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Describe Qualitative data and Quantitative data.

Qualitative data is data in the form of words, which is rich and detailed, often produced from case studies, and unstructured interviews and observations. Quantitative data is data in the form of numbers, often produced from lab experiments or closed questions.

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"Describe Qualitative data and Quantitative data."	"Qualitative data is data in the form of words, which is rich and detailed, often produced from case studies, and unstructured interviews and observations. Quantitative data is data in the form of numbers, often produced from lab experiments or closed questions."
"Define Primary data and Secondary data."	"Primary data is data that has been collected by the researcher for the purposes of the study. Secondary data is data collected by someone other than the researcher, for example, census information. The researcher makes use of this as part of their study, but the information was not collected for the purpose of that study."
"How does the Mean and Median measures of central tendency differ?"	"Mean is calculated by adding up all of the scores, then dividing by the number of scores there are. It takes into account all of the data, but is affected by extreme values. Median is the middle score when the scores are put in numerical order. It is much less affected by extreme scores than the mean, and is easy to calculate, but is less sensitive as not all scores are taken into account."
"Do Qualitative data and Quantitative data overlap? Give an example."	"Yes, the two types of data can overlap. For example, interviewing participants who have taken part in a lab study, or converting the responses to open questions into some form of qualitative data."
"What are the advantages and disadvantages of using Primary data?"	"Primary data perfectly fits the study, as it has been designed for this specific purpose, and the researcher has control over it. However, this requires more time and effort for the researcher."
"What are the advantages and disadvantages of using Secondary data?"	"Secondary data is potentially less time-consuming and expensive, but the quality of it cannot be controlled by the researcher and it may not perfectly match the needs/aims of the study."
"Define the term 'Mode' in the context of data analysis."	"The mode is the most commonly occurring score in a data set. If there are two modes, the data set is bi-modal. If all the scores are different then there is no mode."
"Describe the concept of 'Range' in data dispersion."	"The range is the difference between the lowest and highest score in a data set. Usually, 1 is added to the difference, to allow for the fact that scores are often rounded up or down in research."
"How is 'Standard deviation' used in data analysis?"	"Standard deviation measures the spread of scores around the mean, in other words the average distance of each of the scores from the mean. The higher the standard deviation, the more spread out the scores are, suggesting a large variation in the results."
"Explain how to calculate 'Percentages' in data analysis."	"Percentages are calculated by dividing a score or number by the total, then multiplying by 100."
"How are 'Fractions' represented in data analysis?"	"If there is one decimal place in the number, it is divided by 10. If there are two, then it is divided by 100. If there are three, it is divided by 1000."
"Define the term 'Probability' in the context of psychological research."	"The accepted level of probability in psychological research is 5%, often represented as p= 0.05, meaning there is a 5% possibility that the results of an experiment were caused by chance factors, rather than the IV."
"What is the significance of 'Significant figures/decimal places' in data analysis?"	"An appropriate amount of decimal places to use in data analysis is usually 2-3."
"Define the concept of rounding to significant figures."	"Rounding to significant figures is a method of approximating a numerical value to a certain number of significant digits. If the next number is 5 or higher, the previous number is rounded up. If lower than 5, it is rounded down."
"Describe the difference between positive, negative and zero correlations."	"A positive correlation occurs when both variables increase in number. Negative correlation occurs when one variable increases and the other decreases. No correlation is when neither variable increases or decreases with the other."
"How are bar charts and histograms used in the presentation of data?"	"Bar charts are used to visually represent data such as the mean scores of two conditions in discrete categories. Histograms, like bar charts, display continuous data, so the bars are touching."
"Explain the use of scattergrams in data representation."	"Scattergrams are used to represent correlational data, showing the relationship between two variables. The two co-variables can appear on either the x or y-axis."
"Define normal distributions and skewed distributions."	"Normal distributions form a bell-shaped curve, with most people located in the middle of the curve, and the mean, median and mode are all the same. Skewed distributions, where the majority of results appear on one side of the graph, can be positive (most scores on the left, long tail on the right) or negative (most scores on the right, long tail on the left)."
