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EXPLAIN SPSS-part1.pdf
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- 2. What is SPSS (StatisticalProduct and Service Solutions) + a software program used for statistical analysis. It's a tool that helps people understand and work with data. + import data from almost any type of file (like Excel or Google Sheets) and then perform statistical analysis on it. + analysis can range from simple descriptive statistics (like averages, medians, or modes) to more complex inferential statistics (like t-tests, chi- square tests, or regression analysis).
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- 6. Variable View questions like:"What's your age?", "Are you male or female?", "How much do you like ice cream on a scale of 1 to 10?“ to enter all these responses into SPSS to analyze them. Each question from your survey will be a "variable" in SPSS. ❑ Name: A short, no-space name for each question. Like "age", "gender", "ice cream". ❖ Uniqueness: Each variable in your dataset must have a unique name. You cannot have two variables with the exact same name. ❖ No Spaces Allowed: ❖ No Special Characters: like #, $, %, &, *, etc. ❖ Starting Character: Variable names must start with a letter or an underscore, not a number. ❖ Case Sensitivity: doesn't distinguish between uppercase and lowercase letters. So "TestScore", "TESTSCORE", and "testscore" would all be considered the same variable name. ❑ Type: What kind of answer are you expecting? Numbers? Words? ❖ Numeric: This type is used for variables that contain numbers. These numbers can be either integers (like 1, 2, 3) or decimals (like 1.23, 4.56). You would use this type for a variable like "Age" or "Number of Children". ❖ String: This type is for variables that contain text, or "strings" of characters. For example, a variable like "Name" or "City of Residence" would be a string. ❖ Date: The Date type is for variables that contain dates. SPSS has many different date formats you can choose from, so you can handle just about any kind of date data.
- 7. Variable View ❑ Width:How long can the answers be? + For a numeric variable, width is the total number of digits allowed, including any decimal points and negative signs. For example, with a width of 3, you can store numbers like -99, 100, or 12.3. + For a string (text) variable, width is the total number of characters allowed. For example, with a width of 5, you can store words like "hello" or "world". ❑ Decimals: If you're using numbers, how many decimal places can they have? • if you set "Decimals" to 2 for a variable, then the values of that variable will be shown with two digits after the decimal point, like 12.34. • This doesn't round or change your data, but it controls how the data is displayed. ❑ Label: A longer, more descriptive name for each question. Like "Age of the respondent", "Gender of the respondent", "Rating for ice cream“ • While the "Name" of your variable has to be unique, short, and cannot contain spaces, the Label can be longer, more descriptive, and can contain spaces. This allows you to provide additional context or clarity that the variable name might not be able to communicate. .
- 8. ❑ Missing: Howshould SPSS handle it if there's no answer for a question? 1.define how the program should handle missing data for each variable. 2.it's common to have missing data. This could happen for a variety of reasons. For example, a survey respondent might skip a question, or a particular measurement might not be taken. 1.If you've used a specific number or code to represent missing data (like "999" or "-1"), you can tell SPSS that this number means the data is missing, and it will ignore these when doing analyses. ❑ Columns: How wide should the column be when you're looking at the data? • about how the data is displayed when you're looking at it in SPSS's Data View. ❑ Align: Should the data be left-aligned, right-aligned, or centered in the column?
- 9. Measure: What kindof data is it? Categories (like male/female), order (like rankings), or scale (like age)? Nominal: This is used for variables that represent categories, no inherent order or ranking, example, "Gender" (male, female) or "Eye Color" (blue, green, brown) Ordinal: This is used for variables that represent categories with an inherent order. For example, "Education Level" (high school, bachelor's, master's, PhD) Scale: Also known as "interval/ratio", this is used for variables that have a consistent scale and meaningful zero point, Age" or "Height" would be scale variables, as they have a constant interval of measurement (one year, one inch, etc.) and a true zero point.
