Content analysis, studies, correlations.

• Content analysis - analysis of the content of something, indirect form of observation, observing through artefacts, from tv programmes, books, songs, paintings, can be qualitative or quantitative data, opportunity sampling used in systematic manner/behavioural categories.
• Content analysis- create an aim, view content and decide behavioral categories, analyze content, describe examples, draw conclusions from findings.
• Content analysis - strengths - high ecological validity based on real life situations and the findings can be generalized beyond the setting. Easy to access reliability of findings and conclusions, researchers can access same materials and use a coding system to find consistency or not.
• Content analysis - weaknesses - potential for researcher bias. judgement about applying content of artefact to a category is subjective, the way the researcher interprets content to confirm hypothesis, lowers internal validity. Hard to establish cause and effect as only describes data not conducted under controlled experimental conditions, reducing internal validity.
• Case Studies - involves detailed in-depth study of one individual/institution/event, collects information from a variety of sources, often longitudinal, collects both qualitative and quantitative data, uses different methods to gather information, interviews, questionnaires, tests, observations.
• Case Studies - strengths - high ecological validity, no artificial manipulating behavior, case studies analyze natural behavior in real life. Used when it's unpractical/unethical to use other methods to study unique/rare behavior such as amnesia/brain damage which would be unethical to generate experimentally- rich in-depth data.
• Case studies - weaknesses - ethical issues of anonymity/confidentiality due to studying one group or individual. Reliability, difficult to replicate as they are based on naturally occurring one-off behaviors/events, therefore cannot be checked for consistency of findings so low external reliability. Researcher bias - lack objectivity, low population validity.
• Cross sectional and longitudinal - in developmental psychology.
• Cross sectional - investigates different sections in society such as age, gender, known as a snapshot study (people/behaviour at one time) different groups - teachers, doctors - does not involve manipulation of variables.
• Cross sectional - strengths - relatively quick and cheap, tested once for comparisons so no follow up study is necessary, ppts are easier to obtain less pressure to take part, larger sample size increasing population validity.
• Cross sectional - weaknesses - difficult to determine differences between 2 cohorts, ppts cannot always be asked about differences, data is collected from a snapshot in time so its harder to identify/analyse developmental trends in the studies.
• Longitudinal - does not involve manipulation of variables, a method where research carried out over a long period of time involves identifying one group of ppts and study them at various periods over several years, observe long term effects on people, involves using a range of methodologies, to collect data uses observations, interviews etc.
• Longitudinal strengths - ppts variables controlled - same person tested numerous times, increased internal validity, developmental trends spotted easily, tests repeated at regular intervals over many years, reduces recall bias and increases internal validity.
• Longitudinal weaknesses - high attrition (drop out rate) takes a long time, leaves a biased sample, ppt more likely aware of the aims of the study so show demand characteristics.
• Correlations - a non-experimental method that measures strength and direction of relationship positive or negative. Involves measuring two+ covariables not involve an IV/DV, can be quickly used to analyse relationships in large amounts of data, direction of relationship positive/negative/non, identifies weak, moderate and strong correlations, correlation coefficient calculated to identify strength and direction of correlation. (statistical test)
• Correlation coefficient - measuring how strongly two variables are correlated, from +1 to -1 indicates strength (number) and direction (+ or -) +1 = perfect positive correlation, -1 = perfect negative correlation, 0 = no correlation.