"What is the correlation coefficient?"	"The correlation coefficient is a measure used to determine the strength of a correlation between two co-variables."
"Define the concept of correlation in statistical analysis."	"Correlation is a numerical value between -1 and +1 that indicates the strength and direction of the relationship between two variables. A positive correlation means as one variable increases, so does the other, while a negative correlation means as one variable increases, the other decreases. A value of 0 means there is no correlation."
"What does a correlation value close to 1 or -1 signify?"	"A correlation value close to 1 or -1 signifies a strong correlation. The closer the number is to 1 or -1, the stronger the correlation between the two variables."
"Describe the three levels of measurement in quantitative data."	"The three levels of measurement are Nominal, Ordinal, and Interval. Nominal is when data is categorized, such as the number of males and females in a class. Ordinal is when data is ranked, like the order of finishers in a race. Interval data provides the rank order of scores and the precise intervals between scores, such as the finishing times of runners in a race."
"What is the purpose of coding in content analysis?"	"Coding in content analysis is used to generate quantitative data. Data is categorized into units for the purposes of analysis, such as studying the portrayal of gender in TV adverts."
"How does thematic analysis differ from coding in content analysis?"	"Thematic analysis generates qualitative data. It identifies recurring themes using coding, then describes these themes in greater detail. These themes may then be tested by conducting further analyses to ensure they represent the content of the data."

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[{"id":1591872,"front":"Describe Qualitative data and Quantitative data.","back":"Qualitative data is data in the form of words, which is rich and detailed, often produced from case studies, and unstructured interviews and observations. Quantitative data is data in the form of numbers, often produced from lab experiments or closed questions.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Qualitative data provides in-depth insights through words, often from open-ended interviews and observations. It's rich in detail and context. Quantitative data, on the other hand, involves numerical information, typically from controlled experiments or structured surveys, allowing for statistical analysis.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591873,"front":"Define Primary data and Secondary data.","back":"Primary data is data that has been collected by the researcher for the purposes of the study. Secondary data is data collected by someone other than the researcher, for example, census information. The researcher makes use of this as part of their study, but the information was not collected for the purpose of that study.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Primary data is firsthand information collected by the researcher for a specific study, ensuring its relevance and accuracy. Secondary data, on the other hand, is pre-existing data gathered by sources other than the researcher, such as government agencies or research institutions.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591874,"front":"How does the Mean and Median measures of central tendency differ?","back":"Mean is calculated by adding up all of the scores, then dividing by the number of scores there are. It takes into account all of the data, but is affected by extreme values. Median is the middle score when the scores are put in numerical order. It is much less affected by extreme scores than the mean, and is easy to calculate, but is less sensitive as not all scores are taken into account.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"The mean is influenced by extreme values, as it considers all data points. In contrast, the median is less affected by extreme values and provides a better representation of the central tendency when the data set contains outliers. However, the median does not consider all data points, unlike the mean.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591875,"front":"Do Qualitative data and Quantitative data overlap? Give an example.","back":"Yes, the two types of data can overlap. For example, interviewing participants who have taken part in a lab study, or converting the responses to open questions into some form of qualitative data.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Qualitative and quantitative data can overlap when using mixed methods. For instance, conducting interviews post a quantitative study adds qualitative insights. Converting survey responses into qualitative themes also demonstrates this overlap, enriching data analysis.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591876,"front":"What are the advantages and disadvantages of using Primary data?","back":"Primary data perfectly fits the study, as it has been designed for this specific purpose, and the researcher has control over it. However, this requires more time and effort for the researcher.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Primary data is firsthand and tailored to the research needs, ensuring relevance and accuracy. Researchers have control over its collection and quality. Yet, it demands more time, resources, and effort compared to secondary data due to the need for data collection from scratch.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591879,"front":"What are the advantages and disadvantages of using Secondary data?","back":"Secondary data is potentially less time-consuming and expensive, but the quality of it cannot be controlled by the researcher and it may not perfectly match the needs/aims of the study.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Secondary data can save resources by avoiding data collection, but its reliability and relevance may vary. Researchers must critically evaluate its suitability for their study to ensure it aligns with their specific objectives. Quality control and alignment are crucial considerations when utilizing secondary data.