- 10. Scale + interval data: 1.Time of Day: When measuring time in hours throughout the day, we're dealing with interval data. The difference between 1 PM and 2 PM is the same as between 2 PM and 3 PM, which is an hour. However, there is no true zero point - 0 o'clock doesn't mean the absence of time, it's simply midnight. 2. Elevation Above Sea Level: If we consider places above sea level, the difference between an elevation of 100 meters and 200 meters is the same as between 500 meters and 600 meters. Both differences are 100 meters. But here, 0 doesn't mean the absence of elevation; instead, it represents sea level. 3. Temperature in Degrees Celsius or Fahrenheit: The difference between 15°C and 25°C is the same as the difference between 25°C and 35°C. Both are differences of 10°C. However, 0°C or 0°F does not mean the absence of temperature. 4. intervals between the values are equal, but there's no true zero point that indicates an absolute lack of the quality being measured.
- 11. Scale + Ratio Data •Money: If you have $10, $20, or $30, the differences between these amounts are all the same ($10). But if you have $0, that means you literally have no money. • Age: The difference between 10 years old and 20 years old is the same as between 30 years old and 40 years old (a difference of 10 years). A person who is 0 years old is literally not yet born or just born. + Imagine measuring the distance in kilometers. The difference between 10 km and 20 km is the same as between 30 km and 40 km. It's a 10 km difference in both cases. But in this scale, 0 km really means no distance at all - it's a true zero. + 0 really means the absence of the thing you're measuring.
- 12. ❑ Values: Ifan answer option corresponds to a number (like "1 for male, 2 for female"), you can specify that here + used to assign meaningful labels to the numerical codes of a categorical variable. This is particularly useful when your data contains coded responses. + EXAMPLE, Imagine you have conducted a survey and collected data about people's favorite fruit. You asked the question "What is your favorite fruit?" and gave them three options: 1.Apple 2.Banana 3.Orange + Instead of entering these fruit names into your SPSS dataset, you decide to code them numerically: + 1 = Apple 2 = Banana 3 = Orange
- 13. "Permissible Statistical Operationsby Level of Measurement" Level of Measurement Mode Median Mean Order (Greater/Less) Differences Ratios Nominal Yes No No No No No Ordinal Yes Yes No Yes No No Interval Yes Yes Yes Yes Yes No Ratio (Scale in SPSS) Yes Yes Yes Yes Yes Yes
- 14. Variable Property -Role Input:independent variable. This is the default assignment for variables in SPSS. This is a variable that you think might affect something. Target: The variable will be used as an outcome or dependent variable Both: The variable will be used as both (independent variable) and an outcome (dependent variable). None: The variable has no role assignment. This might be used for variables that are included in your dataset but not used in the analyses, Partition: The variable will partition the data into separate samples. This can be used in more advanced analyses where you want to run separate analyses for different groups in your data. Split: a more advanced version of SPSS for predictive modeling and data mining.
- 15. Variable Property -Role + magine you are conducting a study to understand the effect of exercise and diet on a person's weight. + You collect the following data for each participant: 1. ID number 2. Age 3. Gender 4. Hours spent exercising per week 5. Type of diet (Vegan, Vegetarian, Non-Vegetarian) 6. Weight + In this study, each variable might have the following roles: 1. ID number: This would be set to None because it's just used to identify participants, not for analysis. 2. Age: This could be an Input. You think age might have an effect on a person's weight. 3. Gender: This could also be an Input. Gender might influence weight. 4. Hours spent exercising per week: This is another Input. You suspect that more exercise could lead to lower weight. 5. Type of diet: This is also an Input. The type of diet might affect a person's weight. 6. Weight: This is your Target variable. You're trying to understand and predict weight based on the other variables (age, gender, hours of exercise, type of diet). 7. "Split" in the variable role is more typical when using SPSS Modeler
- 16. EXAMPLE + Input thefollowing data into SPSS
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- 19. Briefly explain theconcept of “Central Tendency”. + The concept of "Central Tendency" refers to the way of quantifying the "centre" or the "average" of a dataset. For our dataset, the central tendency could be applied to the 'Score', 'Age', 'Weight', and 'Height' variables because they are continuous. For the 'Gender' and 'Collection Place' variables, which are categorical (nominal), the mode is the most relevant measure of central tendency, The three main measures of central tendency are: + 1. Mean: This is the arithmetic average of a set of values. It's calculated by adding up all the values and then dividing by the number of values. + 2. Median: This is the middle value in a sorted list of values. If the number of values is odd, the median is the exact middle number. If the number of values is even, the median is the average of the two middle numbers. + 3. Mode: This is the most frequently occurring value in a set of data. For continuous data, the mode might not be as meaningful, but for categorical data like 'Gender' and 'Collection Place', the mode tells us the most common category.
