From? •Example 1: Interviews/questionnaires –Question: On a scale from 0 to 10, please rate your level of fatigue –Answer (Data): Person 1: 7 Person 2: 3 Person 3: 10 Etc.

  8. Variables A variable is something that takes on different values Example of variables –Height, sex, weight, age, level of education, marital status, respiratory rate, heart rate and etc…

  9. Types of Variables –Independent variable: The hypothesized cause of, or influence on an outcome –Dependent variable: The outcome of interest, hypothesized to depend on, or be caused by the independent variable

  10. Research Questions •Research questions communicate the research variables and the population(the entire group of interest) –Example: In hospitalized children (population) does music (IV) reduce stress (DV)?

  11. Types of Sampling 1. probability Sampling 2. Non- probability Sampling. Dr. Yousef Aljeesh

  12. Probability sample The probability sample means, the probability of each subject to be included in the study. There are four types of probability sample Dr. Yousef Aljeesh

  13. Four basic kinds of probability samples. a. Simple random sample. The simple random sample is the simplest probability sample, so that every element in the population has an equal probability of being included. Note All types of random samples tend to be representative. Dr. Yousef Aljeesh

  14. b. Stratified random samples In a stratified random sample, the population is first divided into two or more homogenous strata (age, gender, occupation, level of education, income and so forth) from which random samples are then drawn. This stratification results in greater representativeness. Dr. Yousef Aljeesh

  15. C. Cluster samples For many populations, it is simply impossible to obtain a listing of all the elements, so the most common procedure for a large surveys is cluster sampling. Dr. Yousef Aljeesh

  16. D. Systematic samples Systematic sampling involves the selection of every (kth) element from some list or group, such as every 10th subject on a patient list. If the researcher has a list, or sampling frame, the following procedure can be adopted. The desired sample size is started at some number (n). The size of the population must be known or estimated (N). By dividing (N) by (n), the sampling interval is the standard distance between the elements chosen for the sample. Dr. Yousef Aljeesh

  17. Example if we were seeking a sample of 200 from a population of 40,000, then our sampling interval would be as follows: K= 40,000 = 200 200 In other words, every 200 the element on the list would be sampled. The first element should be selected randomly, using a table of random numbers, let us say that we randomly selected number 73 from a table. The people corresponding to numbers 73, 273, 473, 673, and so forth would be included in the sample. Dr. Yousef Aljeesh

  18. 2. Non-probability Sample Non-probability sample is less likely than probability sampling to produce a representative samples. Despite this fact, most research samples in most disciplines including nursing are non-probability samples. Dr. Yousef Aljeesh

  19. a. convenience sampling (Accidental, volunteer) The use of the most conveniently available people or subjects in a study. For example, stopping people at a street corner to conduct an interview is sampling by convenience. Sometimes a researcher seeking individuals with certain characteristics will stand in the clinic, hospital or community center to select his convenience sample. Sometimes a researcher seeking individuals with certain characteristics will place an advertisement in a newspaper, so the people or subjects are volunteer to take apart of the study. Dr. Yousef Aljeesh

  20. b. Snowball or network sampling Early sample members are asked to identify and refer other people who meet the eligibility criteria. or it begins with a few eligible subjects and then continues on the basic of subjects referral until the desired sample size has been obtained. This method of sampling is most likely to be used when the researcher population consists of people with specific traits who might otherwise be difficult to identify. Dr. Yousef Aljeesh

  21. C. Quota Sampling Quota sampling is another form of non-probability sampling. The quota sample is one in which the researcher identifies strata of the population and determines the proportions of element needed from the various segments of the population, but without using a random selection of subjects. Dr. Yousef Aljeesh

  22. Note: Although there are no simple formulas that indicate how large sample is needed in a given study, we can offer a simple piece of advice: you generally should use the largest sample possible. The larger the sample the more representative of the population it is likely to be. Dr. Yousef Aljeesh

  23. Variable and constant Variable: is something that varies or takes in different values (weight, sex, blood pressure, and heart rate) are all examples of characteristics that vary from one person to the next. If they did not vary, they would be constants

  24. Discrete Versus Continuous Variables •Variables have different qualities with regard to measurement potential –Discrete variables –Continuous variables

  25. Note: • We use non-parametric tests in case of Nominal and Ordinal measurement (Example: Chi-Square test) - Both depend on percentages because Mean does not make sense

  26. Note In interval scale, there is no real or rational zero point

  27. Another Example Weight (Zero weight is actual possibility) It is acceptable to say that some one who weights 100 kg is twice as heavy as some one who weights 50 kg.

  28. Note Interval and Ratio measurements are continuous variables and parametric tests should be used in this situation. Also Mean is applicable

  29. Types of Statistical Analysis • Calculation –Manual versus computerized • Purpose –Descriptive versus inferential • Complexity –Univariate, bivariate, multivariate

  30. Descriptive Statistics •Researchers collect their data from a sample of study participants—a subset of the population of interest •Descriptive statistics describe and summarize data about the sample –Examples: Percent female in the sample, level of education, Income, residency and ect

  31. Example 1 of Descriptive statisticsDistribution of study population according to place of work

  32. Calculation of Response Rate Response Rate (RR) = Respondents (R) 100 Target Population (TP) RR= 51 100 = 91.07 56

  33. Example 2 of Descriptive statistics Distribution of Study Population According to Height, Weight and BMI (N= 143)

  34. Age distribution Example 3 of Descriptive statistics

  35. Example 4 of Descriptive statistics Gender distribution

  36. Example 5 of Descriptive statistics Distribution of subjects by governorates

  37. Inferential Statistics • Researchers obtain data from a sample but often want to draw conclusions about a population Inferential statistics are often used to test hypotheses(predictions) about relationships between variables Example:- Positive, negative, directional hypothesis and etc.

  38. Example of inferential statistics Association between socio-demographic factors and diarrhea among children aged less than 5 years (N=140)

  39. Hypotheses Definition of hypothesis : It is a statement of predicted relationship between two or more than two variables. Dr. Yousef Aljeesh

  40. Types of Hypotheses 1. Simple Hypothesis :A hypothesis that predicts the relationship between one dependent variable (DV) and one independent variable (IDV). It is easy to test and analyze it. Example There is a relationship between smoking and development of stroke among hypertensive patients in Gaza strip. Dr. Yousef Aljeesh

  41. 2. Complex hypothesis:(Multivariate hypothesis) : A hypothesis that predicts the relationship between two or more dependent variables and two or more independent variables. Example: There is a relationship between high fat diet and smoking and development of atherosclerosis and stroke among hypertensive patients in Gaza strip. Dr. Yousef Aljeesh

  42. 3. Directional hypothesis: is one that specifies the expected direction of the relationship between variables. The researcher predicts not only the existence of a relationship but also the nature of the relationship. Dr. Yousef Aljeesh

  43. Example 1. There is a positive relationship between Smoking and lung cancer Dr. Yousef Aljeesh

  44. 4. Statistical hypothesis (Null hypothesis): is one that stated there is no relationship between variables. Example 1. There is no relationship between Smoking and lung cancer 2. There is no relationship between obesity and Breast cancer. Dr. Yousef Aljeesh

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