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591890,"front":"Define the term 'Mode' in the context of data analysis.","back":"The mode is the most commonly occurring score in a data set. If there are two modes, the data set is bi-modal. If all the scores are different then there is no mode.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"The mode in data analysis represents the most frequently occurring value in a dataset. It is useful for identifying the central tendency of categorical data. In cases where there are multiple modes, the data is described as bi-modal. If all values are unique, the dataset has no mode.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591906,"front":"Describe the concept of 'Range' in data dispersion.","back":"The range is the difference between the lowest and highest score in a data set. Usually, 1 is added to the difference, to allow for the fact that scores are often rounded up or down in research.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Range in data dispersion provides a simple measure of the spread of values in a dataset. It is calculated by finding the difference between the highest and lowest values. Adding 1 accounts for potential rounding discrepancies in the data, ensuring a more accurate representation of the spread.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591909,"front":"How is 'Standard deviation' used in data analysis?","back":"Standard deviation measures the spread of scores around the mean, in other words the average distance of each of the scores from the mean. The higher the standard deviation, the more spread out the scores are, suggesting a large variation in the results.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Standard deviation is a crucial measure in data analysis as it indicates the variability or dispersion of a dataset. It helps in understanding the distribution of data points and identifying outliers. A higher standard deviation signifies greater diversity within the dataset, influencing decision-making in various fields such as finance, science, and quality control.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591910,"front":"Explain how to calculate 'Percentages' in data analysis.","back":"Percentages are calculated by dividing a score or number by the total, then multiplying by 100.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Calculating percentages is essential in data analysis as it allows for the comparison of different data points relative to a whole. This method helps in understanding proportions, trends, and making informed decisions based on the significance of the data in context. It simplifies complex data into easily interpretable figures.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591911,"front":"How are 'Fractions' represented in data analysis?","back":"If there is one decimal place in the number, it is divided by 10. If there are two, then it is divided by 100. If there are three, it is divided by 1000.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"In data analysis, fractions are often represented as decimal values to facilitate calculations and comparisons. This method allows for easier interpretation of data, especially when dealing with percentages or ratios, as it standardizes values for analysis and visualization.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591912,"front":"Define the term 'Probability' in the context of psychological research.","back":"The accepted level of probability in psychological research is 5%, often represented as p= 0.05, meaning there is a 5% possibility that the results of an experiment were caused by chance factors, rather than the IV.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"In psychological research, the significance level of 0.05 is commonly used to determine the likelihood that results are due to chance. This threshold helps researchers assess the validity of their findings and draw meaningful conclusions based on statistical analysis.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591914,"front":"What is the significance of 'Significant figures/decimal places' in data analysis?","back":"An appropriate amount of decimal places to use in data analysis is usually 2-3.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Significant figures or decimal places are important in data analysis because they indicate the precision and accuracy of the measurements or calculations. Using an appropriate number of decimal places helps to avoid misleading or false conclusions. It ensures that the results are reliable and consistent. Additionally, it allows for proper comparison and interpretation of data across different measurements or experiments.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591920,"front":"Define the concept of rounding to significant figures.","back":"Rounding to significant figures is a method of approximating a numerical value to a certain number of significant digits. If the next number is 5 or higher, the previous number is rounded up. If lower than 5, it is rounded down.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":null,"deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591921,"front":"Describe the difference between positive, negative and zero correlations.","back":"A positive correlation occurs when both variables increase in number. Negative correlation occurs when one variable increases and the other decreases. No correlation is when neither variable increases or decreases with the other.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Positive correlation means both variables increase, negative correlation means one increases while the other decreases, and zero correlation means no consistent relationship between the variables. This understanding is fundamental in various fields such as statistics, economics, and psychology.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591922,"front":"How are bar charts and histograms used in the presentation of data?","back":"Bar charts are used to visually represent data such as the mean scores of two conditions in discrete categories. Histograms, like bar charts, display continuous data, so the bars are touching.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Bar charts are effective for comparing discrete categories, such as mean scores of different conditions. Histograms are suitable for displaying the distribution of continuous data, with bars representing ranges of values. Both aid in visualizing and understanding data patterns.