- 20. Briefly explain theconcept of “Central Tendency”.
- 21. Statistics MUET S coreAge Weight Height N Valid 770 769 769 753 Missing 0 1 1 17 Mean 3.4270 30.69 61.29 162.04 Median 3.5000 25.00 60.00 161.00 Mode 3.67 22 60 160 Briefly explain the concept of “Central Tendency”. Statistics CollectionPlace Gender N Valid 770 770 Missing 0 0 Mode 3 2 Sum 1827 1232
- 22. Briefly explain theconcept of “Central Tendency”. + Age: The mean age is 30.69 years, indicating that if you averaged all the ages, you would get approximately 30.69 years. The median age is 25 years, indicating that the middle person's age, when all ages are arranged in order, is 25 years. The mode is 22 years, which means that the most common age among the people is 22 years. + Weight: The mean weight is 61.29 kg, meaning that on average, a person in this group weighs around 61.29 kg. The median weight is 60 kg, indicating that the middle person's weight, when all weights are arranged in order, is 60 kg. The mode of weight is 60 kg, meaning the most common weight among the people is 60 kg. + Height: The mean height is 162.04 cm, suggesting that the average height of individuals in the group is approximately 162.04 cm. The median height is 161 cm, which means the height that falls in the middle when all heights are arranged in order is 161 cm. The mode of the height is 160 cm, suggesting the most common height among the individuals is 160 cm. + MUET Score: The mean MUET Score is 3.4270, suggesting that when all MUET scores are averaged, the result is roughly 3.4270. The median MUET Score is 3.50, which means that when all MUET scores are arranged in ascending or descending order, the score in the middle is 3.50. The mode for MUET Score is 3.67, indicating that the most frequently occurring MUET score among the participants is 3.67. + Gender: the most common gender in this dataset is coded as '2'. 'Female' is the most common gender in this dataset. meaning that there are more females than males in this data set. + Collection Place: The 'Mode' is the most frequently occurring category, which in this case is 3, corresponding to "Sunway Pyramid". This indicates that Sunway Pyramid is the most common collection place in the data. + Note that in this case, we don't calculate mean and median for these categorical variables because they're nominal, meaning they represent categories that don't have an inherent order or ranking. For such variables, it wouldn't make sense to calculate an average or median.
- 23. 2-Identify the averageMUET score; height, weight and age Descriptive Statistics N Mean MUET S core 770 3.4270 Age 769 30.69 Weight 769 61.29 Height 753 162.04 Valid N (listwise) 752 "MUET Score": There are 770 valid responses, and the average score is 3.4270. "Age": There are 769 valid responses, and the average age is 30.69 years. "Weight": There are 769 valid responses, and the average weight is 61.29 kg. "Height": There are 753 valid responses, and the average height is 162.04 cm. The "Valid N (listwise)" refers to the number of cases (individuals, participants, etc.) that have valid, non-missing values for all of the variables listed. In this case, there are 752 cases with complete data for MUET Score, Age, Weight, and Height.
- 24. 3-Identify the mostfrequent MUET score, height, weight and age. Mode: This is the most frequently occurring value in a dataset. For the MUET Score, the mode is 3.67, meaning this score appears most often. For Age, the most frequently occurring age is 22 years. The most common weight is 60 kg, and the most common height is 160 cm. The "Missing" row indicates how many missing values are in each column. For instance, "Age" and "Weight" each have one missing value, while "Height" has 17 missing values