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591927,"front":"Explain the use of scattergrams in data representation.","back":"Scattergrams are used to represent correlational data, showing the relationship between two variables. The two co-variables can appear on either the x or y-axis.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Scattergrams visually display the relationship between two variables, making it easy to identify patterns or correlations. They are particularly useful in identifying trends, clusters, or outliers in the data, aiding in the analysis and interpretation of relationships between the variables.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591936,"front":"Define normal distributions and skewed distributions.","back":"Normal distributions form a bell-shaped curve, with most people located in the middle of the curve, and the mean, median and mode are all the same. Skewed distributions, where the majority of results appear on one side of the graph, can be positive (most scores on the left, long tail on the right) or negative (most scores on the right, long tail on the left).","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Normal distributions are characterized by a symmetrical bell-shaped curve, with the mean, median, and mode being equal. Skewed distributions, on the other hand, show an asymmetrical pattern, with most values clustering on one side and a tail stretching towards the other. Skewness can be positive or negative.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591941,"front":"What is the correlation coefficient?","back":"The correlation coefficient is a measure used to determine the strength of a correlation between two co-variables.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"The correlation coefficient quantifies the relationship between two variables, indicating the strength and direction of their association. It's a valuable tool in statistics for understanding the degree to which changes in one variable correspond to changes in another.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591953,"front":"Define the concept of correlation in statistical analysis.","back":"Correlation is a numerical value between -1 and +1 that indicates the strength and direction of the relationship between two variables. A positive correlation means as one variable increases, so does the other, while a negative correlation means as one variable increases, the other decreases. A value of 0 means there is no correlation.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Correlation measures the degree and direction of the relationship between two variables. It helps in understanding how changes in one variable relate to changes in another. A positive correlation indicates that both variables move in the same direction, while a negative correlation means they move in opposite directions. A correlation of 0 suggests no linear relationship between the variables.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591956,"front":"What does a correlation value close to 1 or -1 signify?","back":"A correlation value close to 1 or -1 signifies a strong correlation. The closer the number is to 1 or -1, the stronger the correlation between the two variables.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"A correlation value close to 1 or -1 indicates a strong linear relationship between the variables. This means that as one variable changes, the other variable tends to change in a predictable way. The closer the value is to 1 or -1, the stronger the relationship.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591967,"front":"Describe the three levels of measurement in quantitative data.","back":"The three levels of measurement are Nominal, Ordinal, and Interval. Nominal is when data is categorized, such as the number of males and females in a class. Ordinal is when data is ranked, like the order of finishers in a race. Interval data provides the rank order of scores and the precise intervals between scores, such as the finishing times of runners in a race.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"The three levels of measurement in quantitative data are Nominal, Ordinal, and Interval. Nominal data involves categorization, like gender. Ordinal data involves ranking, such as race finishers. Interval data includes precise intervals between scores, like race times.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591974,"front":"What is the purpose of coding in content analysis?","back":"Coding in content analysis is used to generate quantitative data. Data is categorized into units for the purposes of analysis, such as studying the portrayal of gender in TV adverts.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Coding in content analysis serves to systematically categorize and organize data, enabling researchers to identify patterns and trends within the information. This process allows for the generation of quantitative data, facilitating in-depth analysis and interpretation of the content being studied. Through coding, researchers can effectively examine and compare specific elements within the data, such as themes or representations, to gain valuable insights and draw meaningful conclusions.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}},{"id":1591983,"front":"How does thematic analysis differ from coding in content analysis?","back":"Thematic analysis generates qualitative data. It identifies recurring themes using coding, then describes these themes in greater detail. These themes may then be tested by conducting further analyses to ensure they represent the content of the data.","front_language":"English","back_language":"English","front_image":"","back_image":"","order":0,"explanation":"Thematic analysis differs from coding in content analysis as it focuses on generating qualitative data by identifying recurring themes through coding. It then delves deeper into describing these themes and may conduct further analyses to validate their representation of the data.","deleted_at":null,"flashcard_deck_id":"uCAHW","original_id":null,"examMode":{"text":"","correct":"","incorrect":"","info":""}}]

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